What Organizations Need Instead

This article was originally posted on jens.linden.de.

Organizations want to become data-inspired and typically begin by drawing up a roadmap. But treating an overwhelmingly complex endeavor as if it were merely complicated is a guaranteed path to failure.

I am Jens, a business-minded data expert with two decades of experience in implementing data and AI use cases. I help leaders across industries design strategies and foster cultures that unlock the full potential of data and algorithms.

tl;dr

• Organizations want to become data-inspired¹ and typically begin by drawing up a detailed roadmap.
• However, not every part of such an endeavor is plannable, and organizations must distinguish intelligently between planning and exploration⁵.
• Making this distinction protects investments in data and AI and significantly increases the odds of success.

1 The Understandable Desire for a Plan

In my consulting practice I frequently encounter requests to develop a roadmap that will make an organization “data-driven” or even data-inspired¹ [1]. Such projects typically carry “data strategy” in their title, the sponsors are often technical experts within the company — and sometimes a fixed-price offer is explicitly demanded: “What will it cost, what are the milestones, and when will it be done?”

That is entirely understandable. Procurement and controlling operate with budgets, comparability, and planability. Investments in technology and organizational change can be substantial — management needs certainty before committing, especially in times of tight budgets and economic uncertainty.

Yet this is precisely where a paradox lies: the greater the uncertainty an organization faces regarding its use of data and AI, the stronger the desire for a detailed plan — and the less useful that plan actually is as a tool. The certainty being sought is an illusion.

2 Planability as Illusion: A Missing Distinction

The crux of planning: it only works under certain conditions [2, 3, 4]. Those who ignore these conditions waste resources.

Planning anticipates decisions: what, when, how, and by whom something is to be done. The planner makes these decisions based on what is known at the time of planning. As long as the future is sufficiently predictable and implementation produces few surprises, this is a superior approach. If a company wants to migrate its BI landscape to a new data platform and knows exactly how to do it, the project can be planned with precision. Where knowledge of a solution exists — whether in-house or purchased as a consulting service — solid planning is the best foundation for project success.

Such problems are called complicated². They can be demanding and challenging, but the path to a solution is in principle known or determinable.

Not all problems are complicated, however. Many are complex³: the solution path is unknown because living systems — whether markets or one’s own organization — generate surprises that cannot be anticipated in advance. Here, pre-formulated plans quickly become obsolete. What is needed are ideas and experiments from skilled practitioners[5], and decisions must be made where new insights emerge, not at the drawing board.

For the journey towards a data-inspired¹ organization, this means companies must distinguish cleverly between complicated and complex problem components [2]. The complicated component — such as building IT infrastructure and tools for data management, and designing standardized processes for operating data and AI solutions — can be planned. The complex component — including making strategic decisions, innovating and prioritizing use cases, and changing the organization — requires an exploratory⁵ approach: the path must first be discovered and cannot be mapped out at the drawing board in advance.

One caveat applies to the distinction: even seemingly plannable endeavors will encounter surprising elements, and conversely, exploration also contains plannable phases. The question is not whether a problem is purely complicated or purely complex, but which character dominates.

Those who fail to make this distinction do not fail by accident — they fail by design: with complex problems, the plan becomes fiction, and the organization loses confidence in the entire endeavor because the approach simply does not match the problem.

3 Protecting Investments: Combining Planning and Exploration

How does an organization approach the possibilities of data and AI without falling into the planning trap — and the waste that comes with it?

The key lies in the right sequence. Before data platforms are designed, the workforce is rallied around AI, and new data-inspired¹ ways of working are demanded, strategic clarity is needed first. Dazzled by the hype, organizations often look to solutions before looking at the problems to be solved. Without a strategic frame of reference, there is no basis whatsoever for sound investment decisions. Questions like these must be answered first:

1. How do the new technological possibilities affect our differentiation in the market?
2. Where are data and AI changing the rules of the game in our industry?
3. Which specific problems of our customers can we solve better than before using data and AI?
4. What possibilities for new business strategy emerge from this?

The answers to such questions cannot be obtained by working through a methodology. Strategy design addresses complex³ problems and therefore requires an exploratory⁵ approach [5] that calls equally for creativity and practitioner expertise[5–1]. Often it is unclear which data & AI use cases with the potential to transform strategy and the business model even exist — if any do at all. This is where strategy design and the innovation of data and AI use cases converge. Both must go hand in hand and require collaboration between management and data and AI experts [6].

Only the strategic clarity makes it clear which functions⁶ and capabilities the organization requires. And only then does it make sense to differentiate: which build-up work is complicated² and therefore plannable? Which requires an exploratory approach? A platform migration with clear requirements and a defined scope can be planned well. Changing the way employees use data for decision-making, on the other hand, cannot be planned.

Those who follow this sequence — strategic clarity first, then differentiated implementation — avoid failure by design and thereby protect their investments in data and AI.

From strategic clarity the required functions emerge — typically including a data and AI function with its own functional strategy [6].

In practice, this means: do not plan and approve the entire endeavor a-priori, but invest incrementally. Each step — whether a strategy workshop or a pilot — is decided on the basis of the insights gained along the way. Furthermore, skilled practitioners are needed for exploration. Finding them and creating the conditions they require is one of the greatest challenges management faces on the journey to a data-inspired organization.

4 Conclusion and Outlook

The desire for a fully planned roadmap for becoming a data-inspired¹ organization is understandable — but dangerous. It mistakes an endeavor that is often predominantly complex³ for one that is complicated².

Investment protection for data and AI does not come from planning in as much detail as possible, but from the ability to distinguish complicated and complex components and to choose the right approach for each: planning for the known, exploration⁵ for the unknown. And above all: maintaining the right sequence. First develop strategic clarity, then derive the required functions⁶ and capabilities, and only then implement in a differentiated way.

This is less comfortable than a seductively detailed roadmap — but it is the difference between the illusion of control and genuine investment protection.

How strategic clarity for data and AI can be developed in practice is described in [6]. How organizations can design their data and AI functions in a dynamic-robust way is the subject of a further article.

References

[1] S. Wernicke, Data Inspired: Erfolgskonzepte für die datengetriebene Organisation. München: Verlag Franz Vahlen, 2024.

[2] G. Wohland and M. Wiemeyer, Denkwerkzeuge der Höchstleister: Warum Dynamikrobuste Unternehmen Marktdruck Erzeugen, 1st ed. UNIBUCH VERLAG, 2014.

[3] Harvard Business Review, A Plan Is Not a Strategy (June 29, 2022). Accessed: March 8, 2026. [Online Video].

[4] R. L. Martin, “Strategy vs. Planning: Complements not Substitutes”, Substack. February 15, 2021. Accessed: March 8, 2026. [Online].

[5] R. L. Martin, “The Evolution of the Strategic Choice Structuring Process”, Medium. June 23, 2025. Accessed: March 8, 2026. [Online].

[6] J. Linden, “How Most Organizations Get Data Strategy Wrong — and How to Fix It”, Towards Data Science. January 20, 2025. Accessed: March 8, 2026. [Online].

Glossary

1. Data-inspired organizations use data, analytics, and AI to continuously experiment and learn, to ask new business-relevant questions, and to realize competitive advantages by combining data, creativity, and intuition.
2. Exploration is the activity of discovering an unknown solution path through ideas and experiments. It is the counterpart to planning, which breaks a known solution path into steps in advance. Both are sides of the same distinction: approach — the way an organization works on problems. Which side is appropriate is determined by the character of the problem: planning for complicated components, exploration for complex ones.
3. Complicated problems are low in surprises and can therefore be mastered with existing knowledge.
4. Complex problems are high in surprises and cannot therefore be solved with available knowledge alone. Ideas are required to address them.
5. Skill is the person-bound and non-transferable capacity to generate problem-solving intuitions in concrete, often poorly understood situations. It is grounded in talent and deliberate practice and is the decisive resource for handling surprises — since by definition no knowledge yet exists for what is surprising.
6. A function is a bundle of specific services that solves a permanently recurring problem. Important: function here does not refer to the organizational unit that produces those services.

Data and AI Roadmaps: Failure by Design was originally published in Strategy, Organization & Culture for AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

What executives must understand to build data- and AI-driven organizations

This article was originally posted on CIO.com and co-authored by Leonie Petry.

Despite billions invested in data, analytics, and AI, most corporate initiatives still fail to deliver business impact. Executives often respond by focusing on establishing a stronger data culture, treating it as the missing ingredient to becoming data-driven. But this logic is flawed. Drawing on modern organizational theory, this article argues that culture resists direct control. It is the outcome — not the prerequisite — of successful value creation with data. Moreover, recipes for change initiatives that worked in one organization cannot simply be copied to another. Any attempt to engineer a desired data culture therefore has low success rates by design.

Instead, leaders should treat data culture as a sensor: it reveals hidden barriers that block value creation with data, analytics, and AI. Once identified, the task is not to fix people or their mindsets, but to change the context in which they operate. We propose a practical tool that helps leaders surface cultural barriers and design targeted interventions. An example from healthcare illustrates how such interventions shift daily routines and, as a side effect, create the very culture executives seek.

I am Jens, a business-minded data expert with two decades of experience in implementing data and AI use cases. I help leaders across industries design strategies and foster cultures that unlock the full potential of data and algorithms.

Table of Contents

· 1 Why Do So Many Data Initiatives Struggle?
· 2 Organizational Culture
· 3 Data Culture
· 4 Making Data Culture Practical: A Tool and an Example
· 5 Conclusion
· References

1 Why Do So Many Data Initiatives Struggle?

Organizations have been investing heavily in data, analytics, and AI for decades. The promise is clear and ambitious: better reporting, more automation, sharper decisions and new insights that promise breakthrough innovations. Since the arrival of generative AI, value generation with data has climbed even higher on the agendas of executives.

Yet despite billions poured into technology, talent and consulting, most data initiatives fall short of expectations.¹ Reports remain unused, dashboards collect dust, and AI pilots rarely scale. Why is this so?

The problem is not technology. Infrastructure and tools are readily available and concepts for data management, business intelligence, data science, and AI are mature. The real bottleneck lies elsewhere: how organizations work with data in everyday decisions. In addition to strategic clarity on how data creates competitive advantages,² executives increasingly cite data culture as the missing ingredient to becoming truly data-driven.¹ ³

But here lie some critical questions: What exactly is data culture and how does it relate to corporate culture in general? What role does it play in corporate transformations, when organizations aim to become data-driven? And how can executives avoid the often-quoted low success rates of changing organizational culture?⁴

To answer these questions, we first need to look at organizational culture before turning to data culture in particular.

2 Organizational Culture

Executives have long recognized culture as a critical driver of corporate success. Yet large-scale cultural change seems to remain stubbornly difficult. Reports often cite failure rates as high as 70%.⁴ Even though scholars debate the figure,⁵ most leaders know from experience: shifting culture is among the hardest management challenges. Why is this the case?

Modern organizational systems theory, rooted in the work of Niklas Luhmann, offers an explanation.⁶ From this perspective, culture resists direct control; it can only be influenced indirectly, through the way work is organized and how decisions are made. In other words, culture is not the cause of performance problems or successes, but an effect of how the organization continuously handles them.

As a consequence, culture cannot be treated as something leaders can actively engineer⁷ — through new values statements, training programs, or rebranding exercises. It emerges from repeated interactions and shared experiences. A further implication of the systems-theoretic view⁶ is that leaders should resist the common temptation to ‘fix’ people or their supposed mindsets. Behavior always makes sense within the organizational context that produces it. Thus, changing context, not people, is the bigger lever for achieving cultural shift. When that organizational context — such as decision routines, incentives, and communication patterns — is adjusted, new behaviors naturally emerge.

For executives, this shift in thinking is crucial. Instead of trying to design a desired target culture, they should focus on value generation and start identifying very specific cultural barriers that block it. Once cultural root causes are uncovered and understood, they can design targeted interventions that reshape how employees make decisions, solve problems, and collaborate. Done well, these interventions overcome the barriers to value creation — and, in turn, culture follows as a side effect.⁷ ⁸

So what does this perspective mean for the quest to build a data-driven organization?

3 Data Culture

To make the concept of data culture tangible, we start with a definition. Data culture can be understood as the recurring patterns by which an organization uses data, analytics, and AI to create or protect business value. These patterns show, for example, how much weight managers give to evidence in decision-making, whether data is shared proactively or hoarded for political advantage, and whether teams feel safe to challenge intuition with facts — and, just as importantly, to know when to follow intuition rather than relying on facts.

When executives talk about becoming data-driven, they often highlight the need for a strong data culture. It is frequently described as the missing ingredient that explains why so many investments in data and AI fail to deliver business impact.³ But from a systems-theoretic view, data culture, which is a specific perspective on organizational culture, is not something leaders can design and impose. Like culture more broadly, it emerges from how organizations actually use data, analytics, and AI in daily decisions.

This has two important implications: First, treating data culture as a prerequisite for success is misleading: culture does not come first. It is the result of people’s concrete experiences when solving problems in value creation with data and AI. Second, attempts to engineer data culture are like trying to pull on plants to make them grow faster instead of ensuring the right growth conditions. What matters is that leaders identify and address the root causes that hinder data from becoming a corporate asset. This requires the crucial skill of seeing the invisible: why does a manager refuse to share data with other departments? Why is it rational for that manager to behave this way?

When data culture is seen this way, it nicely complements data governance. The latter is often understood as designing and enforcing rules, processes, and roles to assure data quality and compliance,⁹ thus being a foundation for leveraging data as an asset.

Data governance and data culture are two essential and complementary modes of data value creation. They are the two sides of the coin that determine how an organization translates data into tangible value. While data governance focuses on creating a reliable foundation — the data asset itself — data culture determines how that foundation is used to improve business outcomes. The following framework contrasts these two modes across seven key dimensions.

Data culture becomes decisive at the moment of application — especially when teams must decide autonomously under uncertainty rather than ‘work by the book. Another crucial distinction is that data governance can be mandated, while data culture cannot — it only emerges.

4 Making Data Culture Practical: A Tool and an Example

If culture eludes design, what tools can leaders use to work with culture as a sensor? The Culture Board is such a tool helping to link business needs with cultural barriers and to create effective interventions.¹⁰ The following five-step process is based on its structure and adapted to focus on data culture:

1. Business Need: Frame the guiding organizational challenge clearly.
2. Identify: Uncover cultural patterns in light of the business need.
3. Sense: Distill and prioritize the barriers that matter most.
4. Creating: Design interventions that connect to daily practice.
5. Implement: Anchor interventions and observe their impact over time.

To see how the Culture Board works in practice, consider a hospital that sets out to improve patient safety by learning from medical errors. Although a reporting system was in place, almost no incidents were logged in useful detail making it impossible to generate insights and learn from past incidents. The business need was clear: the reduction of error rates was not only a regulatory requirement, but also a strategic demand to stay competitive.

In the identify phase, leaders dug deeper, and a telling pattern emerged. Staff avoided reporting. Doctors and nurses feared reputational damage or disciplinary action. On top of that, efficiency targets left no time for careful documentation. Thus, the Culture Board helped frame these issues not as a lack of human commitment, but as contextual barriers that had to be addressed.

In the sense phase, leaders agreed: unless incident reporting became safe and worthwhile, no real progress was possible.

Two interventions followed these insights. The incident reporting process was redesigned to guarantee anonymity. At the same time, incentive structures were adjusted: efficiency KPIs that solely focused on treatment efficiency, but punished detailed logging of incidents were removed.

The results were striking. Both the number and the quality of reports rose sharply. A small innovation team could now analyze the richer data, develop better insights and measures that reduced error rates in specific procedures. As these improvements became visible, staff began to trust the system and a new culture began to emerge.

This outcome also reveals a powerful dynamic of data value generation: the feedback loop. The primary path is clear: A formal change in data governance can impact data culture. But crucially, this new culture can, in turn, improve the data asset itself, as the example of staff providing richer reports illustrates. The interplay of data governance and data culture creates a virtuous cycle, where an emergent culture actively strengthens the very formal system that enables it. This cycle is the true engine of sustainable, data-driven transformation.

5 Conclusion

Executives often treat data culture as something broken that must be fixed before their organizations can become data-driven. But modern organizational theory suggests this is an error in reasoning. Attempts to engineer a desired data culture have low success rates by design. In contrast to data governance, data culture is the result, not the prerequisite, of becoming data-driven.

Instead, leaders should focus on identifying and outwitting the barriers that block value creation with data, analytics, and AI. When these barriers are addressed, business success with data improves — and a suitable data culture emerges as a side effect, creating a virtuous cycle that actively strengthens overall data value generation.

Adopting this perspective enables organizations to shift focus and investments away from broad cultural initiatives and standard recipes with inherently low chances of success. Instead, resources can be directed towards the real challenge: identifying data cultural patterns hindering value generation. Once the underlying contextual barriers are uncovered, this allows to design interventions that enhance value creation and, in turn, finally improve the return on data investments.

References

1 R. Bean, “Why Becoming a Data-Driven Organization Is So Hard,” Harvard Business Review, February 2022, https://hbr.org/2022/02/why-becoming-a-data-driven-organization-is-so-hard.

2 J. Linden, “How Most Organizations Get Data Strategy Wrong — and How to Fix It,” Towards Data Science, January 2025, https://towardsdatascience.com/how-most-organizations-get-data-strategy-wrong-and-how-to-fix-it-b8afa59f1533/.

3 G. Kesari, “Building a Data-Driven Culture: Four Key Elements,” MIT Sloan Management Review, January 2025, https://sloanreview.mit.edu/article/building-a-data-driven-culture-four-key-elements/.

4 N. Nohria and M. Beer, “Cracking the Code of Change,” Harvard Business Review, May 2000, https://hbr.org/2000/05/cracking-the-code-of-change.

5 M. Hughes, “Do 70 Per Cent of All Organizational Change Initiatives Really Fail?”, Journal of Change Management 11, no. 4 (2011): 451–464, https://doi.org/10.1080/14697017.2011.630506.

6 N. Luhmann, “Organization and Decision: Autopoiesis, Action, and Understanding” (Cambridge: Cambridge University Press, 2018), https://www.cambridge.org/core/books/organization-and-decision/F69AA272C0F1DC151692CF6ECAFCCE6A.

7 G. Wohland and M. Wiemeyer, “Denkwerkzeuge der Höchstleister“ (Frankfurt: Unibuch Verlag, 2012), https://www.amazon.de/Denkwerkzeuge-H%C3%B6chstleister-dynamikrobuste-Unternehmen-Marktdruck/dp/3934900119.

8 M. Poppenborg, “Wir führen anders“ (Hannover: intrinsifyVerlag, 2021), https://www.amazon.de/befreiende-wirksame-Organisationsentwicklung-Mitarbeitermotivation-Leadership/dp/3947886128.

9 DAMA International, “The Data Management Body of Knowledge (DMBOK),” 2nd ed. (Technics Publications, 2017), https://technicspub.com/dmbok2/.

10 F. Heuer and O. Kruggel, “Der Kulturarchitekt“ (Cologne: Verlag Dr. Otto Schmidt KG, 2022), https://www.fachmedien.de/heuer-kruggel-der-kulturarchitekt-buch/0201A24040211882?srsltid=AfmBOoohZOT1VmL5ErgU2shHpQZeJQY-Z0Nlu_kB7YRDXP51KJdyYjAk.

Stop Trying to Engineer Data Culture was originally published in Strategy, Organization & Culture for AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

The Hidden Reason Your Data Investments Fail

— This article was originally posted on Towards Data Science —

Data culture and data governance are increasingly cited as prerequisites for building data-driven organizations. Both are seen as essential for turning investments in Business Intelligence and Artificial Intelligence (AI) into business value. But here is the catch: data governance can be actively designed — data culture cannot.

This article is a detailed deep-dive of the management summary published on CIO.com. It explores the true role of data culture when an organization sets out to become data-driven — a role that may be quite different from what you expect. Drawing on modern organizational theory, I offer a practical approach to move beyond failed ‘data culture’ initiatives and propose a way of thinking that finally unlocks the value of your data and AI investments.

An appendix provides a Data Culture Cheat Sheet that distills the key points for immediate use.

I am Jens, a business-minded data expert with two decades of experience helping business leaders across industries unlock the full potential of data and algorithms.

tl;dr

• Many data initiatives stay behind expectations not because of technology, but because leaders misunderstand culture.
• Culture is an emergent property of an organization, not an object of design. It can be diagnosed and influenced indirectly, but never directly shaped or engineered into a desired form.
• Therefore, unlike data governance, data culture cannot be treated as a prerequisite for value creation with data and artificial intelligence. It is effect not cause.
• The real use of data culture is diagnostic: like a sensor, it helps to reveal the hidden barriers to leveraging data as an asset.
• Once the root causes of these barriers are identified, leaders can design targeted interventions that improve data value creation. When interventions have an effect, this indirectly shifts data culture as a side effect.
• True data value creation is possible when both are considered together: governance providing the reliable foundation, and culture enabling its effective application.

Table of Contents

1 Why do organizations strive for data culture?
2 Organizational culture
2.1 Limitations of mainstream approaches
2.2 Definition of organizational culture
2.3 Implications for cultural work
3 Data culture
3.1 Definition and value of data culture
3.2 Fields of analysis for identifying data-cultural barriers
4 Data governance vs. data culture
4.1 Data value creation
4.2 Distinguishing data governance and data culture
4.3 The interplay: A virtuous cycle
5 A tool to design and probe cultural interventions
6 Practical example — how a smarter approach to data culture can save lives
7 Conclusion
Appendix: The Data Culture Cheat Sheet
References

1 Why do organizations strive for data culture?

Most organizations aim to become data-driven in order to harvest the promised potentials of data being an organizational asset. They aim to apply techniques such as Business Intelligence (BI) or Artificial Intelligence (AI) to re-use operational data for analytical purposes. Applications include:

• Control: Reports or BI dashboards are used for monitoring and oversight
• Automation: Tasks to address complicated business challenges can be automated
• Decision making: Insights derived from data can sometimes support (not substitute!) human intuition required for decision-making to address complex business problems
• Innovation: Creating insights that help ask (and answer) the right questions about customers, competitors, technology, and industry trends

Despite the promised benefits many businesses struggle to unlock the value of their data [1]. And this is not a new problem. BI, Analytics and AI — not just large language models, but also Data Science and Machine Learning — have been around for many decades. Yet, turning data into measurable outcomes remains a challenge for many organizations.

As a result, in addition to strategic clarity [2], the term data culture is increasingly cited as a key success factor for organizations to become data-driven [3].

But what exactly is data culture? Is it really the precondition for turning data into business value? And is it possible to actively shape it into a desired form, such that value creation with data succeeds?

Since data culture is ultimately a perspective on the overall organizational culture, it is crucial to first align on what organizational culture is and how it should be approached.

2 Organizational culture

2.1 Limitations of mainstream approaches

Organizational culture has been a well-established field of study in sociology [4, 5, 6] for many decades. Yet many executives report that cultural change and transformation efforts are still among the most difficult challenges organizations face. Although the famously quoted failure rate about 70% [7] is subject to debate [8], there is broad consensus on one point: cultural change is far from trivial and many initiatives stay behind expectations. Why is this the case?

Modern organizational theory based on Luhmann’s system theory [9, 10, 11] offers an answer: Whilst many business leaders implicitly assume that culture is something that can be intentionally shaped, from a systems-theoretic perspective this is not possible. Consequently, the high failure rates of cultural change initiatives are not surprising, as many initiatives are designed based on a flawed assumption when taking on a systems-theoretic perspective.

Luhmann conceptualized organizations as self-reproducing, complex social systems of decision communication. Culture, in this view, is not an object that can be directly designed, but a latent structure of meaning that guides and constrains decisions.

Culture is an emergent product of organizational communication. It can be observed, perturbed, or nudged, but can never be engineered.

Applying these insights can increase the success rate for change and transformation initiatives in practice, justifying the increasing interest in systems theory for researchers and practitioners alike [12, 13, 14].

2.2 Definition of organizational culture

Adopting a systems-theoretic perspective here, organizational culture can be loosely defined as the largely unspoken and partly subconscious rules in an organization, the latter being a particular type of complex social system. Its purpose is to inform members of the organization about the expected behavior and thus makes certain actions more likely than others.

Organizational culture determines how things are done around here.

Culture acts as a so-called undecidable decision premise — a filter that becomes increasingly important in complex contexts, where individuals must decide and act more and more autonomously [14].

Typical examples of such unspoken and subconscious rules (culture) include:

• People help each other here
• We deal openly with mistakes
• In meetings, the highest-ranking person speaks first

2.3 Implications for cultural work

Adopting the systems-theoretic viewpoint provides new insights for cultural work in an organization:

Culture is not the cause but the effect of prevailing conditions.

Thus, the assumption that a ‘good culture’ is a precondition for successful value creation does not hold [14]. Culture is better understood as a symptom of underlying problems (or successes) in value creation.

Several practical consequences follow from the systems-theoretic foundation for work with organizational culture:

• Change context, not people: Leaders should shift focus from changing people or their ‘mindset’ to changing context, as this is the bigger lever for achieving behavioral change.
• Avoid culture-design initiatives: Attempts to engineer an ‘optimal’ target culture have inherently low success rates and should be avoided.
• Use culture as a diagnostic tool: Treat culture as a sensor for hidden obstacles to value creation, rather than a variable that can be directly shaped. Use it to uncover the root causes that explain seemingly irrational behavior blocking value creation.
• Work through small interventions and feedback loops: Design and probe interventions that change the context people work in and observe the feedback, such that the obstacles are resolved. Possible interventions are changes in structures, management systems, the setup of interpersonal exchanges, or protecting (new) ways of working.
• Beware of blueprints: Organizations are complex social systems. As such, you cannot expect causality. What works in one context may fail in another. Therefore, learning from successful organizations has often limited value.

The real value of culture work therefore lies in identifying unseen barriers, experimenting with small context-specific interventions, and allowing more suitable patterns to emerge — rather than trying to engineer a desired target picture of culture, e.g., through designed corporate values.

3 Data culture

3.1 Definition and value of data culture

Data culture is merely a specific perspective on organizational culture. One potential definition might be:

Data culture focuses on the shared habits, values, and informal rules that decide how we use data here to create or protect business value

Because data culture is ultimately organizational culture in action, the principles from Section 2 apply: you cannot engineer a desired data culture directly, nor is it possible to define universal best practices to influence data culture in a desired way.

Instead, organizations should use data culture to identify barriers that block the use of data as an asset, and then probe small interventions which gradually remove those obstacles. When successful, a suitable data culture will emerge by itself, once the value creation with data is functioning effectively.

As for organizational culture in general, the real value of data cultural work lies in its diagnostic power:

• Sensor: Reveals hidden problems in data value creation
• Lever identification: Points to root causes instead of symptoms
• Early indicator: Shows whether an intervention is beginning to work
• Risk management: Flags unintended side-effects during digital transformation

3.2 Fields of analysis for identifying data-cultural barriers

Where to start when identifying data-cultural barriers? It has proven helpful to look at typically relevant aspects of data culture. One possible collection of such aspects are the following six fields of analysis and their corresponding success patterns that often appear when cultural barriers are being addressed successfully. These should not be treated as target patterns for data culture design, but as perspectives to look at in order to identify relevant barriers.

1. Data Awareness: Leaders and employees understand both the opportunities and limitations of data-driven value creation
2. Data Leadership: Leaders actively demand and champion data-informed ways of working — where they add value
3. Data Literacy: All members of the organization possess the relevant interdisciplinary competencies to use data in a value-creating way
4. Insight-Based Way of Working: Everyone in the organization is willing to explore and exploit data’s potential for value creation
5. Collaborative Way of Working: Data and insights are shared willingly and proactively across boundaries
6. Data Availability: Users can access the data relevant to them — easily, securely, and in time

These patterns are signals to monitor, not targets to engineer. Their formation indicates that barriers are being reduced and that data culture is beginning to shift as a byproduct of successful interventions.

4 Data governance vs. data culture

4.1 Data value creation

Data governance is another perceived precondition for organizations to become data-driven. But how does it relate to data culture?

The term data governance is not uniquely defined, but one frequently quoted definition is that of [15]:

“Data governance is defined as the exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets”

Using this common definition, data governance is mainly about implementing processes, roles, and responsibilities to enable sustainable value creation with data. It covers the formal rules to enable value creation with data. These formal rules can be enforced by management with argument, rewards, or punishment. It therefore complements data culture.

Data culture and data governance are two sides of the same coin: governance brings order to the complicated, whilst culture creates adaptability in the complex.

The key is to see data governance and data culture not as separate initiatives, but as two essential modes whose interplay contributes to data value creation: the ongoing organizational process in which data is used, interpreted, and integrated into decisions to generate business value.

4.2 Distinguishing data governance and data culture

The interplay between data governance and data culture is best understood by observing it through the lens of a powerful guiding distinction used in organizational design [14] and management literature [16]: the distinction between the complicated and the complex.

Data governance is the attempt to master the complicated, while data culture is the emergent result of navigating the complex. This guiding distinction manifests across the organization in further corresponding distinctions.

1 Context: Complicated / complex

This is the guiding distinction from which all others follow. Complicated contexts are knowable and predictable, even if they are difficult. Their interactions follow clear cause-and-effect relationships. With a sufficient blueprint and expertise, a desired outcome can be engineered. Data governance is the primary tool for mastering the complicated, e.g. defining who may access data or ensuring that data quality is sufficient.

Complex contexts are unpredictable and emergent. Cause-and-effect relationships are not clear in advance and are constantly shifting. Data culture allows an organization to effectively navigate the complex, e.g. when employees need to decide to address complex business problems, they proactively share analytical insights to improve their decision making.

2 Primary focus: The foundation / the application

This distinction highlights what each mode prioritizes in the journey from data to value. Data governance focuses on creating a reliable foundation. Its primary concern is to render data a stable, predictable, and manageable object — the data asset itself. This is largely a complicated task: it involves defining rules ensuring quality, and structuring access according to clear cause-and-effect relationships.

Data culture, in contrast, enables the effective application of that foundation in the event of the decision-making moment. It determines if and how that stable foundation is actually used by people to navigate uncertainty and create business value.

3 Management: Command and control / commitment by choice

Data governance relies on command and control — the exercise of formal, positional authority. In contrast, data culture cannot be dictated. It enables decision-making in complex contexts through commitment by choice: an emergent phenomenon where people follow informal leaders based on voluntarily granted trust and perceived competence [13, 14].

4 Rules: Formal / informal

Data governance operates through formal, explicit, written rules found in policies and processes. These manifest as tangible artifacts: written policies, process diagrams, role descriptions, or data quality KPIs.

Data culture operates through informal, unwritten social norms and routines that dictate ‘how things are really done around here’. These manifest as invisible but decisive premises that guide action: the level of trust in a dashboard, the perceived relevance of data, the willingness to share insights, or the psychological safety to challenge data-driven assumptions.

5 Decisions: Work by the book / decide autonomously

Data governance requires people to work by the book to ensure consistency and standardization. This is execution — no decision required.

Data culture helps people to decide autonomously in the face of uncertainty. Either by combining the available data with intuition, or by asking the right questions about customers, competitors, technology, and industry trends, which then requires identifying what data might be missing to validate hypotheses.

6 Goal: Stability / adaptability

Data governance aims to create stability, a predictable and reliable foundation where data quality is consistent, access is controlled, and processes follow defined standards. This stability enables reliability and compliance and allows organizations to use data for decision making with confidence.

Data culture aims to enable adaptability, the capability to respond effectively to changing business demands, emerging opportunities, and unexpected challenges. This adaptability allows organizations to survive and thrive in dynamic settings and to leverage data in novel ways.

7 Creation: Can be enforced / needs to emerge

This captures the fundamental difference in their origin and emphasizes one of the key messages in this article. Data governance needs to be designed and enforced, whilst data culture needs to emerge as an indirect result of the prevailing structural conditions.

4.3 The interplay: A virtuous cycle

While the primary path of influence flows from data governance to data culture — with governance providing the reliable foundation for culture to emerge — the interplay goes both ways. A strong data culture brings the formal, complicated structures of governance to life.

Some examples are:

• Data quality — Data governance can define a data quality rule, but a culture of accountability motivates an employee to proactively report an anomaly observation.
• Metadata management — Data governance can mandate the creation of a data catalog, but only a culture of collaboration can ensure its ongoing curation with the rich, contextual, and up-to-date metadata that makes it truly valuable.
• Data acquisition — Data governance can define the processes for acquiring data, but a culture of inquiry constantly generates new business hypotheses that need testing, driving the acquisition of entirely new datasets through those very processes.

This feedback loop between data governance and data culture creates a virtuous cycle. The designed, complicated systems of governance and the emergent, complex behaviors of culture continuously reinforce and improve each other, driving data value creation far beyond what rules alone could enforce. The practical example in Section 6 will also illustrate this effect.

5 A tool to design and probe cultural interventions

How can we put the above theory into practice? How can we identify the cultural patterns that hinder value generation with data and AI and design interventions that address their root causes?

There are certainly different ways to approach culture work in an organization, here I select an approach, which we have successfully applied in our consulting practice. To structure the process of uncovering cultural patterns and designing interventions, we draw inspiration from the so-called Culture Board [17].

Starting from a validated business need, the board guides you through identifying data-cultural barriers, condensing what you discovered, designing connectable interventions, and finally implementing and assessing them. In detail, the five steps are:

• Step 1 — Business Need: Frame your guiding organizational problem clearly. Then trace it to its root cause to avoid treating symptoms. Document the business need to ensure a shared understanding between stakeholders. Hint: becoming data-driven is not your business need, it might be your solution.
• Step 2 — Identify: Analyze the data culture in light of the business need. What cultural patterns are in its way? The six fields of analysis from above can help to spot concrete barriers.
• Step 3 — Sense: Distill, integrate and document the barriers you have discovered to focus on what matters.
• Step 4 — Creating: Design connectable interventions that are likely to address the barriers.
• Step 5 — Implement: Anchor the interventions effectively and sustainably in the organization, then observe and evaluate their impact.

Because success of interventions is never guaranteed in complex social systems — like organizations — , the culture board is used iteratively: loop as often as needed until the the barriers are resolved.

Note that applying the culture board is in itself already an intervention: it exposes employees to previously unseen cultural patterns. These insights alone can already trigger change and have a positive impact on value creation with data.

The following example from healthcare illustrates how the culture board helps put this approach into practice.

6 Practical example — how a smarter approach to data culture can save lives

A hospital faces a high rate of treatment errors threatening patient safety, regulatory compliance as well as its strategic ambition to be a leader in quality of care. A so-called Critical Incident Reporting System (CIRS) is in place to capture and learn from near-misses and errors. However, it remains largely unused. The few reports submitted are often too vague for meaningful analysis.

Step 1: Business Need

To fulfill its strategic ambition and meet regulatory requirements, the hospital must reduce its error rate. This requires a systematic way to learn from incidents. The business need is not just to increase the number and quality of reports, but to create a reliable feedback loop that measurably improves patient safety. This is where solid strategy work [2] plays out, providing prioritization, management attention and the motivation for designing a data-driven organization.

Step 2: Identify

Using the existing culture as a diagnostic tool, interviews with medical staff reveal several powerful, unspoken rules that block the use of the CIRS:

• Fear: “Reporting an error is an admission of guilt. It starts a search for a culprit, not a cause.” This points to a culture of blame, where reporting feels like a personal risk.
• Effort: “We are overloaded as it is. Taking 20 minutes to document something for a system that gives nothing back is a waste of time.” This reveals a conflict with the core value of efficiency.
• Futility: “These reports go into a black box. We never hear what happens with them, so why bother?” This shows a lack of a visible feedback loop, making the effort seem pointless.

Step 3: Sense

Distilling these findings makes the core problem visible: The organization’s context is incompatible with the desired learning ambition. While management officially asks for learning from incidents, the system’s structures, incentives, and routines actually punish the required behavior. The problem is not the mindset of the staff; it is the context in which they work.

Step 4: Creating

Instead of trying to force doctors to better document incidents or initiating a ‘culture change program’, three precise, structural interventions are designed to address the identified barriers:

• To counter fear, a data governance intervention: Clear, binding rules for a fully anonymized and protected reporting process are established. The process is explicitly designed to be ‘blame-free’, a formal guarantee protected by management.
• To counter effort, a technology intervention: The incident reporting platform is redesigned for minimal friction, making documentation as quick and easy as possible.
• To counter futility, a data leadership intervention: A protected, interdisciplinary learning team is created, sponsored by a senior manager. This team is shielded from the daily efficiency pressures and has the mandate to analyze the reports, develop concrete improvements (like new checklists), and, crucially, communicate successes and learnings back to the organization.

Step 5: Implement

The interventions create a positive feedback loop. Time to document incidents reduces significantly. As the first reports are handled under the new, protected process, staff see that it is safe and start providing detailed incident reports. The learning team can develop and optimize a surgical checklist based on early reports, which demonstrably reduces a specific class of error. This tangible success is widely communicated, proving the CIRS’s value, reinforcing report documentation further.

As a result, the quantity and quality of reports increase significantly. A new culture of psychological safety and data-based learning begins to emerge — not as a planned goal, but as a side effect of the tailored change in structural conditions.

7 Conclusion

Data culture is not a set of values to be designed and rolled out, but an emergent property of the organization. It is the effect, not the cause, of successful value creation with data and AI. In contrast to data governance, it cannot be engineered or demanded.

Stay critical of one-size-fits-them-all best practices for designing a ‘good data culture’. Each organization is a complex social system and reacts differently to interventions. It is emergent — you never know what results you will get when following such recipes. What works in one organization might fail in another.

Instead, use data culture as a sensor. Let it reveal the crucial barriers that prevent your data foundation from being translated into better business decisions. Once these root causes are visible, design small, tailored interventions to bridge this gap, observe the impact, and allow a more effective data culture to take shape as a result.

The culture board is a practical tool for this data-cultural work. It helps leaders surface barriers, design targeted interventions, and iterate until the business need is met and, as a result, new cultural patterns have emerged.

Editorial support (spelling, grammar, wording, literature research) was provided with the assistance of generative AI. The ideas, structure, and arguments in this article are entirely created by the author.

I welcome your feedback, suggestions, and questions. Do you agree, disagree, or have additional thoughts? I look forward to engaging in discussions with you in the comments here on Medium.

Appendix: The Data Culture Cheat Sheet

References

[1] R. Bean, “Why Becoming a Data-Driven Organization Is So Hard,” Harvard Business Review, Feb. 24, 2022. [Online]. Available: https://hbr.org/2022/02/why-becoming-a-data-driven-organization-is-so-hard

[2] J. Linden, “How Most Organizations Get Data Strategy Wrong — and How to Fix It,” Towards Data Science. [Online]. Available: https://medium.com/data-science/how-most-organizations-get-data-strategy-wrong-and-how-to-fix-it-b8afa59f1533

[3] G. Kesari, “Building a Data-Driven Culture: Four Key Elements,” MIT Sloan Management Review, Jan. 23, 2025. [Online]. Available: https://sloanreview.mit.edu/article/building-a-data-driven-culture-four-key-elements/

[4] G. Burrell and G. Morgan, Sociological Paradigms and Organisational Analysis, 1st Edition. London: Routledge, 1979. doi: 10.4324/9781315242804.

[5] G. Morgan, Images of organization, Updated ed., [Nachdruck]. Thousand Oaks,CA: Sage Publications, 2011.

[6] E. H. Schein, Organizational culture and leadership, 1. ed. San Francisco: Jossey-Bass Publ, 1985.

[7] N. Nohria and M. Beer, “Cracking the Code of Change,” Harvard Business Review. [Online]. Available: https://hbr.org/2000/05/cracking-the-code-of-change

[8] M. Hughes, “Do 70 Per Cent of All Organizational Change Initiatives Really Fail?,” J. Change Manag., vol. 11, no. 4, pp. 451–464, Dec. 2011, doi: 10.1080/14697017.2011.630506.

[9] C. Baraldi, G. Corsi, E. Esposito, and K. Walker, Unlocking Luhmann: A Keyword Introduction to Systems Theory. Bielefeld: Bielefeld University Press, 2021. [Online]. Available: https://www.transcript-verlag.de/978-3-8376-5674-9/unlocking-luhmann/?number=978-3-8394-5674-3

[10] N. Luhmann, Organization and decision. Cambridge: Cambridge University Press, 2018. doi: 10.1017/9781108560672.

[11] J. Tække, “Luhmann and Organization Culture -A Systems Theoretical Analytical Strategy Luhmann and Organization Culture -A Systems Theoretical Analytical Strategy,” June 2025. [Online]. Available: https://www.researchgate.net/publication/392760245_Luhmann_and_Organization_Culture_-A_Systems_Theoretical_Analytical_Strategy_Luhmann_and_Organization_Culture_-A_Systems_Theoretical_Analytical_Strategy

[12] D. Seidl and K. H. Becker, “Organizations as Distinction Generating and Processing Systems: Niklas Luhmann’s Contribution to Organization Studies,” Organization, vol. 13, no. 1, pp. 9–35, Jan. 2006, doi: 10.1177/1350508406059635.

[13] M. Poppenborg, Wir führen anders: 24 ½ befreiende Impulse für Manager, 1. Auflage. Berlin: intrinsify.me GmbH, 2021.

[14] G. Wohland and M. Wiemeyer, Denkwerkzeuge der Höchstleister: Warum Dynamikrobuste Unternehmen Marktdruck Erzeugen, 1st ed. UNIBUCH VERLAG, 2014.

[15] D. Henderson, S. Earley, C. Bradley, and Data Administration Management Association, Eds., DAMA-DMBOK: data management body of knowledge, Second

[16] D. J. Snowden and M. E. Boone, “A Leader’s Framework for Decision Making,” Harvard Business Review, Nov. 2007. [Online]. Available: https://hbr.org/2007/11/a-leaders-framework-for-decision-making

[17] F. Heuer and O. Kruggel, Der Kulturarchitekt: mit dem Culture Board Unternehmenskultur analysieren und verändern, 1. Auflage. Düsseldorf: Fachmedien Otto Schmidt KG, 2022.

Data Culture Is the Symptom, Not the Solution was originally published in Strategy, Organization & Culture for AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

DEMYSTIFY DATA STRATEGY

Which is Responsible for What?

—This article was originally published on Towards Data Science —

There seems to be a consensus that leveraging data, analytics, and AI to create a data-driven organization requires a clear strategic approach. However, there is less clarity and agreement on exactly what this strategic approach should look like in practice.

This article provides a short overview of what strategy work I believe is required to become data-driven. It offers a summary of a detailed deep-dive I recently wrote and is the fourth installment in a series demystifying data strategy.

I am Jens, a business-minded data expert with nearly two decades of experience in implementing data and AI use cases. I help leaders across industries design strategies and foster cultures that unlock the full potential of data and algorithms.

Table of Contents

1 The Challenge of Becoming Data-Driven
2 The Problem With Data Strategy
3 Understanding Business Strategy
4 Common Data Strategy Misconceptions
5 Strategy for Designing Data-Driven Organizations
6 Conclusions
References

1 The Challenge of Becoming Data-Driven

The business world is currently abuzz with developments in Artificial Intelligence (AI) — from the race among leading technology companies to build ever more advanced models, to the opportunities for everyday businesses to leverage AI to cut costs, generate new revenue streams, or mitigate risks.

While time will reveal the full extent of AI’s benefits and risks, let’s look beyond the hype and focus on how organizations can harness data today to optimize or extend their existing business models.

Data can be leveraged in various ways, including:

• Control: Creating static reports for monitoring and oversight
• Automation: Automating tasks and decisions to address complicated business challenges
• Decision making: Generating insights that support decision making of complex business problems
• Innovation: Create insights to ask and answer the right questions about customers, competitors, technology and industry

The potential of data spans virtually every industry, including healthcare, finance, retail, manufacturing, energy & utilities, software development, media, and the public sector. Furthermore, data-driven opportunities exist across the entire value chain of organizations as well as within most supporting functions.

Despite this vast potential, many organizations struggle to identify and unlock the value of data, analytics, and AI. There is no simple recipe or universal plan to becoming data-driven. It is a complex challenge.

First, the use cases that provide value to a company are highly specific to its context. Second, not only are the use cases unique, but so too are the people, challenges, and external conditions that shape an organization’s journey to become data-driven.

Common challenges organizations face when trying to leverage data include People, Organization and Technology.

The complexity of becoming data-driven is widely acknowledged, and many organizations recognize the need for a strategic approach to managing this complexity. As a result, the term ‘data strategy’ has gained significant attention.

But what exactly is a data strategy? What problems does it solve, and what does it not address?

2 The Problem With Data Strategy

I argue that there is no universal consensus on these questions, neither within the data community nor among business professionals. Moreover, I assert that many existing interpretations of data strategy contain fundamental misconceptions [1].

I have been working in the data field for approximately 20 years — first as a data scientist in industry and consulting, and later as a data strategist helping organizations tackle the many challenges they face in becoming data-driven. Recognizing the lack of a shared understanding of data strategy, I felt that effectively guiding organizations requires a deeper grasp of business strategy.

Through my exploration of business strategy design, I arrived at the following conclusions:

1. Business strategy itself is widely misunderstood — despite the existence of well-established definitions. Even without the data aspect, a lack of ‘strategy literacy’ prevents many companies from achieving greater success — including leveraging data.
2. Data strategy is often misdefined — its use and interpretation frequently contradict established definitions and well-known strategy frameworks.

In my opinion, this lack of shared understanding creates a serious problem. It hampers focused discussions between data professionals — like myself — and executives, ultimately acting as yet another roadblock for organizations seeking to unlock the value of data.

However, I believe this issue is solvable. My proposal is to adopt a well-established business strategy framework and apply it to designing data-driven organizations. By doing so, we can create a common language and a shared understanding for both business leaders and data professionals.

My ambition is not to dismiss approaches of esteemed colleagues who have successfully applied their own methodologies. There is no single solution for complex problems. My aim is rather to contribute to clarity and a joint language of business and data professionals — ultimately increasing the effectiveness of designing data-driven organizations.

This article is a summary of a detailed playbook I recently published [1], providing a concise and focused overview.

3 Understanding Business Strategy

Business strategy is about making deliberate choices — choices about a company’s ambition, about which customers it serves, which products or services it offers, and how it delivers superior value relative to competitors. It also includes choices on which activities to prioritize and which systems are needed to measure success and progress. Every company makes these choices — either consciously and explicitly through a strategy design process or unconsciously and implicitly.

Strategy is defined as a set of integrated and mutually reinforcing choices that together form a compelling logic for how a company wins in the market. Winning means creating sustainable competitive advantage and delivering superior value relative to the competition.

This definition of strategy aligns with the Playing to Win framework [2], developed by Roger Martin [3], a globally recognized business thinker, CEO advisor, former Monitor consultant, and Professor Emeritus at the Rotman School of Management.

I personally consider Playing to Win one of the most powerful strategy frameworks available. I chose Playing to Win because it is widely recognized as a standard for effective strategy design. Moreover, it comes with a comprehensive ecosystem of resources, including literature [2, 4], structured processes [4a], templates, and training programs [5] — all of which support the design and activation of any kind of strategy.

A crucial aspect of the strategy definition above is integration — the choices forming a successful strategy must fit together into a cohesive whole. They must logically align and reinforce each other, leading to a plausible, difficult-to-replicate theory of how to outperform competitors.

To illustrate this concept, I like to use the analogy of LEGO bricks — just as LEGO pieces interlock to form a strong structure, strategic choices must be well-connected to create a solid competitive logic.

The Playing to Win framework organizes strategic choices into five key building blocks, visualized in the so-called Strategy Choice Cascade:

The cascade illustrates that an organization needs to make choices for:

1. Winning Aspiration: What winning means for the organization.
2. Where to Play: Which customers to serve and which products or services to offer.
3. How to Win: The approach to winning in the chosen market.
4. Capabilities: The critical activities and resources needed.
5. Management Systems: The systems, processes, norms, culture, and metrics required to build and maintain these capabilities.

These choices are not a loosely connected list but must be carefully integrated to form a coherent and compelling whole — this is your strategy.

Another crucial characteristic of strategy is that it is singular [4b]. This means that a company selling one product in one geography requires one business strategy. However, an organization may also have additional functional strategies. If a function has its own budget, it requires a strategy to guide its investment choices [6]. This leads to the development of HR, IT, or Marketing strategies, among others.

4 Common Data Strategy Misconceptions

A persistent misconception is that a separate data/analytics/BI/AI strategy is needed to define how these elements create value and competitive advantage. However, we have just established that an effective business strategy is singular: there is one coherent set of strategic choices that determines how an organization creates competitive advantage and delivers superior value relative to the competition.

Introducing a separate strategy that defines how an organization wins with data, analytics, and AI — alongside a business strategy that defines how it wins otherwise — is neither necessary nor advisable. To put it in the words of the 1986 fantasy action-adventure Highlander: “There can only be one.” [7]

Proposing a data strategy in this dysfunctional form is likely to irritate business professionals and executives — especially those with a strong understanding of strategy. The same principle applies to digital strategy — treating it as a separate entity rather than embedding it within the business strategy leads to similar misalignment.

For another typical misunderstanding of data strategy, you just need to ask ChatGPT (here I used version o3-mini-high), which nicely averages what the internet provides: “A data strategy is a comprehensive plan that outlines how an organization collects, manages, analyzes, and leverages data as a strategic asset to drive decision-making and achieve its business goals.”

Confusing plans with strategy is a well-known misunderstanding — even beyond the data context. Roger Martin, the originator of the Playing to Win framework, has dedicated significant time and effort to explaining the difference between a plan and a strategy [8, 4c-f].

In essence, plans focus on certainty — they outline projects with timelines, deliverables, budgets, and responsibilities. Strategy, on the other hand, is about uncertainty — it consists of choices that represent a bet on how an organization aims to win. Planning complements strategy but does not replace it. It should naturally follow strategy design as a means to activate the strategy.

5 Strategy for Designing Data-Driven Organizations

What strategy work is then required to design a data-driven organization? The answer is straightforward: if data, analytics, and AI contribute to your company’s ability to win, the relevant choices must be embedded in your business strategy. As with any approach that provides a competitive advantage, data-related strategic choices should not exist in isolation but as an integral part of the singular business strategy.

During the strategy design process [4a], organizations must identify where data can create a competitive edge. This defines the strategic data demands of an organization.

However, not every organization will differentiate itself through data, analytics, or AI [9]. For some, data-related efforts may remain purely operational, focusing on financial reporting, process efficiencies, or other standard practices that competitors also follow. In this scenario, your organization possesses ‘just’ operational data demands.

Regardless of whether data plays a strategic or operational role — or both — many organizations require a dedicated function for data, analytics, BI, insights and/or AI. To operate effectively, this function needs a strategy [6]. Depending on its scope and focus, you might choose to call this your ‘data strategy’ [10].

Interested in more details? I invite readers to deep dive into my data strategy playbook [1], which contains detailed processes, best practices and industry examples.

6 Conclusions

Strategy is key to business success. Unfortunately, rigorous and effective business strategy design appears to be a lost art. This is one reason why organizations attempt to apply dysfunctional data strategy variants, with the well-intended goal of helping organizations become data-driven.

However, a well-designed business strategy is singular and defines how an organization wins in the market. If a company wins with the help of data and AI, these strategic choices are simply part of the business strategy, forming an integrated set of choices that create a compelling logic for how the organization can sustainably outperform the competition.

As a consequence, organizations must develop both strategy and data literacy as foundational elements for designing data-driven organizations. Moreover, dysfunctional data strategy approaches must be discarded to create space for a shared language and mutual understanding.

By leveraging existing and established strategy frameworks, organizations can move beyond the common misconceptions surrounding data strategy and ensure that data, analytics, and AI truly serve as enablers of business success.

I welcome your feedback, suggestions, and questions. Do you agree, disagree, or have additional thoughts? I look forward to engaging in discussions with you in the comments here on Medium.

References

[1] Jens Linden, How Most Organizations Get Data & AI Strategy Wrong — and How to Fix It (2025), article published in Towards Data Science

[2] A. G. Lafley and Roger L. Martin, Playing to Win (2013), book published by Harvard Business Review Press

[3] Roger Martin’s website (2024)

[4] Roger Martin, Playing to Win/ Practitioner Insights (2024), website with list of articles

[4a] Roger Martin, The Strategic Choice Structuring Process (2024), Medium article of the ‘Playing to Win Practitioner Insights’ series

[4b] Roger Martin, Strategy is Singular (2023), Medium article of the ‘Playing to Win Practitioner Insights’ series

[4c] Roger Martin, The Five Deadliest Strategy Myths, Medium article of the ‘Playing to Win Practitioner Insights’ series

[4d] Roger Martin, Why Planning Over Strategy? (2022), Medium article of the ‘Playing to Win Practitioner Insights’ series

[4e] Roger Martin, Strategy vs. Planning: Complements not Substitutes (2024), Medium article of the ‘Playing to Win Practitioner Insights’ series

[4f] Roger Martin, From Strategy to Planning (2021), Medium article of the ‘Playing to Win Practitioner Insights’ series

[5] Jennifer Riel, How to Make Your Strategy Real (2024), IDEOU Blog entry

[6] Roger Martin, Jennifer Riel, The One Thing You Need to Know About Managing Functions (2019), article published in Harvard Business Review

[7] Jens Linden, Data Strategy with a twist — there can be only one! (2025), LinkedIn blog entry

[8] Roger Martin, A Plan Is Not a Strategy (2022), video

[9] Jens Linden, The Root Cause of Why Organizations Fail With Data & AI (2024), Medium article published in Toward Data Science

[10] Jens Linden, The Data Strategy Choice Cascade (2024), Medium article published in Towards Data Science

Data vs. Business Strategy was originally published in Strategy, Organization & Culture for AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

DEMYSTIFY DATA STRATEGY

How Most Organizations Get Data & AI Strategy Wrong — and How to Fix It

Redefining Data Strategy to Drive Competitive Advantage with Data, Analytics and AI

Unlocking business value from data, analytics, and Artificial Intelligence is a hot topic. Yet, most organizations struggle to monetize existing data or to create new data that advances their thinking. I assert that in most cases, this struggle stems from a lack of effective business strategy design.

This is the third installment in a series demystifying data strategy— something that organizations seem to need to become data-driven and remain competitive in today’s digital world. This document serves as a playbook for data strategy, offering a deep and structured exploration by addressing misconceptions, clarifying definitions, presenting actionable frameworks, and illustrating concepts through a detailed example.

I am Jens, a business-minded data expert with nearly two decades of experience in implementing data and AI use cases. I help leaders across industries design strategies and foster cultures that unlock the full potential of data and algorithms.

tl;dr

• A data strategy is not a standalone supplement to business strategy, nor is it a plan for building data capabilities. It is rather the strategy of a data function, providing services to the internal data customers.
• A data strategy does not define how an organization creates value with data, analytics and AI. This is the job of the business strategy with data-related strategic choices being an integral part.
• Misconceptions about data strategy, combined with weak business strategy design, prevent organizations from leveraging data effectively. These issues hinder their ability to gain a competitive advantage.
• The Playing to Win framework helps organizations with both: embedding data innovation into their business strategy and designing effective data strategies.

TABLE OF CONTENTS

1 THE FUZZ ABOUT DATA STRATEGY
1.1 Data Can Create Benefits Throughout the Organization
1.2 Organizations Struggle to Become Data-Driven
1.3 The Data Strategy Promise
2 DATA STRATEGY MISCONCEPTIONS
2.1 Misconception #1: A Plan to Build Data and AI Capabilities
2.2 Misconception #2: Approaches and Guidelines How Data is Managed
2.3 Misconception #3: A Supplement to Business Strategy
2.4 Quo Vadis?
3 STRATEGY
3.1 The Playing to Win Strategy Framework
3.2 What Strategy Actually Is
3.3 Plan vs. Strategy: The Typical Misunderstanding
3.4 The Strategic Choice Structuring Process
3.5 What Strategies an Organization Needs
3.6 Reinforcing Rods
3.7 What Strategies an Organization Doesn’t Need
3.8 Strategy Design Teams
3.9 Summarizing Strategy Fundamentals
4 DATA STRATEGY DEFINITION & DESIGN
4.1 Defining Data Strategy
4.2 Data Strategy Choice Cascade
4.3 Data Strategy Design Process
4.4 Step I: Identify Data & AI Demands
4.5 Step II: Collect Challenges
4.6 Step IV: Operational Capabilities & Systems
4.7 Step V: Maturity Assessment
4.8 Step VI: Planning
5 INTEGRATING BUSINESS STRATEGY & DATA
5.1 Data Choices as Part of Your Business Strategy
5.2 Data for Problem Formulation and Possibility Generation
5.3 Data Use Case Validation as Part of Testing
5.4 Concluding Remarks on Business Strategy & Data
6 PRACTICAL EXAMPLE
6.1 Step I: Identify Data & AI Demands
6.2 Step II: Collect Challenges
6.3 Step III: Designing a Data Strategy
6.4 Steps IV-VI: Activating Strategy
7 CONCLUSION
REFERENCES

1 THE FUZZ ABOUT DATA STRATEGY

Data, analytics, and Artificial Intelligence (AI) are not new phenomena in business but have grown increasingly relevant over the past decades.

A lot of excitement currently exists around generative AI due to its recent impressing advances for generating outputs such as texts or images. When I refer to AI, I do not mean solely generative AI, but the broader context of tools to generate value from data. And these tools include Business Intelligence, Machine Learning or Data Science and are everything but new. They have been around for many years.

Where does the persistent interest come from?

1.1 Data Can Create Benefits Throughout the Organization

Whilst data was once mainly relevant to accounting and controlling teams, it is nowadays often success-critical throughout the organization:

• Marketing uses historical campaign data to identify factors that drive success, enabling more effective allocation of marketing spend.
• Procurement uses data and analytics to analyze supplier performance, so that disruption can be minimized and cost savings maximized.
• Sales uses historical data to recommend an optimal point-of-sale portfolio leading to increased revenue.
• Customer service centers leverage data to predict customer churn, enabling proactive actions that reduce churn rates and drive sustainable revenue growth.
• Production departments leverage IoT data to prevent machine failures, reducing repair costs and minimizing production downtime.
• Product development teams enrich existing products and services with digital applications, such as delivering value-added insights to customers, increasing customer loyalty, and securing a competitive advantage.

Many organizations have been successfully leveraging value from data based on such analytical data use cases. But data and AI can do so much more.

Data and AI provide the means to question both, the status-quo of companies and entire industries. They allow organizations to gain a knowledge advantage by asking the right questions about customers, technologies, competitors and industry thus providing a mean to gain and sustain a competitive advantage. It is about combining data insights with human creativity to actively design the future [1a, 2].

Given the vast benefit potentials, it is no surprise that more and more organizations are eager to leverage data as a strategic asset. Companies strive to become data-driven — or rather data-inspired [2], which means that they aim to:

• Consistently leverage the potential of already existing data for optimizing their businesses
• Use data and analyses as integral part of their business innovation to rapidly test new creative ideas, which advances their thinking and increases competitiveness

So where is the catch?

1.2 Organizations Struggle to Become Data-Driven

Despite its promise, many organizations continue to struggle with leveraging the benefits of data, analytics, and AI. How is this possible after technology, algorithms and methodologies have been available for so many decades?

Becoming data-driven can be a complex endeavor

Organizations often underestimate the complexity of becoming data-driven, especially when the lack of strategic clarity and data literacy hinders progress.

A lot of things can go wrong when organizations start an endeavor to leverage data as an asset. It is not about buying some plug-and-play technology solution as many vendors tend to promise.

Becoming data-driven is often referred to as a data-driven business transformation [3]. It is called transformation for a good reason, as it typically involves changes that impact the entire enterprise.

For example, data needs to be acquired and integrated from various sources, so it can be made available for the business. The data integration requires company-wide orchestration and governance. Without it, each data consumer is left to repeat the tedious and error-prone work of data preparation, ultimately resulting in a ‘data wild west’ where different truths are reported, and valuable resources are wasted.

Leveraging data and AI is the responsibility of the business

The actual value generation with data mostly happens in the business units and functions, i.e. in Marketing, Procurement, Sales or Manufacturing.

Many assume that becoming data-driven is purely a matter of technical expertise in IT and analytics, overlooking the cultural and organizational changes it demands. But this is a fallacy.

Business users need to become data literate themselves. They need to embrace a culture of innovating, identifying and developing data use cases, which are relevant for the decision making in their area. They need to be able to use data to discover new insights that provoke new questions and spark innovative ideas to create competitive advantage.

Organizations require cultural and technical capabilities to become data-driven, but struggle to identify, build and maintain these.

Finally, leveraging data is a team effort and requires interdisciplinary stakeholders and thinking.

Data, analytics and AI are not the cure for everything

Data, analytics and AI are currently over-hyped and people seem to forget that the basis for each algorithm is mathematics. However, every statistical algorithm has underlying assumptions and inherent limitations. Relying solely on data for decision making is as bad as the opposite extreme, i.e. not using data at all for decision making.

The famous quote “prediction is difficult, especially when dealing with the future”, which originates from the Danish politician Karl Kristian [4], hints that you need to apply any forecasting with care: One underlying assumption of making predictions based on historical data is, that the underlying system, which generated the data will not fundamentally change in the future. This assumption can often be violated and a reputable data professional must carefully check it before applying fancy algorithms in a crystal ball fashion.

The fact that data, analytics and AI are over-hyped can be problematic, as stakeholders may have unrealistic expectations. This needs to be addressed, by establishing data literacy and culture in the organization fostering a common language and understanding leading to realistic expectations.

To provide an example, one particular field where analytics needs to be treated with care is the strategy design process, as the aim of an innovative strategy is often to uniquely shape the future with an impact on the market. If such a strategy is successful, a sales forecast based on sample data from the past might be unrepresentative and worthless to derive meaningful conclusions from it [1a, 1b].

The lack of business strategy as a root cause for frequent failure of data & AI

As I argued in the second article [5] of the Demystify Data Strategy article series, I believe that the majority of problems organizations have with managing and leveraging data as an asset stems from the lack of strategic groundwork. With strategic groundwork, I do not mean commonly used (mis-) conceptions of data strategy — as we consider below — , but genuine strategy design work which naturally integrates data-related strategic choices into business strategy.

Therefore, the present article extends this idea into a ready-to-use framework, leveraging tools of the well-known Playing to Win strategy framework.

But first, let’s have a closer look at the promise of data strategy and the misconceptions surrounding it.

1.3 The Data Strategy Promise

As the applications of BI and Data Science grew during the last decades, organizations increasingly struggled with managing the complexity of both: data management and the creation and scaling of analytical solutions. More structured approaches were needed and data strategy was and still is the supposed recipe to address these issues.

For writing this article I tried to find the origin of the term data strategy. Using the Google book search I found the book ‘Design and strategy for distributed data processing’ [6] from 1981.

Consequently, the term data strategy is far from new, but has gained increasing interest since 2010, as shown by a Google Trends [6] search, which matches with my personal experience as a data professional during this time period.

Strategy is generally perceived as something clever allowing to manage complexity and the promise of data strategy lies in addressing the challenges organizations face with leveraging data, analytics, and AI.

However, there is a catch: Despite its growing prominence, ‘data strategy’ remains poorly defined and is most often misunderstood, resulting in miscommunication, misaligned initiatives, and unmet expectations.

To understand why organizations continue to struggle with data strategy, we must first address the misconceptions surrounding the term.

2 DATA STRATEGY MISCONCEPTIONS

As a data professional and management consultant, I have learned that clear communication is critical for success. Clear communication begins with a common language, and this is where the problems with data strategy start: There is no consensus on what it actually means or should be. Data professionals, as well as business experts, use it in different contexts whilst meaning different things.

There is currently no universal agreement on what a ‘data strategy’ truly entails.

Furthermore, the most commonly used definitions of data strategy are incompatible with well-established strategy definitions. In my opinion this creates a severe problem, as it hinders target-oriented discussions of data professionals with executives and business stakeholders and is therefore a road-blocker for organizations to address their data challenges, preventing organizations to become data-driven.

The lack of joint understanding for data strategy hinders target-oriented discussions between business and data professionals and is therefore a road-blocker for organizations to leverage data as an asset.

This lack of consensus leads to widespread misconceptions about data strategy.

While many publications on data strategy deliberately or unintentionally avoid explicitly defining the term — assuming the reader already knows what it should mean — when definitions are provided, they often reveal misconceptions. Let’s explore three of what I think are the most common ones.

2.1 Misconception #1: A Plan to Build Data and AI Capabilities

Plans focus on execution and timelines, while strategies center on making deliberate choices under uncertainty. Conflating the two leads to confusion and inefficiency.

The Playing to Win strategy framework, is quite clear about the distinction between a plan and strategy [8] as we shall see in detail below in Section 3.3. However, when I google the term ‘data strategy’, several of the page one results — skipping the ads — define it as a plan or roadmap. Some examples are:

1. “A data strategy is a long-term plan that defines the technology, processes, people, and rules required to manage an organization’s information assets.” [9]
2. “A data strategy is a well-defined plan designed to improve the effectiveness of company-wide data management.” [10]

In essence, data strategy definitions that fall into this category, are about plans for building capabilities for data, analytics and/or AI. These organizational capabilities encompass elements such as data architecture, data governance, data management, BI, data science and data culture amongst others.

Defining data strategy as a plan to build data capabilities reflects a common misunderstanding of what strategy truly entails. Strategy should first define what data-related capabilities your organization truly needs.

A plan to build data, analytics and AI capabilities is not your strategy, but the output of your strategy work.

To create such plans can in itself be a challenging endeavor and their design is absolutely needed to leverage data as a company asset. So it is well invested time and requires solid competencies in a wide range of interdisciplinary fields. All I am saying is simply don’t call it data strategy, as it is what it is: a plan.

2.2 Misconception #2: Guidelines How Data is Managed

Another common definition frames data strategy as a kind of best practice guidelines clarifying how data is acquired, stored, managed, and leveraged.

Approaches for managing data are essential operational tools that support a strategy but are distinct from it. Referring to them as ‘strategy’ risks overshadowing the importance of deliberate, strategic choices.

How data is acquired, stored and managed are important topics and are mission critical to create a scalable data management foundation in any organization. However, approaches and guidelines should not be called strategy. They are just different things.

2.3 Misconception #3: A Supplement to Business Strategy

I must admit, early in my career, I too fell for the misconception that data strategy could exist as a separate supplement to business strategy. However, a data strategy should never stand apart, defining how data creates business value. This is inherently the role of the business strategy itself, as we will explore further in Section 5.

I like to visualize this using Lego blocks: the business strategy is an integrated set of interconnected Lego pieces, while a misconceived data strategy appears as a separate entity forcibly glued on top by the infamous ‘Lord Business’ with his dreaded ‘Kragle’ glue [11], stifling creativity and preventing innovative design.

What do I mean with data strategy as a supplement to business strategy? Whenever data strategy is defined as ‘something that defines how an organization will leverage data to realize its strategic objectives’, that is a misconception [1c]. A a well-defined strategy is singular [1d] with one business strategy for each indivisible business entity. The set of choices for the business strategy forms a harmonious overall whole. Trying to force something on top, such as a separate way of winning by leveraging data and AI, simply does not work. Therefore:

Having a supplementary data strategy that amends your business strategy by stating how your organization can use data, analytics, and AI to create business value and gain a competitive edge is not something your organization should aim for.

Instead, as I argued in part 2 of the Demystify Data Strategy series of articles [5], your business strategy should clarify if and how your organization uses data, analytics and AI to gain a strategic competitive advantage. A well-defined business strategy already includes how data creates value [1c]. Adding a supplementary data strategy dilutes focus and undermines integration.

Section 5 discusses a way to how to design a business strategy that leverages data strategically.

2.4 Quo Vadis?

To circumvent the first two of the three stated misconceptions for data strategy is relatively straightforward, as it is simply agreeing on a common language. Plans are plans, guidelines are guidelines, and strategy is strategy. These are distinct concepts, and both data and business professionals must recognize this in their communication.

But what about the third misconception, the fact, that there should not be a supplementary strategy for data?

What’s missing is a coherent definition of data strategy, that is understood from both, data professionals and business professionals alike.

To resolve this misconception, we need therefore two things:

1. A coherent definition of data strategy that aligns with common business strategy definition and
2. A process for integrating data-related choices into the fabric of business strategy

The remainder of this article will address this. It provides:

• A definition of data strategy, which is compatible with the definition of strategy in a business context
• Clarity on the role of business strategy for leveraging data as an asset
• A process for doing all the strategic work necessary to become data-driven

An important side effect of the suggested framework will be its positive impact on data leadership, contributing to the development of data culture and data literacy of executives.

The remainder of the article is structured as follows:

• Section 3 provides a summary of the renowned Playing to Win Strategy framework, which will serve as basis for all strategic groundwork required to become data-driven.
• Section 4 presents a definition of data strategy, that aligns and fits into the Playing to Win strategy framework. It also provides a detailed end-to-end process to conduct all the strategic work required for an organization to become data-driven.
• Section 5 clarifies, that designing a data strategy as defined in Section 4 alone will not be sufficient for an organization, that aims to become data-driven. You rather need to weave data-related strategic choices into the fabric of your business strategy thus blending strategy design and data use case innovation and development.
• Section 6 presents a detailed example of designing a data strategy for a salty snack food company that produces and sells potato chips and nuts. This section is intended for readers who wish to explore the data strategy design process in depth. Readers may skip this section without losing the overall flow of the article.
• Section 7 provides some concluding remarks.

3 STRATEGY

3.1 The Playing to Win Strategy Framework

Playing to Win is a strategy framework developed by Roger Martin [8], a globally recognized business thinker, advisor to CEOs, former Monitor consultant, and Professor Emeritus at the Rotman School of Management.

The framework was developed and continuously refined in the 1980s and 1990s [1e], and culminated in a joint book [13] by Roger Martin and A. G. Lafley, who is a former CEO of P&G. The book was published in 2013.

The framework became the standard strategy methodology at P&G and has since been successfully applied across numerous industries, establishing itself as a widely recognized approach to strategy. In addition, the Playing to Win framework has constantly been detailed by Roger Martin through an extensive series of Strategy Practitioner Insights [1, 1f].

While other strategy frameworks exist, I chose Playing to Win because it is widely recognized as a standard for effective strategy design. In addition, it comes with an entire ecosystem of resources such as literature, processes, templates and trainings that can be leveraged for designing any kind of strategy.

While Playing to Win provides a robust framework for designing business strategies, it also offers a way how data-related choices should be integrated into business strategy. This will be explored in detail in Section 5.

So, what is then a clear definition of the term strategy?

3.2 What Strategy Actually Is

Defining the term strategy

The Playing to Win framework for strategy design defines strategy as an integrated set of choices that form a plausible theory allowing an organization to win against its competition.

“Strategy is an integrated set of choices that uniquely positions a firm in its industry so as to create sustainable advantage and superior value relative to the competition.” [13]

The framework emphasizes “winning” to highlight that the ultimate goal of strategy is competitive success, not mere survival or participation in the marketplace. If you just play to play, you risk that at least one competitor figures out how to win and will ultimately push your company out of the market.

The Strategy Choice Cascade

The choices a strategy design team needs to make can be structured into five groups, which together build the so-called Strategy Choice Cascade, which is one of the core tools of the Playing to Win strategy framework.

I like to visualize the cascade using Lego pieces: Each box in the cascade represents a set of strategic choices — each choice symbolized by an individual Lego piece. The choices within each box are interconnected, and the boxes themselves reinforce one another, creating an integrated and cohesive strategy.

The cascade illustrates that an organization needs to make choices for:

1. What winning means for the organization (Winning Aspiration)
2. Which customers to serve and what products to provide (Where to Play)
3. How the organization aims to win with these customers in the market (How to Win)
4. What critical activities and resources it needs to realize this (Capabilities), and
5. What systems, processes, norms, culture and metrics are required to ensure that these capabilities are built and maintained (Management Systems)

These choices are not meant to be a loosely-coupled list, but need to be well integrated to arrive at a compelling whole. This is your strategy.

Explicit vs. implicit strategy

If you think about it, every organization makes these five choices either explicitly and consciously in form of an articulated strategy or implicitly through its actions. E.g. an organization makes choices which products to sell and which not, or which customer to serve and which not.

As a result, every organization has a strategy — whether explicitly articulated or not. Such implicit strategy can be deduced from the actions a company takes [1g].

One problem with such implicit ‘strategy-in-use’ is that there might not be a consensus in the organization what the strategy is. This has two implications:

• It is difficult for the organization to improve as it is not clear where it currently stands.
• Actions based on it may be ineffective, as stakeholders act counterproductive.

Use cases for applying the cascade

The cascade is a tool that is not filled out just once during strategy design, but it is revisited and utilized repeatedly for various use cases throughout the strategy design process. For instance, it is used to:

• Reconstruct the strategy of your competitors by observing what they do: This is a nice practice to familiarize yourself with the framework and is a kind of icebreaker [1h] to get started with the strategy design process.
• Document your current strategy: This is a good way to build a common understanding of the status-quo and make your current implicit choices explicit, if this has not been done before.
• Document new strategic possibilities: The process for designing a strategy [1i] contains a creative element, where many possibilities for the future target strategy are generated and systematically evaluated. These are documented and detailed using the strategy choice cascade, one for each possibility.

3.3 Plan vs. Strategy: The Typical Misunderstanding

As the term strategy is associated with intelligence and purposefulness, it is used in a lot of situations for many things and activities. It is used to describe goals, mission and vision statements, business models, slogans or themes or any kind of initiative. If you want to make a term sound more fancy, just use strategy as a post-fix.

A list of typical strategy myths is provided in [1j], including the most dominant misunderstanding: A plan is not a strategy [8]. Roger Martin, the originator of the Playing to Win framework, spent a lot of time and energy to explain the difference between a plan and a strategy [8, 1j, 1k, 1l, 1m]. How do the terms differ?

Plans are about certainty. They lay out projects with timelines, deliverables, budgets, and responsibilities. Strategy is about uncertainty. It consists of choices formulating a bet how an organization aims to win.

Consequently, when most people say strategy, they actually mean a plan or a list of initiatives. However, these are completely separate things, although they go together: planning follows strategy. Once your strategy is designed and agreed on, you know the capabilities and systems needed to activate your strategy. To build these capabilities and systems, you craft plans.

This plan-strategy-confusion is also true for data strategy as we saw in Section 2.1.

Knowing what strategy is, how is it created?

3.4 The Strategic Choice Structuring Process

To design a strategy, it needs much more than simply filling out the five boxes. The second tool of the Playing to Win framework is an established process for strategy design, called the Strategic Choice Structuring Process.

This process is actually an all-purpose tool for addressing a gap between an actual and a desired outcome [1i]. It comprises seven steps.

Step 1: Problem Definition

Strategy is foremost a problem-solving tool [1n], so you first start with identifying your business need for strategy design. As your current strategy — implicit or explicit — is creating a problem, you require a new set of choices, that produce the outcome you desire.

Step 2: How Might We?

This step aims to produce a statement, that opens the discussion for the ideation phase in Step 3.

For the first two steps it is important to focus on the customer and not on internal elements.

Step 3: Possibilities Generation

This is one of the creative parts of the design process. It is about innovating different possibilities, how the problem identified can be addressed. For each possibility, a choice cascade is formulated.

Step 4: What Would Have to Be True?

In this step the strategy design team aims to uncover those conditions, that must hold, such that the possibility considered would make a great strategy. This exercise needs to be done for each possibility created.

The team asks what would have to be true for customers, the company, and competitors to uncover the underlying assumptions of each strategic possibility. It is important not to ask what is true, as this leads to unproductive discussions and the best strategies create a new future, that is, things that today are not true, might become true in the future due to the implementation of the new strategy.

Step 5: Barriers to Choice

Any condition identified in Step 4 can become a barrier if the strategy design team lacks confidence that it is true or can be made true in the future. An identified barrier can stop the design team to choose the corresponding possibility as final strategy in Step 7.

If a barrier has been identified, it needs to be tested.

Step 6: Testing & Transformation

For each barrier, a test needs to be designed and conducted to determine, whether the condition is currently true, or if there exists a plausible transformation path to make it true [1i]. This is the second creative element within the process.

The tests are not for achieving 100% confidence, which is impossible, but rather to shorten the odds for the strategic possibility, such that the design team is confident enough to pick it as the final strategy.

Step 7: Choice

After all strategic possibilities have been developed, potential barriers have been identified and tested, the strategy design team should have sufficient information to choose one possibility over others, which becomes the new strategy.

Note, that the Strategic Choice Structuring process contains elements of Design Thinking [1o] with phases of divergent thinking for Step 3 and Step 6, respectively [14].

3.5 What Strategies an Organization Needs

A company selling one product in one geography may only need one strategy [1d]. However, if business elements differ significantly in Where to Play and How to Win, each element will require its own strategy.

For each strategy, the above design process needs to be applied to arrive at a unique set of choices leading to a separate strategy choice cascade. Consequently, large organizations will have several strategy building blocks, and the challenge is to make them fit together and reinforce each other [1p, 1q, 1r].

“Strategy involves making choices under uncertainty, constraints, and competition, and that happens in lots of places across the company.” [1c]

Business strategies

The most core form for strategy is business strategy, which is the strategy for a service or product [1c] in one geographic region [1s]. At this indivisible level of strategy, an organization competes with given competitors for given customers segments.

Aggregation strategies

Another form of strategy is aggregation strategy [1q], which combines a set of strategies from a below level. The corporate strategy is simply the highest aggregation strategy within in organization. The task of an aggregation strategy is to add competitive value to the portfolio of businesses below, such that the whole is greater than the sum of its parts.

Typical nested aggregation levels are Corporate, Business Unit, Category and Brand. Another example for aggregation strategy is a regional strategy, such as for a given region or country.

Strategies for functions

A third form of strategy are functional strategies.

Functions are specialized areas within an organization, such as HR, IT, Finance, Marketing, and R&D, that provide support services to the broader organization or business units. They may operate as centralized teams (e.g., corporate functions) or decentralized units within specific business areas. The purpose of a function is to provide critical operational support and expertise to help the business units execute their strategies effectively.

Functional strategies are critical for ensuring that the specialized areas or teams within an organization deliver value to their internal customers while aligning with the organization’s overarching goals. Each function needs its own strategy [15] to determine:

• What services to offer to what internal customers
• How limited resources are prioritized
• How to achieve operational excellence while supporting the organization’s broader objectives

In addition to serving purely operational needs, some functions can play a strategic, integrative role by providing shared capabilities or systems that span across multiple business or aggregation strategies. These shared capabilities and systems are referred to as reinforcing rods [1q], which focus on creating synergies and align nested strategies across the organization. We will have a closer look at reinforcing rods in Section 3.6.

Strategy architecture

In larger organizations, where individual businesses have different competitors and customers, there exists an ecosystem of nested and inter-connected strategies.

Example

To illustrate the concept, I choose to depict a subset of strategy building blocks of a salty snack food company, that produces and sells potato chips and nuts.

There are two aggregation strategy layers: one for regions such as Germany and the UK and another one for the overarching corporate group level.

Below the aggregation strategy for the region Germany, there are four business strategies, each forming an indivisible unit, the base level where competition takes place. The nut brands epicure and healthy have different customers and competitors and need therefore a separate theory for winning, just as for private label nuts and potato chips.

The aggregation strategy for the German region provides reinforcing rods to leverage synergies provided by the Production and HR function. Each function requires its own strategy.

Where and how does a data strategy fit into the strategy architecture? In Section 4, I define data strategy as the strategy of the data function, situating it squarely within functional strategies. However, this alone is insufficient for an organization to become data-driven. As we will explore in Section 5, achieving this ambition requires more than just a well-designed functional data strategy — it necessitates embedding data-related choices into the individual business strategies, other functional strategies and the overarching aggregation strategies.

3.6 Reinforcing Rods

Similar to steel rods that strengthen concrete constructions, reinforcing rods are a concept in strategy that fortifies the overall strategic architecture. As discussed in Section 3.5, the primary task of an aggregation strategy is to add competitive value to the portfolio of businesses it spans. This is often achieved through reinforcing rods.

Reinforcing rods are capabilities or systems that span multiple business units or organizational levels, creating synergies that enhance value and competitive advantage. They strengthen the organization as a whole by linking different layers (e.g., corporate, business units, geographies) and enable them to perform better collectively than they would individually. In this way, reinforcing rods provide a net benefit to the indivisible strategies making them more effective.

Examples for reinforcing rods can be shared capabilities for R&D or brand building which are leveraged across regions.

Thus, reinforcing rods are the must-have capabilities or enabling management systems of an aggregation strategy. The aggregation strategy determines why these rods are needed and prioritizes them based on their strategic value to the organization. Their realization, however, is typically delegated to functions.

In summary, reinforcing rods are about building synergies with strategic impact across organizational levels, reducing duplication, and optimizing the use of specialized or scarce resources. They enable the organization to compete more effectively by ensuring that its nested strategies work together seamlessly.

3.7 What Strategies an Organization Doesn’t Need

Whenever you think you need a supplementary kind of strategy to amend or overlay your business, aggregation or functional strategy, it is likely a misconception. A strategy — by definition — is an integrated set of choices, which mutually reinforce each other. By overlaying a strategy with another one, you do not achieve integration, but the opposite, as you loose integration.

In [1c], Roger Martin provides examples for such undesired strategy degenerations. These are content strategy, partner strategy, growth strategy, sustainability strategy, digital strategy and data strategy.

As we already stated in Section 2.3, a data strategy as a supplementing entity to a business strategy is therefore a misconception that should be avoided.

The implications for this on our intention to help organizations to become data-driven to leverage data as an asset, will be discussed in Section 4 and 5.

3.8 Strategy Design Teams

A larger organization can have a set of different strategies, so who is in charge for what strategy design?

Typically, the leader of each entity that needs a strategy is ultimately responsible for making the corresponding strategic choices [1t]. This means the CEO is responsible for the corporate strategy, whilst the IT leader is responsible for the functional IT strategy and so on.

Strategy is rarely designed by a single individual, but it is rather a team exercise, where it is important to create engagement right from the start to activate the strategy [16]. However, this does not mean involving as many people as possible. The strategic decision-makers must possess the competencies to evaluate strategic choices in the context of competition, customers, and the company. These competencies should reside within the leadership team of the entity for which the strategy is being designed [1t].

Within a strategy design team, there typically exist the following roles:

• Sponsor: Responsible for overarching choices, advises and ultimately approves strategy
• Owner: The leader of the unit for which the strategy is designed holds ultimate responsibility for making strategic choices
• Core team member: People, that are responsible for jointly creating the strategic choices. For corporate strategy, this is the entire leadership team.
• Extended team member: People, that have a perspective, which is important to shape the strategy
• Subject matter expert: People, that hold a specific expertise

3.9 Summarizing Strategy Fundamentals

As we have seen, the Playing to Win framework provides a powerful and structured approach to designing strategies. It provides a rich ecosystem of definitions, tools and thinking that helps to generate any kind of successful strategy.

Strategy is not a plan, but an integrated set of choices that identifies a place to play and a theory for how to win there, that is backed by capabilities and corresponding management systems that build and maintain those capabilities [1d].

The Strategic Choice Cascade helps to structure the choices, which are necessary to be made. The Strategic Choice Structuring Process helps to design your strategy by starting with a problem formulating over generating and evaluating different possibilities until a final possibility can be chosen to be the new strategy to address the problem.

Now that we have a solid understanding of what strategy is and how it is designed, it is important to consider how data-related strategic choices fit within this framework. The misconception around data strategy — highlighted in Section 2.3 — can only be resolved by embedding data-related choices into the business strategy itself. This ensures that data, analytics, and AI are treated as enablers of strategic ambition, rather than as standalone or supplementary initiatives.

In the next section, we will explore how data strategy can be defined in a way that aligns seamlessly with the Playing to Win framework. In Section 5, we will then discuss how to ensure that the strategic demands for data are derived directly from the business strategy, creating a cohesive approach that integrates data use case innovation into the fabric of your organization’s overall strategy.

4 DATA STRATEGY DEFINITION & DESIGN

4.1 Defining Data Strategy

Most organizations require a dedicated function to provide data-related services or products, primarily to internal and eventually external data customers. This function requires a strategy as we saw in Section 3.5. This is your data strategy.

A data strategy is the strategy for the data, analytics, and AI function. It outlines how this function serves the organization’s broader business strategy by providing data-related products and services.

Your data strategy clarifies what winning means for the data function, who your internal data customers are, what services or products the data function provides, how it wins with its data customers, and what capabilities and systems are required to win.

Note that the data strategy does not define how data, analytics and AI create a competitive advantage for the organization. This is the task of your business strategy! In Section 5 I show, that data related strategic choices are a natural part of business strategy. The business strategy, in turn, provides the strategic data demands, the requirements for your data function, for which you design your data strategy.

Just as HR or IT strategies exist to serve the business, the data strategy is the functional strategy for delivering on data demands — not a standalone definition of how data creates value.

Failing to define a clear data strategy leads to a lack of focus, with the data function either trying to meet all demands (‘servile strategy’) or pursuing disconnected data initiatives (‘imperial strategy’) [15]. Both approaches undermine the function’s ability to win.

Depending on the nature and focus of the services and products provided in this function, you may call it data strategy, analytics strategy, BI strategy or AI strategy.

The strategy of such a function helps the data, analytics and AI teams to focus and is in my opinion the only entity which should be called data strategy.

4.2 Data Strategy Choice Cascade

When using the Playing to Win framework for the design of a data strategy, some terminology needs to be adjusted. This was topic of part one [17] of this Demystify Data Strategy article series. The adjustments we need, are:

• Offering → Data Offering: This includes products or services related to data, analytic or AI.
• Customers → Data Customers: People, who are served with the data offering. These are most likely to be internal stakeholders or groups within the company but can sometimes also encompass external stakeholders.
• Company → Data service provider: People, who create the data offering. Members of staff of the data function.
• Competition: Alternatives, that data customers might choose, other than the data service provider. This can range from data customers not served at all, serving themselves or using external services.
• Geography → Focus Areas: The business units, departments, teams, domains, or geographic areas your data strategy will focus on.
• Channels → Data Delivery Channels: How data customers get access to the data offering.
• Stages of production → Data Lifecycle Management: Which stages of the data lifecycle are done in-house or are outsourced.

After translating core elements, the Data Strategy Choice Cascade [17] can be defined as follows:

1. Winning Aspiration: The definition of what winning looks like for your function.
2. Where to Play: The playing field on which you will, and will not, choose to compete. This typically includes five dimensions: i) Focus Areas, ii) Data Customers, iii) Data Delivery Channels, iv) Data Offerings, v) Data Lifecycle Management.
3. How to Win: The strategic advantage your data strategy will create. How you will win with data customers sustainably.
4. Must-Have Capabilities: What critical activities and resources it needs to realize the How to Win.
5. Enabling Management Systems: Infrastructure (systems, processes, governance, metrics & culture), which is needed in order to support and sustain your data strategy.

4.3 Data Strategy Design Process

As for any strategy design, we can apply the Strategic Choice Structuring Process of the Playing to Win framework. I propose five additional steps for developing a data strategy to incorporate the business strategy requirements and to explicitly distinguish it from the subsequent plan to build capabilities and systems.

The five additional steps for data strategy design are described in the following subsections.

4.4 Step I: Identify Data & AI Demands

From the above definition, it should be clear that a data strategy should not answer how data, analytics, and AI create business value and a competitive advantage. As I argued in [5], this should be part of your business, aggregation and functional strategies. These were discussed in Section 3.5. The strategic data demands for your data function are derived from these strategies and complemented with operational and eventual future data demands.

In essence, Step I is about gaining clarity about the existing requirements for data, analytics and AI in the organization. In total, there are three sources for such data demands, providing a natural prioritization for the subsequent data function to be built.

Prio 1: Strategic data demands

When working on your strategic architecture, the strategic data demands across the layers of your organization’s strategy should become clear as the individual strategy design teams progress. The inherent difficulty is that strategy design is not a linear process but an iterative going back and forth. This applies for the design of a single strategy [1u] as well as for the orchestration of different levels within a strategy architecture [1q].

Typically, strategic demands for data occur in different levels of your organization and may create the need for data, analytics and AI being a reinforcing rod of the strategic architecture (cf. Section 3.6). This can even be the motivator for designing a central data function as we will see in Section 6.

Another way, the strategic data demands can manifest themselves is in form of data use cases. We will encounter an example for such use case, which directly contributes to the competitive advantage of an organization later in Section 5.1. This use case can then stem the demand for a novel AI capability. Most often, this requires the business to be supported by a corresponding function for data and AI.

A further important aspect of strategic data demands concerns the desire of an organization to become truly data-inspired [2]. That is to constantly push its thinking by creating insights about customers, technologies, competitors and industry to gain competitive advantage. There might be a bit of recursion here, as such capability will also be used in the very heart of the business strategy design process itself, as we will see in Section 5. If an organization, however, fully acknowledges the enormous potential of data, which is not limited to boosting operational performance manifesting in a backlog of best-practices data use cases, this certainly is an requirement for designing the data function. This would lead to a Where to Play Choice within the data strategy, to support leadership with their business strategy design, as we will detail in Sections 5.2 and 5.3.

Ideally, the data demands should be actively stated by the corresponding strategy owner and arrive via choice chartering to a corporate data leader.

The best way for a data professional to collect the strategic data demands as input for a data strategy is to be part of the actual strategy design teams and actively help to innovate and evaluate strategic data use cases as we will discuss in Section 5.2 and 5.3. If this is not possible, you should analyze the current strategy architecture of your organization. If designed correctly, the individual strategic building blocks should directly provide the strategic data demands.

Unfortunately, in many companies strategy design appears to be a lost art [1v] and it requires a bit of detective work to figure out the current strategy architecture of the organization.

The set of identified strategic use cases across all strategy layers provides the most important requirements for data, analytics and AI, as the corresponding use cases are critical to support the organization’s strategy and thus competitive advantage.

Prio 2: Operational data demands

Data demands do not solely arise from strategic choices as part of strategy, but can also come from operational choices.

There are usually only a handful of strategic choices for capabilities and systems as part of your strategy. It is simply not the task of a strategy to provide an exhaustive list of organizational capabilities and systems, just those contributing to the theory of winning.

Thus, your strategic choices are typically complemented by other choices called operative imperatives [1w]. These are those operational choices which do not differentiate your organization from the competition, but are rather standard or best practices choices, positive things that everybody else is doing.

An example for such operational choice with data relevance might be capabilities for reporting and BI of a corporate strategy. It is best practice to have BI solutions for reporting in an organization, but these might not contribute to your theory of winning — they are just an operative imperative.

As with strategic data demands, another way operational data demands can manifest themselves is in form of data use cases. These operational data use cases provide point optimization such as efficiency gains of existing processes.

I typically discover the operational data needs through a series of interviews, which I conduct with business and data stakeholders of my clients.

Prio 3: Future data demands

In order to build a future-proof data strategy, an organization should consider a third category for data demands.

Often, different parts of the organization already have ideas or even plans how to leverage data and AI for their domain in the future. It is therefore important to identify these future data demands as these might have implications for the strategy of the data or AI function.

Such future data demands can be collected either in a passive way through interviews with business stakeholders or in a more active manner by conducting data use case innovation workshops.

Once all data demands are clear, you need to get a solid understanding of current pain points before diving into the data strategy design process as these might shape the problem formulation.

4.5 Step II: Collect Challenges

After clarifying the requirements for your data function, you need to identify potential challenges for leveraging data in your organization. These challenges will form the basis for selecting the problem definition in Step 1 of the Strategic Choice Structuring Process.

The task is to identify the most pressing deficiencies with data value generation in the organization. I typically include this as part of the interviews with business stakeholders when identifying the data demands in Step I. In fact, I usually ask for both, business-related and data related jobs, pains and gains. For documentation purposes, I like to borrow the right half of the famous value proposition canvas [18].

The outcome of this step is a solid understanding of most pressing data-related challenges, which together with the data demands provide a kind of fit-gap analysis as basis for Step 1 of the Strategic Choice Structuring Process.

As Step III of the data strategy design process is the standard Strategic Choice Structuring Process, which we already discussed in Section 3.4, I will skip a repetition here and directly jump to Step IV.

4.6 Step IV: Operational Capabilities & Systems

After Step III has been finalized, the data strategy is readily designed and needs to be activated [16]. This is done, by designing and building those capability and systems, which are required for the function to win. To build an effective data function, it is therefore essential to have a clear understanding of both, strategic and operational capabilities and systems required.

Strategic capabilities and systems are typically a small subset of capabilities and systems the data function requires. These are defined in your data strategy. Operational capabilities are those foundational activities, that enable the effective and reliable execution of strategic choices but do not directly contribute to winning with your data customers.

The distinction between strategic and operational capabilities and systems provides a natural prioritization where to invest most time and resources when building these elements.

As we saw, the operational data demands have been determined in Step I. Operational capabilities and systems are often directly derived from these. This could for instance comprise capabilities such as effective reporting or BI. Note that Step III.5 of the strategy design process, which is the Testing & Transformation step of the Strategic Choice Structuring Process, might provide the need for additional operational capabilities and systems.

Step IV is therefore to complete the picture by gaining an overview of what operational capabilities and systems the data function requires to succeed. For this, it might be helpful to make use of capability maps. Capability maps for data, analytics and AI are a well known tool for data professionals (cf. Figure 2 above) and are often used as a kind of checklist [3] when building data functions.

Typical elements for operational Enabling Management Systems in the context of a data strategy are:

• Data platform
• Data catalogues
• BI tools
• Decision frameworks
• Systems for developing a data culture
• Data maturity models
• Data Governance frameworks
• Skill matrices
• …

Once you have an overview about all strategic and operational capabilities and systems the data functions requires to succeed, it is time to assess the status-quo and to identify gaps.

4.7 Step V: Maturity Assessment

After the strategic and operational data capabilities and systems are defined, it is essential to assess your current state by conducting a data maturity assessment.

Many consultancies and data professionals tend to start with this type of activity early in the data strategy design process. However, I believe this approach is misguided. Whilst it is certainly valuable to have an overview what capabilities the organization currently possesses, a detailed assessment at the beginning of the strategy design process is of little value. You first need to identify the specific capabilities and systems required, along with the desired level of maturity for each. This means the set of capabilities and systems together with their respective target state must be defined. And this is, however, only possible after the data strategy has been designed.

Additionally, I am skeptical about data maturity assessments that focus on bench-marking against competitors or industry averages as I already argued elsewhere [19]. While such comparisons may seem insightful, they often provide limited actionable value for your organization, as they do not account for the unique strategic data demands and context of your business. Such maturity assessments rather pretend to be a standardized, simple solution, which does not live up to the complex problem of becoming data-driven.

However, a thorough current-state assessment of your capabilities and systems is needed as the basis for Step VI, where the plans to build and maintain each capability and system are created.

In addition, some organization successfully utilize data maturity models as a tool to measure their progress in data-driven business transformation programs and leverage it as a communication tool to various stakeholders in the organization. In these scenario, more complex metrics than simple traffic lights might be favorable.

4.8 Step VI: Planning

After performing a fit-gap analysis for each capability and system, it is time to craft a plan for building and maintaining the capabilities your data function requires to win. The plan to build data-related capabilities and systems is the topic most companies are enthusiastic about when it comes to data, analytics or AI. As we saw in Section 2.1, this is often falsely declared as data strategy.

Building each capability and system is typically a project in its own right, requiring an owner and a corresponding project team. The owner is ultimately responsible for designing and building the element.

The individual projects are often part of an overarching transformation roadmap for building a data-driven — or data-inspired — organization.

Implementing the corresponding projects gradually builds the operating model [20] for data, analytics and AI [21], which thus activates your data strategy.

Before we have a more in depth look at the data strategy design process using a practical example in Section 6, let’s consider how business strategy design and data use case design should be combined. This is required to determine your strategic data business needs, which is part of Step I above. We will discover this in the next Section.

5 INTEGRATING BUSINESS STRATEGY & DATA

In Section 4 we saw that it is not task of the data strategy — defined as functional strategy — to define how the organization creates value or a competitive advantage using data, analytics or AI. This is the task of the business, aggregation and/or functional strategies, which we defined in Section 3.5. These strategies define how to win. And sometimes, the organization can win with the help of data, analytics and AI.

In this section, we first have a look at how data strategic choices are a natural part of business strategy design. With data strategic choices I mean those choices of an organization’s theory to win, which rely on or involve the use of data.

I then argue, that data use case discovery, innovation and validation should be viewed as an integral part of the Strategic Choice Structuring Process for business, aggregation or functional strategy design. This naturally bridges business strategy and data use case design, which is often perceived as one of the greatest challenges for organizations to become data-driven.

5.1 Data Choices as Part of Your Business Strategy

When you make your choices of Where to Play, How to Win and what capabilities and systems you need to win as part of your business strategy design, some of these choices might rely on leveraging data, analytics and AI. Strategic choices for data and AI capabilities are a natural part of your business strategy. I provided some examples for this in [5].

Therefore, organizations should consider data and AI as something, which needs to be weaved into the very fabric of their business strategy.

Strategic choices with data or AI relevance are simply part of your business, aggregation, or functional strategy.

For illustration purposes, think of your strategic choices as Lego pieces, from which you build your Strategic Choice Cascade, and data-related strategic choices are just an integral part of it.

Let’s have a look at an example, where AI is a capability as part of the strategy.

Example

I used this example, which is borrowed from [1x], already before in part two of this series of articles [5]. It is about a salty snack producer, which uses a direct-store-delivery system, where the product is directly delivered to convenience stores and placed on the shelf by the delivery driver.

This direct-store-delivery system is labor-intensive and hence expensive, but it differentiates the company from its competition. It is a strategic choice for the regional aggregation strategy, those realization might be chartered [1r] to the Sales department.

Building data and AI solutions to predict store inventory and to generate optimal product orders for each point-of-sale helps reduce costs for the direct-store-delivery system. Hence, the choice to posses a data and AI capability, which enables the organization to predict store inventory, would be a strategic choice of the function Sales strategy, which directly supports the competitive advantage of the organization.

This data-related strategic choice provides a clear strategic data demand [5], i.e. a strategic requirements of the organization for the usage of data and AI. It is an integrated part of the company’s strategy and reinforces other strategic choices regarding differentiation from the competition.

The example illustrates that strategy — in this case it was the strategy of the Sales function — should define how data and AI create strategic value in the organization and not a supplementary data strategy ‘misconception’.

Data use case innovation should therefore be an integral part of business strategy design. The following two subsections show that the strategy design process of the Playing to Win framework provides a natural environment to integrate data use case innovation.

5.2 Data for Problem Formulation and Possibility Generation

Data use cases are the heart of data value creation, but organizations often struggle to identify their individual set of feasible and impactful use cases.

When innovating data use cases, it is often not differentiated whether the purpose is optimization of existing processes or the creation of new businesses opportunities. However, it is essential to differentiate between operational use cases, which address routine needs or improve efficiency within existing systems, and strategic use cases, which directly support competitive advantage and enable new ways of operating. The latter should be part of business strategy design.

There exist well-known and proven formats and techniques for use case discovery and innovation [2, 22]. These often incorporate elements of Design Thinking and are called Data Thinking [23], due to the inherent iterative approach required.

In fact, these formats and techniques can — and should — be directly integrated into the business strategy design process rather than being stand-alone exercises. This aligns nicely with the Strategic Choice Structuring Process, which is not surprising, as it also leverages Design Thinking [1o]. Thus, the Playing to Win framework allows to naturally combine data use case innovation and business strategy design, bridging the gap between business and data folks.

Integrating data use case innovation into the business strategy design process contributes to bridging the gap between business and data folks

This way, the business strategy design team can leverage data and AI as a tool to create strategic innovations by combining human creativity with data insights. This applies to Step 1 – problem formulation – as well as to Step 3 – possibility generation. Let’s have a look at an example.

Example

Consider the salty snack food company as in the previous examples. The salty snack food company noticed declining appeal among health-conscious consumers. An analysis of sales data, combined with social media sentiment analysis, highlighted a key insight: customers increasingly sought personalized snack options. Inspired by this insight, the strategy design team moved beyond merely optimizing existing offerings and stated the following problem with the corresponding how might we question:

1 Problem Definition

Customers are increasingly seeking personalized snack options, but our current product offerings are standardized, leading to declining market share among health-conscious consumers.

2 How Might We Question

How might we better meet the growing demand for personalized snack options among health-conscious consumers?

By combining the data-driven insights with creative formats, the team developed the following innovative possibilities:

3.a Possibility ‘Snack Kits’: Offer build-your-own snack kits in stores where customers can manually select ingredients to create their own mix, catering to their unique preferences.

3.b Possibility ‘Influencers’: Collaborate with health and wellness influencers to design branded snack bundles targeted at their followers, promoting personalization through co-creation with trusted figures.

3.c Possibility ‘Direct-to-Consumer Platform’: Design a direct-to-consumer platform where customers can input their dietary preferences and receive personalized snack bundles using AI-driven recommendations based on insights from customer data.

These possibilities weren’t simply derived from data — they were inspired by data, leveraging insights as a foundation for creative solutions that addressed both customer desires and business goals.

Furthermore, with possibility 3.c the design team created a new data use case. This illustrates that within the strategy design process, data and AI can be used to:

1. Inspire the problem formulation
2. Create new data use cases when innovating new possibilities

The implication for this is, that:

Subject matter experts for data, analytics and AI should be a natural part of any strategy design.

This connects nicely back to Section 4.4 and serves as a general motivation for including ‘business strategy design support’ as a Where to Play choice within the data offering of the data strategy.

Not only the innovation of new data use cases can be incorporated into the business strategy design, but also the subsequent iterative and incremental development.

5.3 Data Use Case Validation as Part of Testing

When organizations discover or innovate data and AI use cases, it is not always clear whether the desired solution will work as intended. There are uncertain assumptions that need to be identified, evaluated and eventually validated often using proof-of-concepts or minimum viable products. This is where design thinking meets data use case design.

This process perfectly aligns with Step 6 — Testing and Transformation — of the Strategic Choice Structuring Process outlined in Section 3.4.

The Strategic Choice Structuring Process naturally allows integrating iterative and incremental data use case development.

For possibility 3.c (Direct-to-Consumer Platform) of the previous example, this might look as follows:

Example (continued)

For each strategic possibility generated, the assumptions for the possibility being a great strategy need to be identified and evaluated. This is the task of Step 4 — What Would Have to Be True? — of the Strategic Choice Structuring Process.

Step 4 is about identifying critical conditions for customers, competition and company. To illustrate the concept, some of the critical conditions might be:

For customers, it would have to be true, that health-conscious consumers are willing to use a digital platform to personalize their snack options.

For competitors, it would have to be true, that they are either unable or unwilling to replicate the direct-to-consumer platform quickly. This is the so called can’t/won’t test [1x, 1y, 1z].

For company, it would have to be true that we can consistently derive meaningful and actionable recommendations from historical consumer data to personalize snack bundles effectively.

If the strategy design team would not be reasonably confident about that any of the identified critical conditions is currently true or could be made true, it would declare it as a barrier, which might stop the team from choosing possibility 3.c as final strategy. If a barrier has been identified, the team would design and conduct a test, in order to learn more and to gain (or lose) confidence that the condition could be made true. This is Step 6 of the Strategic Choice Structuring Process.

For the last of the three critical conditions above (company), a meaningful test could be to design a minimum viable product, where a data science team builds an algorithm using data available. The recommendations are then probed with a set of potential customers.

Such a test would validate, if the company has sufficient data to make meaningful predictions and if the data quality of that data as well as the prediction accuracy of the used algorithms are good enough to derive meaningful recommendations.

The example illustrates, that:

The data use case innovation and development process applied by data professionals, can be seamlessly integrated into the generic strategy design process of the Playing to Win framework.

Note that by doing so, business experts and data experts work together closely to design and probe new strategic possibilities, which addresses a common problem in organizations: the existing gaps between business and data teams.

5.4 Concluding Remarks on Business Strategy & Data

To conclude this section, we first link the strategic data demands originating from the business strategy design process back to the data strategy design process described in Section 4.3.

Strategic Data Demands as Inputs for Data Strategy

We saw that a part of Step I of the data strategy design process is to determine the strategic data demands of the organization, which then become the input in form of requirements for building a data function.

From this one may conclude that:

A solid design of business, aggregation and functional strategies is a necessary condition for success with data, analytics and AI in an organization.

And this is where in my opinion the root cause of why organizations fail with data & AI lies [5]: With solid strategy design becoming a lost art [1v], many organizations lack a sound strategic architecture and strategy design process. The lack of strategic competencies has many negative effects for an organization. One such effect is that organizations struggle to use data as an asset.

By leveraging the Playing to Win strategy framework to create a solid strategy architecture, organizations can embed data-related strategic innovation and resulting choices into their business strategy, generating clear strategic demands that inform and guide the design of the data function’s strategy, ensuring alignment and focus.

Data leadership

As we concluded that leveraging strategic value from data, analytics and AI is the task of business strategy design, it is clear that the responsibility lies ultimately with the respective strategy owners, i.e. the corresponding leaders.

This emphasizes once more that leaders of all kinds and levels need to embrace a data culture, ensuring they understand, are willing to, are skilled to, and are required by their superiors to exploit possibilities to use data and AI as strategic levers. They need to become so called ‘data explorers’ as defined in [2].

Leaders of all kinds need to become data explorers

Data, analytics and AI are therefore a score skill in a digital world. That does not solely apply to data professionals such as data scientist or data engineers, but is also true for business leaders.

The ROI question for programs to become data-driven

Transformations of any kind are not an end in themselves. A transformation program to become data-driven should always arise from business strategy. Such programs are the plans and initiatives designed to activate the strategy, ensuring the realization of the organization’s Winning Aspiration, Where to Play, and How to Win choices.

When it comes to committing resources to these transformation programs, I often encounter executives and leaders raising questions about the return on investment (ROI) — an understandable concern. However, the evaluation of anticipated costs should occur earlier during the business strategy design process, not during data strategy design or even at the implementation stage.

Ideally, the need for specific capabilities or systems should naturally emerge from a well-defined strategy. When ROI calculations dominate the discussion, it may indicate that the leadership perceives the proposed investments as unaligned with immediate strategic priorities​.

During the business strategy design process, the design team should ask, “What would have to be true regarding the cost of building the required capabilities and systems?” If uncertainty exists about the costs, this critical condition becomes a barrier, prompting the need for testing. Such tests must evaluate both the strategic benefit and the associated costs, ensuring that data investments are assessed within the broader context of organizational priorities.

This again highlights the importance of involving data teams in the business strategy design process and demonstrates that strategy and execution are not separate phases but instead form a seamless transition to activation as soon as capabilities and systems are designed.

The next section provides a detailed walk-through of the developed framework, applying it to a practical example that builds upon the earlier case of a salty snack company.

6 PRACTICAL EXAMPLE

In Section 4.3, we introduced an amended version of the Strategic Choice Structuring Process for designing a data strategy. In Section 5, we demonstrated how data-related strategic choices are integral to the business strategy, forming a key requirement for designing the data function.

Now we have all jigsaw pieces together to do the strategy work which is required in an organization to leverage data as an asset using analytics and/or AI.

This section provides a detailed example, illustrating how a data strategy can be developed. For this, we use again the salty snack food company, that produces and sells potato chips and nuts, for which we design a strategy for a group-wide data function. The section is meant to be a deep-dive and can be skipped without losing too much context.

Recall, that we touched upon this example already in Section 3.5 and Sections 5.1, 5.2 and 5.3. We will now go through the steps of the data strategy design process depicted in Figure 11.

6.1 Step I: Identify Data & AI Demands

Strategic demands

As part of the group strategy design, the salty snack food company chose to add competitive value to the country businesses by building a new reinforcing rod. The possibility chosen entailed to centralize all major IT systems (Enterprise Resource Planning, Customer Relationship Management, Manufacturing Execution System), leading to enabling management systems that span across the entire organization.

Thus, the multinational salty snack food company decided to undergo a major IT harmonization program with the goal to centralize all systems and corresponding business processes across all countries.

Based on this, the group strategy design team also chose to build another reinforcing rod for data, analytics and AI, which should be realized through a newly group-wide data function. For instance, this entailed the harmonization of all reporting solutions for the individual countries.

From several regional Sales function strategies, another strategic demand was to provide solutions for store inventory prediction, as we saw in Section 5.1.

These strategic data demands were directly tied to the organization’s strategy, ensuring that the data function supports the competitive advantage of the organization. Strategic demands took precedence as they directly support the organization’s theory of winning, while operational and future demands addressed foundational and exploratory needs. Let’s have a look at the latter two.

Operational demands

A critical operational data demand for the new data function was to ensure that the harmonized systems and centralized reporting solutions were designed to seamlessly replace the existing local systems at the rollout time, without interrupting day-to-day operations. During the transition phase, the existing local data teams would be responsible for maintaining the continuity of current operational dashboards and reporting solutions (e.g. for sales tracking, inventory and production management).

Future data demands

Whilst preparing for the forthcoming data strategy design process, the design team discovered several future data demands through a series of interviews with the individual regional units.

One such future data demand was the planned digital manufacturing program in several producing countries, which aimed to optimize production and quality by intensively leveraging digital and data potentials. The program’s roadmap included potential data use cases, such as predictive process tuning for production machinery.

Strategy Design Team

The team for the designing the data strategy consisted of representatives from both the corporate data function and regional stakeholders.

At the corporate level, the team included the group data leader, who owned the data strategy as she was in charge of the group data function. Other members of the core team were a data governance specialist and an analytics manager overseeing centralized reporting frameworks. As the group CEO chartered the choice of building the reinforcing rod, he acted as sponsor for the data strategy design.

From the regional perspective, the core team involved the German division’s data manager, which oversees the German data, analytics and AI demands and solutions. Furthermore, a representative from the manufacturing function was part of the core team to ensure alignment with local needs, such as digital manufacturing initiatives.

In addition, external subject matter experts for strategy design, data and culture where leveraged. This ensured a sound methodical approach and allowed to address change-related challenges form the very start.

The extended team consisted of several analytics power users and stakeholders with key requirements, who were involved on demand.

This composition allowed the team to balance corporate priorities with the specific demands of regional teams.

6.2 Step II: Collect Challenges

During the preparation phase, interviews with stakeholders across the group and regional units uncovered the most pressing challenges related to data, analytics and AI. These challenges needed to be addressed during data strategy design, in order to ensure the successful implementation of the centralized data function while minimizing disruptions and maximizing the value of the group data function.

The following key challenges were identified:

1 Uncertainty about future state

• Regional units voiced concerns about the clarity of the target operating model for the group data function, especially regarding their role and autonomy in managing and leveraging data after the rollout.
• Stakeholders highlighted a lack of understanding of how the harmonized systems and reporting solutions will meet both group-level and region-specific requirements.

2 Resistance to centralization

• Several regions raised concerns about losing flexibility and responsiveness in addressing their unique data business needs, particularly in scenarios where ad-hoc reporting or rapid adjustments were required.
• Fear of a one-size-fits-all solution failing to meet local business nuances was cited as a potential hurdle to adoption.

3 Skills and capability gaps

• Regional teams highlighted a need for training and support to adapt to the new centralized systems, particularly for self-service analytics and AI use cases.
• A skill and capability gap of the local business was identified in leveraging predictive analytics use cases, such as those outlined in the digital manufacturing program.

4 Interim business continuity risks

• Maintaining uninterrupted operational reporting during the transition phase remains a top priority for local teams, as any disruptions could impact critical business decisions in areas like sales, production, and inventory management.
• Stakeholders highlighted challenges in meeting local interim demands for data and analyses. They expressed uncertainty about which new local solutions would be worth developing within the existing legacy environment prior to the roll-out and which solutions could be deprioritized.

5 Alignment with Future Programs

• The design team noted that the success of future programs, such as the digital manufacturing initiative, would depend on establishing a robust data infrastructure and architecture that is flexible enough to support emerging data use cases like predictive process tuning.

6.3 Step III: Designing a Data Strategy

The strategy design process followed the Strategic Choice Structuring Process depicted in Figure 7, which itself consists of 7 steps.

Step 1: Problem Definition

The data strategy design team began by defining the primary problem they aimed to solve. The team defined the problem as:

“We need to harmonize data, analytics, and AI services across all regions based on the newly centralized IT landscape, while meeting diverse regional needs, maintaining business continuity, and enabling future data-driven opportunities.”

Step 2: How Might We?

The how might we question was designed to inspire innovative possibilities for the data function’s strategy while keeping the focus on delivering value and addressing the core challenges.

“How might we create value for our regional units through harmonized data, analytics, and AI solutions, while addressing their unique needs and aligning with our centralized IT landscape and governance?”

Step 3: Possibilities Generation

After defining the problem and framing the “How Might We” question, the team generated strategic possibilities.

For illustration purposes, I have limited the possibilities of the example to two. These possibilities contrast distinct approaches to structuring and operating the function, focusing on the degree of centralization and regional autonomy. Typically, teams may develop up to six possibilities and may contrast other aspects such as the level of relying on external professional service companies or how offensive or defensive the strategy should be [14].

Possibility ‘fully centralized data function’

This possibility envisioned a fully centralized governance and operational model for the data function. All major data, analytics, and AI activities, including data integration, governance, demand management, and solution delivery, would be managed by the central team. Regional units act as data consumers, relying on standardized reporting frameworks, dashboards, and analytics solutions provided uniformly by the central team to ensure consistency, scalability, and efficiency.

Possibility ‘federated data function with regional customization’

This possibility proposed a federated model that combines centralized governance with significant autonomy for regional units. Regional teams would play an active role in demand management, analytics development, and leveraging data science/AI for local initiatives, particularly in programs like digital manufacturing.

Choice Cascades

To detail both possibilities, the team completed the Data Strategy Choice Cascade for each. This helped the team to build a detailed joint understanding of each possibility.

Fleshing out the Must-Have Capabilities and Enabling Management Systems was for the design team already the first step to activate the data strategy as it was a kind of reality check for each possibility.

Step 4: What Would Have to Be True?

In order not to blow up the content of this section, I will only focus on possibility B for steps 4–6 of the Strategic Choice Structuring Process, as it will be sufficient to illustrate the concept.

The critical conditions were collected for customer, company and competition.

Recall the translations for company and competition we defined in Section 4.2 for this exercise.

Customers

1. Regional data customers value the ability to create their own data solutions to meet their country-specific needs
2. Regional data customers have the competencies to tailor analytics and AI solutions to their specific needs through locally available tools and experimentation.
3. Regional business units trust that centralized governance frameworks enable, rather than hinder, their ability to innovate and adapt to local needs.

Company

1. The central data team is able to provide effective governance frameworks and tools that empower regions to innovate while maintaining alignment with group-wide standards.
2. Regional teams successfully develop and share innovative use cases and best practices that are evaluated by local teams and adopted across other regions where sensible.
3. Resources are allocated effectively to balance support for both central governance and regional customization, without overburdening either side.
4. The company can implement and maintain the federated model in a cost-effective way, ensuring resource allocation supports both central and local capabilities.

Competition

1. The federated model outperforms do-it-yourself workarounds (e.g. with Excel) or external consultancies in delivering faster, more integrated, and cost-effective analytics and AI solutions.

Step 5: Barriers to Choice

From these critical conditions, the data strategy design team was least confident about two:

Customer-3: “Regional business units trust that centralized governance frameworks enable, rather than hinder, their ability to innovate and adapt to local needs.”

The reason for the low confidence was, that regional business units have historically relied on local data experts or external consultants for data solutions. It was unclear whether the data consumer would accept the new central authority.

Company-4: “The company can implement and maintain the federated model in a cost-effective way, ensuring resource allocation supports both central and local capabilities.”

The reason for the low confidence was, that the team had at the time not enough knowledge about the costs of both, data platform and tools as well as staff costs as the overall data operating model was not detailed to a sufficient extent.

Step 6: Testing & Transformation

To address these uncertainties, the data strategy design team developed a series of tests to validate the two critical conditions.

To address the identified barriers, the data strategy design team devised two focused tests:

Test 1 (trust in new data governance model)

The design team created a rough sketch of the future data governance operating model, that made the implications for local data consumers more tangible. The draft was shared with local key stakeholders and their feedback was incorporated within a next design iteration of the data governance operating model.

Test 2 (cost efficient implementation)

The design team created a first draft of the data operating model detailing the technological solution to estimate the future total cost of ownership for technology. They also detailed the newly required expert roles to estimate the future staff cost of the data function to be built.

These tests provided actionable insights into the assumptions and validated the possibility’s potential to succeed.

Step 7: Choice

In the context of this example, the data strategy design team decided to pursue the federated model, as it offered the highest likelihood of winning with the data customers by better balancing the need for regional innovation and customization while achieving the group’s centralized governance and alignment goals.

6.4 Steps IV-VI: Activating Strategy

Once possibility B was selected as the new data strategy, the next task was to activate it by addressing the implementation requirements. The steps IV to VI ensured that the strategic choices made during the design process were translated into operational realities, enabling the data function to deliver on its strategic demands. By activating the data strategy in alignment with the business strategy, the organization ensured that data capabilities directly support its competitive goals.

Step IV: Determining operational capabilities & systems

After the strategic capabilities and systems had been defined in Step III as part of the data strategy, the design team identified the operational capabilities and enabling management systems required to implement the federated model effectively.

Key operational capabilities included efficient report generation. These were complemented by operational systems such as BI and ETL tools as well as systems to monitor data quality.

Step V: Data Maturity Assessment

To assess readiness for the data strategy, the team conducted a comprehensive data maturity assessment for all capabilities and systems required to activate the strategy and across all regions.

They used a traffic light evaluation metric to assess current capabilities and systems in areas such as data modeling, reporting, and local experimentation. Capabilities and systems with “green” ratings were deemed ready to adopt the data strategy, while those flagged as “yellow” or “red” required targeted interventions in form of dedicated projects to build and maintain the new capabilities and systems.

The assessment also revealed regions where experimentation and self-service adoption were underdeveloped, providing a roadmap for prioritizing maturity-building efforts.

Step VI: A Plan to Build Data, Analytics and AI Capabilities

Based on the maturity assessment findings, the team developed a phased plans to build and sustain the required capabilities and systems with priority assigned to those elements of strategic nature.

Each region was assigned specific milestones tailored to its maturity level, with clear timelines and success metrics. To ensure continuous improvement, feedback loops were integrated into the plan, enabling the central team to refine tools and frameworks based on regional experiences and evolving needs.

7 CONCLUSION

This article aimed to demystify data strategy by addressing common misconceptions that prevent organizations from fully leveraging data, analytics, and AI.

Contrary to popular belief, a data strategy is not a plan for building data capabilities, a set of guidelines for managing data, or a supplementary element to business strategy. Instead, it is a functional strategy — the strategy for the data, analytics, and AI function, which provides data services to internal customers.

The data strategy is designed on the basis of the organization’s overarching business strategy. And it is business strategy, not data strategy, which defines how the organization leverages data to achieve competitive advantage.

By leveraging the Playing to Win framework, I demonstrated that data-related strategic choices must be embedded within the organization’s business strategy, forming the input for a robust data strategy. By integrating data use case innovation into the business strategy design process and deriving clear strategic demands, organizations ensure that their data strategy serves as a functional enabler, delivering capabilities that directly support competitive advantage.

Using the practical example of a salty snack food company, I illustrated how organizations can apply this approach to bridge the gap between theory and practice. The structured process not only aligns the data function with business needs but also allows data to become a key driver of competitive advantage.

Organizations that continue to treat data strategy as a standalone supplement or a list of technical initiatives will fail to realize its true potential. Instead, by weaving data-related strategic choices into the very fabric of their business strategy, they can build a cohesive, purpose-driven strategy architecture that enables them to win.

The promise of data, analytics, and AI lies not in their technical complexity but in their strategic and cultural alignment. By embedding data innovation into business strategy design, organizations can move beyond the data hype and leverage data as a true strategic asset.

I welcome your feedback, suggestions, and questions. Do you agree, disagree, or have additional thoughts? I look forward to engaging in discussions with you in the comments here on Medium.

REFERENCES

[1] Roger Martin, Playing to Win/ Practitioner Insights (2024), website with list of articles

[1a] Roger Martin, What Strategy Questions are You Asking? (2023), Medium article of the ‘Playing to Win Practitioner Insights’ series

[1b] Roger Martin, Overcoming the Pervasive Analytical Blunder of Strategists (2021), Medium article of the ‘Playing to Win Practitioner Insights’ series

[1c] Roger Martin, Strategy, Strategy Everywhere (2023), Medium article of the ‘Playing to Win Practitioner Insights’ series

[1d] Roger Martin, Strategy is Singular (2023), Medium article of the ‘Playing to Win Practitioner Insights’ series

[1e] Roger Martin, The Origins of Playing To Win (2023), Medium article of the ‘Playing to Win Practitioner Insights’ series

[1f] Roger Martin, (Playing to Win) x 5 (2024), Medium article of the ‘Playing to Win Practitioner Insights’ series

[1g] Roger Martin, Strategy is what you DO, not what you SAY (2020), Medium article of the ‘Playing to Win Practitioner Insights’ series

[1h] Roger Martin, The Best Strategy Icebreaker (2024), Medium article of the ‘Playing to Win Practitioner Insights’ series

[1i] Roger Martin, The Strategic Choice Structuring Process (2024), Medium article of the ‘Playing to Win Practitioner Insights’ series

[1j] Roger Martin, The Five Deadliest Strategy Myths, Medium article of the ‘Playing to Win Practitioner Insights’ series

[1k] Roger Martin, Why Planning Over Strategy? (2022), Medium article of the ‘Playing to Win Practitioner Insights’ series

[1l] Roger Martin, Strategy vs. Planning: Complements not Substitutes (2024), Medium article of the ‘Playing to Win Practitioner Insights’ series

[1m] Roger Martin, From Strategy to Planning (2021), Medium article of the ‘Playing to Win Practitioner Insights’ series

[1n] Roger Martin, Strategy as Problem-Solving (2020), Medium article of the ‘Playing to Win Practitioner Insights’ series

[1o] Roger Martin, Strategy & Design Thinking (2020), Medium article of the ‘Playing to Win Practitioner Insights’ series

[1p] Roger Martin, The Secret to Knitting Strategy Together Corporate-Wide (2021), Medium article of the ‘Playing to Win Practitioner Insights’ series

[1q] Roger Martin, Corporate vs. Business Unit Strategy (2022), Medium article of the ‘Playing to Win Practitioner Insights’ series

[1r] Roger Martin, Strategic Choice Chartering (2020), Medium article of the ‘Playing to Win Practitioner Insights’ series

[1s] Roger Martin, Understanding the True Building Blocks of Corporate Strategy (2022), Medium article of the ‘Playing to Win Practitioner Insights’ series

[1t] Roger Martin, Who Should Do Strategy? (2023), Medium article of the ‘Playing to Win Practitioner Insights’ series

[1u] Roger Martin, Overcoming the Integrative Strategy Challenge (2024), Medium article of the ‘Playing to Win Practitioner Insights’ series

[1v] Roger Martin, The Lost Art of Strategy (2021), Medium article of the ‘Playing to Win Practitioner Insights’ series

[1w] Roger Martin, Is or Is Not The Opposite Stupid on its Face? (2024), Medium article of the ‘Playing to Win Practitioner Insights’ series

[1x] Roger Martin, Distinguishing How-to-Win from Capabilities in Your Strategy Choice (2021), Medium article of the ‘Playing to Win Practitioner Insights’ series

[1y] Roger Martin, Can Your Strategy Pass Its Most Important Test? (2021), Medium article of the ‘Playing to Win Practitioner Insights’ series

[1z] Roger Martin, Untangling Value Proposition & Competitive Advantage (2022), Medium article of the ‘Playing to Win Practitioner Insights’ series

[2] Sebastian Wernicke, Data Inspired (2024), book in German language published by Vahlen

[3] Caroline Carruthers and Peter Jackson, Data Driven Business Transformation (2019), book published by Wiley

[4] Quote Investigator, Quote Origin: It’s Difficult to Make Predictions, Especially About the Future, website accessed on Jan. 4th 2025

[5] Jens Linden, The Root Cause of Why Organizations Fail With Data & AI (2024), Medium article published in Toward Data Science

[6] James Martin, Design and strategy for distributed data processing (1981), book published by Prentice-Hall

[7] Google Trends, Worldwide interest of the term ‘data strategy’ (2024), website

[8] Roger Martin, A Plan Is Not a Strategy (2022), video

[9] AWS, What is a data strategy? (2024), website accessed on Dec. 22nd 2024

[10] SAP, What is a data strategy? (2024), website article accessed on Dec. 22nd 2024

[11] Wikipedia, The LEGO Movie (2014), WIKI entry

[12] Roger Martin’s website (2024)

[13] A. G. Lafley and Roger L. Martin, Playing to Win (2013), book published by Harvard Business Review Press

[14] Michael Goitein, How a leading strategy expert combined Design Thinking and scientific inquiry to allow anyone to create winning strategies, blog entry accessed on Dec. 25th 2024

[15] Roger Martin, Jennifer Riel, The One Thing You Need to Know About Managing Functions (2019), article published in Harvard Business Review

[16] Jennifer Riel, How to Make Your Strategy Real (2024), IDEOU Blog entry

[17] Jens Linden, The Data Strategy Choice Cascade (2024), Medium article published in Towards Data Science

[18] Alex Osterwalder, Achieve product-market fit with our brand-new Value Proposition Canvas (2012), website accessed on Dec. 30th 2024

[19] Jens Linden, Unser Datenreifegrad ist „Reactive“ oder“Managed“ — und nu?! (2023), LinkedIn blog entry in German language

[20] Andrew Campbell, Mikel Gutierrez, Mark Lancelott, Operating Model Canvas (2017), Book published by Van Haren

[21] Jens Linden, Data & AI Operating Model Canvas (2023), LinkedIn blog entry in German language

[22] Datentreiber, The Datentreiber Method: Data & AI Business Design (2024), website accessed on Dec. 30th 2024

[23] Wikipedia, Data Thinking (2024), website entry on accessed on Dec. 30th 2024

[24] Leandro DalleMule, Thomas H. Davenport What’s Your Data Strategy? (2017), article published in Harvard Business Review

Unless otherwise noted, all images are by the author.

All Lego illustrations have been created with MECABRICKS.

I used ChatGPT 4o as sparring partner, to generate some of the example ideas.

How Most Organizations Get Data Strategy Wrong — and How to Fix It was originally published in TDS Archive on Medium, where people are continuing the conversation by highlighting and responding to this story.

DEMYSTIFY DATA STRATEGY

A guide to be successful with the strategic groundwork required

Business value through Data and Artificial Intelligence. Everybody talks about it, yet most companies are struggling to monetize their data. I claim that in most cases, this is due to a lack of effective business strategy. This article shows what strategic groundwork is necessary to activate your company’s data assets.

This is part two of a series of articles, in which I demystify data strategy — an essential component for any organization striving to become data-driven in order to stay competitive in today’s digital world.

I am Jens, a business-minded data expert with nearly 2 decades of practical experience in implementing data & AI use cases. I advise leaders from various industries on designing strategies & cultivating a data culture to leverage data, analytics & AI.

tl;dr

• The lack of real business strategy is the root cause why so many organizations fail with their data, analytics and AI ambitions and struggle to become data-driven.
• Therefore, organizations should first invest in the design of their business strategies.
• After that, data-related choices should be charted throughout the entire organization.

Table of Contents

1. What is Going Wrong With Data?
1.1 Symptoms of Dysfunctional Data Value Creation
1.2 The Root Cause
1.3 How Big is The Problem?
2. Playing To Win
2.1 Its Origins
2.2 Strategy Is About Making Choices
2.3 The Strategy Choice Cascade
2.4 The Cheat Sheet
2.5 Making Integrated Choices
2.6 Chartering Choices
2.7 Bringing Strategy to Life
3. Data Business Needs
3.1 No Clear Data Needs
3.2 Data as Operational Duty
3.3 Data as Strategic Differentiator
3.4 Data as Business
3.5 Why Does it Matter to Know Your Data Business Needs?
4 When Is a Dedicated Data or AI Strategy Needed?
4.1 The Data Strategy Mystery
4.2 Strategy For a Data or AI Functions
4.3 A Plan to Build Data Capabilities
5 Putting It All Together
5.1 A Process to Lay The Strategic Groundwork for Data
5.2 An Example for Chartering Data-Related Choices
6 Conclusion
References

1. What is Going Wrong With Data?

Organizations big or small, public or private, all strive to become data-driven. Being data-driven means that data, which is created by internal operational processes is ‘recycled’ to support human decision making, to automate decisions and to enable digital business models.

In general, leveraging data can lead to:

• More revenue
• Less costs, more efficiency and margin
• Better risk management
• More innovation and the possibilities of novel business models based on digital products and services

For more details on the motivation and context of data-driven organizations, see part 1 of this series of articles [1].

The data consulting industry, numerous data experts (including myself) and technology vendors never tire of repeating the compelling reasons, why organizational leaders should invest in their data capabilities including skills, competencies, methodology, culture and — of course — corresponding IT tools and platforms. A quote translated from [2] brings it to the point:

“Consistent value creation with data is already a decisive competitive advantage for companies today, and in the near future it will even be essential for survival.” [2]

The topic has gained attention in recent years due to the quite impressive technical advances and the resulting medial hype around Generative Artificial Intelligence (GenAI), which has arrived in the mainstream media and — at least for me personally —is even part of many private conversations with family and friends.

Despite the current GenAI hype, the theory and the practice of data value creation are anything but new. It has appeared under different names in the past, such as Analytics, Business Intelligence, Statistical Modeling, Data Science, Machine Learning, Big Data and is now mainly called Artificial Intelligence. All these terms describe more or less different techniques to extract insights out of data, which — when done correctly — allows to improve business demonstrably.

Being a mathematician, I spent many years in the role of a Data Scientist applying statistical methods to detect patterns, which could be used by businesses to increase understanding of consumers, markets or competition, which in turn could be leveraged to increase revenue or to reduce costs or risks. There exist many practice-proven and well-understood use cases for data value creation in every industry and every point of the value chain.

Whilst there are clear applications and benefits of ‘recycling’ data to create additional business value and even to transform existing business models, I also observe a very different picture of business reality:

Many organizations just struggle to effectively and efficiently leverage data to create business benefits.

What is going wrong with data in organizations?

1.1 Symptoms of Dysfunctional Data Value Creation

The list of problems organizations have regarding data can be long [13] and each organization is certainly different. However, it often comes down to some typical suspects:

1. Unclear responsibilities for the orchestration and development of data solutions as well as supporting activities, such as data governance or data architecture.
2. Unsuccessful data, analytics or AI initiatives: Analytics and AI projects take long and are expensive, or are initiated without a strong connection to specific business objectives, leading to projects that do not add measurable value.
3. Lack of data awareness: The potential and benefits for leveraging data as an asset are unclear to employees and executives. The current and future role of data is simply not understood and opportunities are missed out. This often goes together with a lack of top-management attention.
4. Silos of two kinds exist: organizational silos prevent collaboration, innovation and effectiveness, and data silos prevent people from accessing, combining and leveraging data from different parts of the organization, resulting in fragmented views and redundant or contradictory results.
5. Lack of skilled personnel: Lack of data-affine business users or data experts in the organization.
6. Poor data quality: Teams frequently report issues with data accuracy, completeness, or consistency, leading to a lack of trust in insights and decisions based on that data.

These are are symptoms many organizations know all too well. But what is the root cause of all this pain?

1.2 The Root Cause

There was and there still is the persistent misconception that data value creation is a technical challenge. If we just introduce the right platform and provide the required training, the organization will be able to effectively and efficiently use data to create business value. But this is a fallacy.

IT platforms, tools and algorithmic libraries have been around for a while, have matured, are ready to use and continue to improve. And whilst the technical extraction and preparation of data should be done ‘properly’, in order to achieve high quality data as well as scalable and flexible data models, there exist proven frameworks and best-practices to achieve exactly that.

To choose and implement IT platforms, tools, best practices and algorithmic libraries for data is a complicated, but not complex problem, which is actually solved.

Organizations either have the expertise to select and implement technology in-house or can easily get solid external expert consulting on the matter.

I claim, that the root cause for dysfunctional data value creation is a lack of coherent business strategy:

The lack of business strategy is the root cause why so many organizations fail with data, analytics and AI.

If the business strategy is not explicitly defined, organizations struggle to build and maintain the data capabilities they require to effectively and efficiently discover, innovate, build and maintain data-driven solutions.

For example, one core data capability required to be successful with data value generation is (data) culture [3, 4], which ensures that employees:

1. Understand what is possible with data
2. Want to use data to improve business
3. Can use data to do so
4. Must use data, because it is expected from their leaders

However, in order to create an effective data culture, you need to know the direction your target culture should head to. You have to know the data business needs, which guide you in which direction you need to develop your target data culture. And these data business needs are provided by strategy.

Without a clear business strategy, it’s hard to agree on what the business needs for data, analytics, and AI are in the organization. The role of data value creation is then unclear. However, in order to build tailored and sustainable data capabilities, you simply need to understand if the main purpose of data in your organization is, for example, to provide a set of reports to the CFO or if data is a means to drive your competitive advantage, say through digital products and services.

By making implicit choices explicit through thoughtful strategy design, you build a form of communication to create a consensus about your data business needs.

Getting your data & AI capabilities right is surely not the sole benefit of crafting a solid strategy for your organization. It is rather a side effect. This should be self-evident, but for the sake of completeness, the benefits of having an explicitly and well-designed business strategy are for example:

1. Competitive edge
2. Clear direction and goals
3. Focus and effective resource allocation
4. Employee alignment and morale
5. Long-term success and sustainability
6. Effective risk management

1.3 How Big is The Problem?

When I start an engagement with a new client to help with data-related problems, one of the first information requests I usually carry out is to ask for relevant strategy documentation, most importantly the (corporate) business strategy. I often receive vague responses like:

• “Our strategy is not documented, yet.”
• “Our strategy is outdated and needs an update.”
• “I am not sure, I have to do some research.”
• “We have a kind of action-oriented strategy.”

In cases when a strategy documentation is provided, it often turns out to be a loose list of initiatives, which all sound good, but overall does not meet common strategy standards [5a].

While I am not aware of representative statistics on the matter, and the presence of a good business strategy might certainly depend on the size of the organization and potentially other factors, such as industry, my personal impression of the German speaking market, the playing field in which I am most active, is that many companies are lacking a solid, explicit, well designed strategy.

Very few organizations pursue a solid, explicit business strategy, from which the required data, analytics and AI capabilities can be deduced.

In my experience, things look particularly challenging with small and mid-sized enterprises, the “Mittelstand”, which is the backbone of the German economy for which around 56% of employees work [6].

With this article, I aim to address the root cause of organizational data failures by clarifying what strategic work is necessary to leverage data as an asset.

To get started, let’s have a close look at what strategy actually is.

2. Playing To Win

2.1 Its Origins

Playing to Win is a strategy framework originated by Roger Martin [7], named world’s #1 management thinker in 2017, trusted advisor to CEOs of companies around the world, former Monitor consultant and former Professor of the Rotman School of Management.

The framework was developed and continuously refined in the 1980s and 1990s [5b] and culminated in a joint book [8] by Roger Martin and A. G. Lafley, who is a former CEO of P&G. The book was published in 2013.

The framework became the standard strategy methodology at P&G and has successfully been applied in many industries since. In addition, the Playing to Win framework has constantly been detailed by Roger Martin through a series of Strategy Practitioner Insights [5, 5c].

Whilst there might be other well-suited strategy frameworks out there, I choose the Playing to Win approach above others, since it is widely known and applied and comes with an entire ecosystem of processes, templates, resources and trainings that can be leveraged for designing any kind of strategy.

2.2 Strategy Is About Making Choices

In the Playing to Win framework, strategy is defined as follows [8]:

“Strategy is an integrated set of choices that uniquely positions a firm in its industry so as to create sustainable advantage and superior value relative to the competition.”

So strategy is about making (often tough) choices that give your firm a competitive edge. There is, however, no bullet-proof certainty that these choices will turn out to be the right ones. The result is always a strategic bet. The strategy design process aims to shorten your odds, i.e. to maximize the probability of success, given the information available.

Note that this definition of strategy is quite different to a plan, which is often misunderstood as strategy [9, 5d].

“Planning is the act of laying out projects with timelines, deliverables, budgets, and responsibilities.” [5d]

However, a strategy and a plan are not exclusive. Crafting plans is a natural part of the strategy design process, in order to build the capabilities required to bring the strategy to life (cf. Section 2.7 below).

There is another definition of strategy, which I particularly like, namely: “Strategy is the logic that determines what you choose to do and not do in service of a particular goal” [10].

Consequently, every organization has a strategy, whether it’s written down or not. Such implicit strategy can be deduced from the actions a company takes. The problem with such implicit strategies is, that there might not be a consensus in the organization what this implicit strategy is and that actions may be ineffective.

2.3 The Strategy Choice Cascade

Within the Playing to Win framework, there are five key choices every strategy designer must make. These choices are structured in the so called Strategy Choice Cascade, which is one of the core tools of the framework.

The actual heart of the strategy is made of the coherent choices made in boxes two and three: Where to Play & How to Win.

How the cascade is applied is best illustrated using a real-world example, which is taken from [9] and describes choices for the corporate strategy of Southwest Airlines.

The cascade is not filled out just once; it is revisited and utilized repeatedly for various use cases throughout the strategy design process:

• Reconstruct the strategy of your competitors by observing what they do: This is a nice practice to familiarize yourself with the framework and is a kind of icebreaker [5e] to get started with the strategy design process.
• Document your current strategy: This is a good way to build a common understanding of the status-quo and make your current implicit choices explicit, if this has not done been before.
• Document new strategic possibilities: The process for designing a strategy [5f] contains a creative element, where many possible scenarios for the future target strategy are generated and systematically evaluated. These are documented and detailed using the strategy choice cascade, one for each possibility.

2.4 The Cheat Sheet

When I work with the cascade, my Playing to Win Cheat Sheet assists me, which contains some further information on the individual boxes together with the main sources for the framework.

2.5 Making Integrated Choices

When working on the strategy cascade, the boxes should never be filled individually [5g], but need to be considered together as the choices of a real strategy should fit together nicely and reinforce each other.

This applies particularly for the Where to Play and How to Win, which are inseparable pairs and always need to be considered together [5h].

But also when considering the Must-Have Capabilities and Management Systems, you need to keep your Where to Play and How to Win in mind. Capabilities and systems are a kind of a reality check: if you cannot build the capabilities and systems required to bring your strategy alive, you need to think of a different way to win and sometimes even of a different playing field.

From this it should be clear, that the exercise of filling the strategy choice cascade can be a quite iterative process moving back and forth until you arrive at a set of choices with a convincing theory of winning for the chosen playing field enabling the winning aspiration, which can realistically be achieved with the required capabilities, which are built & maintained with the needed management systems.

2.6 Chartering Choices

Strategic choices occur throughout the organization and not all choices are made at the top (corporate) level.

Choice Chartering [5i] is the structured process of defining, communicating, and delegating strategic choices throughout an organization to ensure aligned and effective decision-making at all levels.

Consequently, in large organizations there exist nested strategies and corresponding choice cascades, e.g. for a country, category, brand, or business unit strategies.

Not only individual business units need to make their own strategic choices and thus formulate nested choice cascades, but also functions [10], such as IT, HR, Finance, Marketing, R&D or Data & AI.

One particularity with functional strategies is, that they can be treated as natural monopolies [5j], where (internal) customers usually have no other choice than using the offering provided from that function.

2.7 Bringing Strategy to Life

A good strategy design process does not distinguish between the creation and subsequent execution phase, but builds activation into the strategy process from the very first beginning [11].

The goal of strategy activation is that everyone understands the design choices made and stakeholders are enabled to make their own choices to support the strategy.

Strategy activation includes the creation of stakeholder engagement, to make the strategy tangible and to move from design into action by building the Must-Have Capabilities and corresponding Enabling Management systems.

Once the capabilities and systems are defined, they need to be built and maintained. A fit-gap-analysis — often called maturity assessment — for each capability or system then provides the input to create plans for building, buying or borrowing the capabilities and systems needed.

Interestingly, (data) consultancies often offer to start with maturity assessments as a kind of foot-in-the-door offering for clients. From the above mentioned, it should however be clear, that any fit-gap activities only make sense after the groundwork of strategic work has been finalized.

Bringing strategy to life also provides the link to planning, which is often confused with strategy: strategic choices typically require new capabilities to be built, for which actions plans are required, once the status-quo has been evaluated.

3. Data Business Needs

Once you have made your business strategy explicit, it should be clear what role data, analytics and AI play for your organization, i.e. what your (corporate) data business needs are.

It is as simple as that:

Business strategy must provide an answer to how relevant data for your organization or business unit will be.

When using the Playing to Win framework, one glance at the choice cascade of your business strategy should clearly indicate the relevance of data for your business.

The data business needs can take one of four different forms.

Let’s deep-dive into each of these four scenarios.

3.1 No Clear Data Needs

In this scenario, there is no consensus in the organization if data value creation is at all necessary to win. That means, data seems to be irrelevant for any of the five boxes of the strategy choice cascade.

Example

An example might be a small traditional crafts business focused on handmade goods, which relies on artisanal skills, reputation, and customer relationships.

Recommendations

While I believe that data, analytics and AI are not the center of the universe and not every organization depends on it to win, as a potentially biased data professional, I would naturally challenge such a viewpoint by asking:

• What current business pains & gains could be addressed with the help of data analytics & AI?
• What role can data, analytics & AI play for innovation?
• What solutions are already leveraged by competitors, or companies in your own or other industries?
• Are the potential benefits worth it to invest more energy and resources in this?

The organization should gain clarity about potential data use cases and how relevant these are for the business.

3.2 Data as Operational Duty

In the second scenario, data, analytics and AI are operational, but not strategic capabilities and/or systems, and should therefore be realized as cost-efficient as possible. Relevant data elements can be found within in box four and five of the cascade, namely Must-Have Capabilities and Enabling Management Systems.

Example 1

For the case of Southwest Airlines above, the business need for data, analytics and AI is to manage the complexity of airline operations efficiently and cost effectively.

The company needs to build, run and maintain Enabling Management Systems to ensure operational efficiency [12]:

• A performance management system to track aircraft on-time performance in real-time
• A system to monitor and optimize gate waiting times
• A system to minimize fuel costs
• A decision support system for the planning of new stations and routes

This means for the Southwest example, the business needs for data, analytics and AI are mainly found in the last box — Enabling Management Systems.

Example 2

A regional utility company uses data, analytics and AI to monitor energy usage, predict maintenance needs, and generate internal reports. Like in Example 1, data helps to achieve operational efficiency and to meet compliance requirements, but does not provide a strategic differentiation.

Hence, data, analytics and AI are capabilities (box 4) or systems (box 5) representing an operational imperative rather than a strategic capability.

Examples for data related enabling management systems could be data reporting systems needed to provide management insights, BI tools or a corresponding data platform.

Recommendations

When building and maintaining data related capabilities and systems of the business strategy for this scenario, cost-efficiency should be a key dimension when determining whether to build, borrow or buy the capabilities and systems needed to win in the specified playing field.

Whilst there is nothing wrong with using data solely for operational efficiency, organizations should regularly assess whether strategic opportunities are being overlooked. Naturally, as a data professional I would challenge this by asking:

• What role can data, analytics & AI play for innovation?
• Can current products and services be extended with data-based insights leading to added value for the customers and/or differentiation from competition?

Again, organizations should aim for achieving clarity about potential data use cases and how strategically relevant these are for the current business model or if the existing business model could be extended using data, analytics and AI.

3.3 Data as Strategic Differentiator

In this scenario, data, analytics, and AI serve as core differentiators, directly influencing how the company wins in the market. They are a key part of realizing the company’s value proposition, helping the firm to create a competitive edge (box 3: How to Win) or are key strategic capabilities (box 4: Must-Have Capabilities) to realize the theory of competitive advantage.

Example

This example, which is borrowed from [5k], is the one of Frito Lay, a salty snack producer (Lays, Doritos, …), which uses a direct-store-delivery system, where the product is directly delivered to convenience stores and placed on the shelf by the delivery driver.

This direct-store-delivery system is labor-intensive and hence expensive, but it differentiates Frito Lay from its competition. Having a large number of well-known products creates a massive cost advantage for Frito Lay’s delivery system (one stop for many products) producing a unique competitive advantage, that cannot be matched by competitors.

Building data and AI solutions, that predict store inventory and generate the optimal product orders for each point-of-sale, helps to reduce costs for the direct-store-delivery system. Hence, data, analytics and AI are strategic capabilities (box 4: Must-Have Capabilities), which directly support the competitive advantage (box 3: How to Win).

Recommendations

Use the business strategy cascade to communicate the strategic importance of data in your organization. Make sure that executives and all relevant stakeholders understand the impact and value of data, analytics and AI for your business. This lays the foundation for necessary data culture and data literacy developments and the organizational changes required to fully build the capabilities needed to win.

Finally, it also helps in discussion regarding funding as well as to set the right priorities.

Critically question whether it makes sense to outsource any of the data capabilities, as they are of strategic importance for the organization.

Push your thinking, consider whether there are additional opportunities for data monetization [16], potentially leading to ‘Data as Business’.

3.4 Data as Business

This is the scenario, which most people get excited about: data extends or even radically changes the current business model and organizations enter the digital economy.

In this case, digital data-driven products or services are part of the company’s offering (box 2: Where to Play) and can even be part of the vision (box 1: Winning Aspiration).

Example

This example is also borrowed from [5k] and is about Westlaw, a digital provider of legal search services. One of Westlaw’s strategic assets is a database of notes about court cases dating back to 1975, which allows lawyers to browse historical cases for their legal matters at hand.

The data is part of the firm’s offer (box 2: Where to Play) and its uniqueness in the market secures its competitive advantage (box 3: How to Win).

Recommendations

As data is clearly a company asset that secures a competitive advantage, make sure the data is treated accordingly. For example, it needs caring, nurturing, protection and quality assurance.

Hence data governance (box 4: Must-Have Capabilities) and corresponding systems (box 5: Enabling Management Systems) are an imperative.

3.5 Why Does it Matter to Know Your Data Business Needs?

Each organization is different and has unique data business needs.

Your organization needs to be aware what role data, analytics and AI plays in the business strategy, in order to know how much resources to spend for creating and maintaining the data-related capabilities and systems.

Many executives still struggle with the hype around data and AI leading to skepticism, uncertainty or to thoughtless investments into it.

Consequently, a solid business strategy and a thorough understanding of the data business needs provide guidance and helps to communicate the importance and role of data in your organization.

Therefore, I claim:

The first step to become data-driven is solid business strategy design.

4 When Is a Dedicated Data or AI Strategy Needed?

4.1 The Data Strategy Mystery

If you already think there is confusion around the term strategy, let me tell you it is even worse with the term data strategy. Nearly each article or book I read defines it somewhat differently — if it defines it at all. This gave me frequent headaches when I started to deep-dive into the topic.

In most cases the term ‘data strategy’ is used to refer to a plan for building data-related capabilities within an organization.

It is referred to as a plan for building analytics IT platforms, to roll-out new BI-tools, to build data governance, to increase data literacy, to develop data culture or to build an operating model for data management, analytics and AI.

In my opinion, the term data strategy should only be used to describe the strategy of a function, which provides data services to foremost internal, but sometimes also external data customers.

4.2 Strategy For a Data or AI Function

In many cases organizations will need a data related function, delivering services such as:

• Providing integrated, modeled and quality assured analytics-ready data for business self-service users (e.g. in form of data marts, which are browsed via data catalogues)
• Developing and maintaining BI dashboards and reports
• Conducting analyses for business users, which require specialist knowledge, such as data science or machine learning
• Providing consulting regarding the innovation, discovery and evaluation of data use cases or consulting services related to buy vs. make decisions (e.g. for GenAI solutions)
• Creating and performing teaching and training for data, analytics or AI
• Providing continuous information requests to internal or external stakeholders, such as data exchange with suppliers or satisfying regulatory data demands

If an organization has or will have a function for data management, analytics or AI, which provides services to (internal) data customers, it simply requires a strategy as outlined in Section 2.6 above.

How the strategy choice cascade is applied to design such a data strategy, was topic of part 1 of this series [1].

A clear strategy allows the function to gain clarity, where to play and how to win with its data customers. It also guides and aligns its actions, to effectively allocate resources and to enhance the value provided to the organization.

If your data function does not have an explicit strategy, it is likely to default to a damaging implicit strategy, which is either the ‘servile strategy’ (trying to satisfy all possible business demands) or the ‘imperial strategy’ (doing data, analytics or AI just for the sake of it) [10].

Depending on the nature and focus of the services and products provided in this function, you may call it data strategy, analytics strategy, BI strategy or AI strategy.

The data strategy of the function usually contains capabilities and systems that span across or exist in multiple parts of the organization, such as a corporate analytics data platform. In strategy language, such capabilities and systems are referred to as a reinforcing rods [8, 5l].

4.3 A Plan to Build Data Capabilities

The plan to build data related capabilities and systems is the topic most companies are enthusiastic when it comes to data, analytics or AI.

To create such a plan can be a challenging endeavor, so it is well invested time. All I am saying is that you need to make sure, that the strategic groundwork has been done beforehand, so you know what kind of capabilities and systems you actually need to win. The nature of the plan and the requirements for many design choices depend on that strategic groundwork.

The scope of such ‘data plans’ can vary widely depending on the organization’s data business needs: from building a reporting system and corresponding data landscape exclusively for controlling purposes to enabling many business units and functions to extensively leverage potentials from data use cases along the entire value chain.

The latter often goes together with a company-wide transformation program, and the plans for building data capabilities can be part of an overarching data-driven business transformation roadmap, led by a dedicated leader for data, analytics or AI [14, 15] – sometimes called Chief Data Officer.

5 Putting It All Together

A plan to build data or AI capabilities and systems should come last, as the result of a fit-gap-analysis, in which the target state is provided by the preceding strategic choices.

Therefore, organizations require clarity about their data business needs, i.e. what role data, analytics and AI will play in their future business. The bottom line: You need to start with the strategic groundwork.

5.1 A Process to Lay The Strategic Groundwork for Data

A process to approach the design of effective data and AI capabilities, could look as follows:

1. (Re-) Design your business strategy

• Identify existing strategic choices, whether implicit or explicit
• Identify, what data business needs your organization or business unit has and make sure that the related strategic choices regarding data, analytics and AI are clearly formulated within your business strategy
• Ensure that executives and relevant stakeholders are aligned on the future role of data, analytics and AI in your organization

2. Charter relevant choices

• Charter strategic choices for a data function and design your data strategy for the function
• Charter other related data choices for relevant business units and/or functions

3. Assess & plan capabilities and systems

• This applies for all levels of strategy within the organization
• Perform a maturity assessment of each data related capability and system required to bring your strategy to life
• Create a plan for building, extending or maintaining each capability and system
• For your data strategy this may include: organizational design, a data target operating model, technology and tools, data management, data integration, data modeling, data architecture, data governance, data democratization, data quality management, master data management, data innovation management, BI, data science, data literacy and data culture

4. Build capabilities and systems

• Conduct the projects to build and maintain your capabilities and systems required to win with data
• Measure the success of your strategy and monitor progress

By laying the strategic foundation, you will address the root cause of dysfunctional data value creation in your organization.

Laying the strategic foundation ensures that executives and employees will understand the role of data within your organization. This, in turn, will help to build effective and sustainable data capabilities and to provide guidelines on the necessary resources to invest in developing and maintaining these capabilities.

For step 1, we already saw examples in Section 3 if this article. How might the choice chartering within step 2 look like?

5.2 An Example for Chartering Data-Related Choices

Let’s apply step two of the process to the above example of Frito Lay, where the store inventory forecasting supports the competitive advantage of the direct-store-delivery system.

This use case for store inventory prediction for the sales department may be one of many AI use cases for Frito Lay, so that an AI capability is required in many parts of the organization. Let’s say Frito Lay therefore decides to build a central AI capability leading to a dedicated AI function.

The AI function leader is then tasked with designing an AI strategy that supports the corporate strategy and directly contributes to Frito Lay’s How to Win. One Where To Play in the AI strategy would be to provide forecasting solutions for the sales department.

To ensure the AI forecasting solution has an impact, it would also be necessary to charter data-related choices to the sales leader, such that sales commits to use the AI-driven insights within its planning process. This means that ‘AI-driven sales planning’ becomes a Must-Have Capability of the sales strategy.

6 Conclusion

The relevance of good business strategy for successful data value creation appears to be underestimated and undervalued. Companies must have a well-defined, explicit business strategy, from which any data, analytics and AI demands are apparent. This strategic foundation is the starting point for chartering data-related choices, whether in dedicated data management, analytics, or AI functions, and for building the necessary capabilities across business units.

For your organization to become data-driven, ensure you have a solid strategic foundation. Otherwise, you will likely run into troubles and will experience the symptoms of dysfunctional data value creation, which we all know too well.

Looking ahead, the competitive landscape is evolving rapidly. The most successful organizations of the future will be those that recognize data as a strategic asset, not just a byproduct of their operations. As AI technologies mature and integrate further into business processes, companies with a coherent strategy will be poised to capitalize on these advancements, creating new revenue streams, driving innovation, and maintaining a sustainable edge over their competition. In the coming years, we will see a growing divide between companies that have laid the strategic groundwork for data and AI and those that continue with business as usual. The former will thrive, while the latter may struggle to survive in an increasingly data-driven world.

The time to act is now: prioritize your strategy (re-) design, align on your data business needs, and build the capabilities needed to win with data. Only then can you truly harness the transformative power of data, analytics and AI for long-term success.

References

[1] Jens Linden, The Data Strategy Choice Cascade (2024), Medium article published in Towards Data Science

[2] Sebastian Wernicke, Data Inspired (2024), book in German language published by Vahlen

[3] Carsten Bange, Data Culture: Definition, Herausforderungen & Maßnahmen (2022), article

[4] Jens Linden, Datenkultur — Was & Warum? (2024), LinkedIn post

[5] Roger Martin, Playing to Win/ Practitioner Insights (2024), website with list of articles

[5a] Roger Martin, From Laudable List to How to Really Win (2020), Medium article of the ‘Playing to Win Practitioner Insights’ series

[5b] Roger Martin, The Origins of Playing To Win (2023), Medium article of the ‘Playing to Win Practitioner Insights’ series

[5c] Roger Martin, (Playing to Win) x 5 (2024), Medium article of the ‘Playing to Win Practitioner Insights’ series

[5d] Roger Martin, Strategy vs. Planning: Complements not Substitutes (2024), Medium article of the ‘Playing to Win Practitioner Insights’ series

[5e] Roger Martin, The Best Strategy Icebreaker (2024), Medium article of the ‘Playing to Win Practitioner Insights’ series

[5f] Roger Martin, The Strategic Choice Structuring Process, Medium article of the ‘Playing to Win Practitioner Insights’ series

[5g] Roger Martin, Overcoming the Integrative Strategy Challenge (2024), Medium article of the ‘Playing to Win Practitioner Insights’ series

[5h] Roger Martin, On the Inseparability of Where-to-Play and How-to-Win (2020), Medium article of the ‘Playing to Win Practitioner Insights’ series

[5i] Roger Martin, Strategic Choice Chartering (2024), Medium article of the ‘Playing to Win Practitioner Insights’ series

[5j] Roger Martin, Strategy for Natural Monopolies (2024), Medium article of the ‘Playing to Win Practitioner Insights’ series

[5k] Roger Martin, Distinguishing How-to-Win from Capabilities in Your Strategy Choice (2021), Medium article of the ‘Playing to Win Practitioner Insights’ series

[5l] Roger Martin, Corporate vs. Business Unit Strategy (2022), Medium article of the ‘Playing to Win Practitioner Insights’ series

[6] statista, Mittelstand als Rückgrat der Wirtschaft (2024), website in German language

[7] Roger Martin’s website (2024)

[8] A. G. Lafley and Roger L. Martin, Playing to Win (2013), book published by Harvard Business Review Press

[9] Roger Martin, A Plan Is Not a Strategy (2022), video

[10] Roger Martin, Jennifer Riel, The One Thing You Need to Know About Managing Functions (2019), article published in Harvard Business Review

[11] IDEO U, Activating Strategy (2024), website

[12] Tableau, Southwest Airlines maintains on-time flights and optimizes fleet performance with Tableau (2024), website

[13] Jens Linden, Data First Aid Kit (2022), LinkedIn Post

[14] Caroline Carruthers and Peter Jackson, The Chief Data Officer’s Playbook 2nd edition (2020), book published by Facet Publishing

[15] Caroline Carruthers and Peter Jackson, Data-Driven Business Transformation (2019), book published by Wiley

[16] Jens Linden, Datenmonetarisierung — Das ‘Where to Play’ für Datenstrategien (2024), LinkedIn Post

Unless otherwise noted, all images are by the author.

The Root Cause of Why Organizations Fail With Data & AI was originally published in TDS Archive on Medium, where people are continuing the conversation by highlighting and responding to this story.

DEMYSTIFY DATA STRATEGY

What your data strategy should look like

This is part one of a series of articles, in which we will demystify data strategy — an essential component for any organization striving to become data-driven in order to stay competitive in today’s digital world. Use the cheat sheet provided in this article to kick-start your own data strategy development!

I am Jens, a business-minded data expert with two decades of experience in implementing data and AI use cases. I help leaders across industries design strategies and foster cultures that unlock the full potential of data and algorithms.

tl;dr

• When organizations strive to become data-driven, they need to transform, which requires a solid data strategy design
• The renowned Playing to Win strategy framework provides a process to develop, and a format to document and communicate any kind of strategy
• This article shows how the strategy choice cascade for corporate strategy design can be adopted to develop a data strategy
• The cheat sheet provided allows data strategy design teams to rapidly apply a practice-proven framework for their own data strategy development thus helping organizations to become data-driven

Table of Contents

· 1. Motivation & Context
∘ 1.1 The Data-Driven Company
∘ 1.2 Where is the Challenge?
· 2. Is Data Not Already Handled Within the IT Strategy?
· 3. What Strategy Framework to Use for Designing a Data Strategy?
∘ 3.1 The Playing to Win Framework
∘ 3.2 Tool 1: The Strategy Process Map
∘ 3.3 Tool 2: The Strategy Choice Cascade
∘ 3.4 The Playing to Win Framework for Data Strategy Design
· 4. Link Between Data & Business Strategy
· 5. The Data Strategy Choice Cascade
∘ 5.1 Translating the Choice Cascade to Data & AI
∘ 5.2 The Data Strategy Choice Cascade
∘ 5.3 The Cheat Sheet for the Data Strategy Choice Cascade
∘ 5.4 When to Use the Data Strategy Choice Cascade
· 6. Two Example Data Strategies
· 7. Conclusions & Outlook
· References

1. Motivation & Context

1.1 The Data-Driven Company

Companies big or small, and independent of which industry they are playing in, strive to become data-driven. Regardless of what technology you use or how you name it — Data Science, Business Intelligence (BI), Advanced Analytics, Big Data, Machine Learning or (Generative) Artificial Intelligence (AI) — being data-driven is about leveraging data as a strategic company asset.

The big promises of being data-driven are:

1. Humans make better decisions: Managers and employees on all organizational levels and across the entire value chain are enabled to consistently make better decisions by combining data & analyses with human common sense.
2. Computers make automated decisions: Frequent and repeated decisions in operations can be automated.

This, in turn, leads to a long-term competitive edge through:

• More revenue
• Less costs, more efficiency and margin
• Better risk management
• More innovation and the possibilities of novel business models based on digital products and services

These benefits are realized through individual data uses cases. Some concrete examples are:

• Targeted customer retention for B2C business: Customer data can be enriched with demographic or market data and is then used to build statistical models (you may call it Machine Learning or AI if you like), to learn rules, which can predict the probability of customer churn. This means, that for each single customer a probability between 0% and 100% is computed, indicating if a customer is likely to terminate his/her contract next month. In combination with a customer (lifetime) value, which also can be deduced from data, the customer service center can leverage this information to make informed decisions about which precise customers to phone up, in order to prevent losing “good” customers. For companies with a large customer base and limited service center resources, this often leads to a significant reduction in churn and hence a long-term measurable increase in revenue.
• End-to-end product margin analysis: Financial and product data, together with clear cost-allocation rules, can be used to develop a BI dashboard to analyze the margin of individual products. Dashboard users can surf the data and drill-down in various dimensions, such as country, point-of-sales, time or customer type. This allows business users to make informed decisions about which products might be removed from the existing portfolio and hence lead to higher margins.
• Predictive maintenance to optimize production: Sensor data such as temperature or vibration can be used to predict the probability of machine failure for the near future. Equipped with such insights, maintenance teams can proactively conduct measures to prevent the machine from failing. This reduces repair costs as well as production downtime, which in turn can increase revenue.

The benefits of becoming data-driven appear to be self-evident and there seems to be a general consent that for most companies there is no way around it. A quote translated from [1] nails it:

“Consistent value creation with data is already a decisive competitive advantage for companies today, and in the near future it will even be essential for survival.” [1]

1.2 Where is the Challenge?

If the general benefits of being data-driven are self-evident, where is the catch? Why is not every organization data-driven?

In fact, although the topic is not new — BI, Data Science and Machine Learning have been around for several decades — many companies still struggle to leverage their data and are far away of being data-driven. How is this possible?

To be clear from the start: From my perspective, the actual challenge was and still is rarely a matter of technology, even if so many technology providers like to claim exactly that. Sure, technology is the basis for making data-based decisions – as it is the basis for nearly every business-related activity nowadays. IT systems, tools and algorithms are readily available to execute data-based decision making. Equipping an organization with the right tools is a complicated, but not a complex problem [16], which can be adequately solved with the right knowledge or support. So, what does prevent organizations to continuously innovate, test and implement data use cases — like those above — to leverage data as a strategic asset?

In order to become data-driven, the way in which employees recognize, treat and use data within the daily business needs to radically change.

Being data-driven means, that people in every department must be able to translate critical business problems into analytical questions, which can then be addressed with data and analyses. There needs to be a (data) culture [3] within the organization, that ensures that employees want, can and must use data to improve their daily work. However, changing the behavior of a larger number of people is never a trivial task.

“Organizations need to transform to become data-driven.” [2]

So the real challenge is to sustainably change the way decisions are made in an organization. This cannot be accomplished, by designing and conducting a project, this requires a transformation.

Transforming an organization to become data-driven is a complex – not complicated – challenge for companies. This requires a solid strategic foundation — a data strategy.

However, many organizations do not posses a data strategy, struggle to create one or fear it is a lengthy process. With this article, we want to address this, by demystifying data strategy design and equipping the readers with the adequate tools to make their data strategies a success.

2. Is Data Not Already Handled Within the IT Strategy?

Most often organizations have a corporate strategy, an IT strategy or even a digital strategy. So, where is the need for a dedicated data strategy?

If your organization possesses any strategy, which addresses data as an asset with sufficient depth, you are fine. Sufficient depth means, that fundamental questions regarding data value creation are elaborated and answered, allowing the organization to take concrete actions to leverage data as an asset. However, in my experience, this is rarely the case, and there is often a lack of joint understanding in organizations, how the terms data, digital and technology differ and relate to each other.

Whilst digital strategies usually focus on process digitalization or the design of digital solutions for either internal staff or external customers, IT strategies often focus on system landscape, applications and network infrastructure. Both of them are closely related to generating and utilizing data, but neither digital nor IT strategy is — in its core — concerned with enabling an organization to become data-driven.

The info-graphic provides a more detailed comparison of the terms data, digital and technology:

Consequently, for each organization, it is important to first assess what data related topics are already covered by existing strategies, initiatives and corresponding leaders. Then, if existing strategies do not give a detailed answer to how the organization can leverage data as an asset, it is time to create a dedicated data strategy.

3. What Strategy Framework to Use for Designing a Data Strategy?

Strategy itself is perhaps one of the most misunderstood concepts [14]. There are many definitions and interpretations. As a consequence, there does not seem to be the one single true approach for designing a data strategy. So where to start?

3.1 The Playing to Win Framework

What we propose here is to select a proven and generic framework, which can be used to develop (any kind of) strategy and apply this to data strategy design. For this, we use the “Playing to Win” (P2W) strategy framework [4], which originates from joint work of Alan G. Lafley, who was former CEO of P&G, and Roger Martin, who worked for Monitor Consulting when starting to develop the framework.

The approach became the standard strategy approach at P&G and has successfully been applied in many industries since. In addition, the P2W framework has constantly been complemented and refined by Roger through a series of Strategy Practitioner Insights [15,17].

The benefits of choosing the P2W approach above others are, that it is widely known and applied and comes with an entire ecosystem of processes, templates and trainings that can be leveraged for designing your data strategy.

In the P2W framework, strategy is defined as follows [4]:

“Strategy is an integrated set of choices that uniquely positions a firm in its industry so as to create sustainable advantage and superior value relative to the competition.” [4]

So strategy is all about choice, making these tough decisions that give you a competitive edge, with no bullet-proof certainty that the choices will turn out to be the right ones. Note, that this quite differs from declaring strategy being a kind of plan [9].

The P2W strategy framework boils down to two core tools for strategy design.

3.2 Tool 1: The Strategy Process Map

The Strategy Process Map is a set of steps guiding the strategy design process [5,6]. In essence, it helps to innovate several scenarios or so called possibilities for what your strategy might look like. These possibilities are subsequently evaluated and compared, so that the strategy team can choose the most promising possibility as final strategy.

It makes use of a rigorous evaluation methodology. This helps to create clarity for each possibility about the underlying assumptions that would have to be true, such that a possibility is considered to be a good strategy.

The Strategy Process Map consists of 7 phases and can be visualized as follows:

3.3 Tool 2: The Strategy Choice Cascade

The Strategy Choice Cascade is the second tool that is used to document essential components of the strategy, which serves as output of the strategy work and is a nice way to visually communicate the strategy to stakeholders [7,8]. It consists of five elements and is often visualized similar to a waterfall:

These five elements of the Strategy Choice Cascade are defined as:

1. Winning Aspiration: The definition of what winning looks like for your organization.
2. Where to Play: The playing field on which you will, and will not, choose to compete. Typically, this element comprises five dimensions: i) Geography, ii) Customer, iii) Channel, iv) Offer, v) Stages of Production.
3. How to Win: Your competitive advantage, how you will win with customers sustainably. This boils down to either lower costs or differentiation.
4. Must-Have Capabilities: Capabilities required to build a competitive advantage.
5. Enabling Management Systems: Infrastructure (systems, processes, metrics, norms & culture), which is needed in order to effectively execute this strategy.

The actual heart of the strategy is made of the coherent choices made in box two and three: Where to Play & How to Win.

The boxes of the cascade each stand for a topic for which those tough choices, that we mentioned earlier have to be made.

How the cascade works is best illustrated using a real-world example, which is taken from [9] and describes choices for the corporate strategy of Southwest Airlines.

During the strategy design process, the Strategy Choice Cascade is not only filled once, but is repeatedly utilized at several phases of the Strategy Process Map. For example, every strategic possibility, which is created in the innovation phase, is described using the cascade to build a joint understanding within the strategy team. Also, the current strategy of the organization is described as a starting point for the strategy design process, in order to build a common understanding of the status-quo.

3.4 The Playing to Win Framework for Data Strategy Design

The P2W framework is not limited to the design of corporate strategies, but can be utilized to build any kind of strategy, e.g. for company divisions, functions (such as IT, Marketing or Sales) or even for individuals.

Consequently, it can also be utilized to design data strategies. So, what makes picking the P2W framework for designing a data strategy a particularly good choice?

• Data strategy design teams often jump into details such as what organizational design to choose (central, decentral or hub-and-spoke), or what technology to apply (data mesh, fabric, lakehouse). These are both part of the last box (Enabling Management Systems) and certainly need to be addressed, but not as the first step. The initial focus should lie on the heart of the data strategy, the where to play and how to win. The P2W framework helps to step back and to focus on the strategic questions to be answered.
• As the P2W framework is widely spread, there are good chances, that your organization already applies it. If this is the case, it is straightforward to integrate the data strategy with other existing and connected strategies (see also Section 4) and it helps to communicate the data strategy to relevant stakeholders, as the they are familiar with the methodology.
• Some data strategy approaches seem to me like picking choices from best practices or following check lists to tackle all elements required, lacking real strategic thinking. The P2W framework guides and forces the strategy design team to apply strategic thinking with rigor and creativity.

Whilst the idea of applying the P2W framework for data strategy design is neither rocket science nor new, existing literature [e.g. 12,13] does — to the knowledge of the author — not provide a detailed description of how to adapt the framework for the application to data & AI strategy design in a straightforward manner.

In the remainder of this article, we therefore adopt the Strategy Choice Cascade to data strategy design, equipping data strategy design teams with the tools required to readily apply the P2W framework for their strategy work. This results in a clear definition of what a data strategy is, what it comprises and how it can be documented and communicated.

4. Link Between Data & Business Strategy

Applying Data Science, AI or any other data-related technology in a company is not an end in itself. Organizations do well not to start a potentially painful corporate transformation program to become data-driven without a clear business need (although I saw this happening more often than one would think). Hence, there must be a good reason why becoming data-driven is a good idea or even essential for the company to survive, before jumping into designing a data strategy. This is often described as a link between data and business strategy.

The great news is, when using the P2W framework for data strategy design, there are natural ways to provide such a link between data and business, by declaring data management, analytics or AI as must-have capabilities within the corporate strategy, enabling the organization to win.

One example for this can be found in [7], where the Strategy Choice Cascade is formulated for the corporate strategy of OŪRA. This health technology company produces a ring, which captures body data similar to a smart watch. The capabilities are, amongst others, defined as follows:

From this, the need for state-of-the-art data management and data analytics capabilities are apparent.

As the benefits for being data-driven exist in nearly every industry, data management, analytics or AI are nowadays not only must-have capabilities for high-tech companies, such as in the example above. Industries such as manufacturing, finance, energy & utilities, chemicals, logistics, retail and consumer goods increasingly leverage data & analytics to remain competitive.

In whatever industry your company plays, it ideally has already established the need for being data-driven and expressed it as part of the corporate strategy or any other strategy within the organization (e.g. sales or marketing), before you start with your data strategy work.

In many situations, however, such an explicit link between business and data is not given. In such cases, it is important to spend enough time, to identify the underlying business problem, which should be addressed by the data strategy. This is typically done in phase 1 of the Strategy Process Map (cf. Figure 2).

One way to identify the strategic target problem for the data strategy is peeling the onion by conducting a series of interviews with relevant business and management stakeholders. This allows to identify pains & gains — from a business perspective as well as with regards to the management and usage of data in the organization. Once this is done, it is then important to create a consent amongst stakeholders about the problem to be addressed. If there is consent that data is mission critical for the organization, the business need for a data strategy is evident and the corresponding corporate strategy should be updated accordingly.

5. The Data Strategy Choice Cascade

Once we have established the need for the company to treat data as a valuable asset which needs to be taken care of and which needs to be leveraged, it is time to design a data strategy.

5.1 Translating the Choice Cascade to Data & AI

In order to apply the P2W framework to data strategy design, we first must translate the wording from the corporate context to the data context. So, we start by defining some core elements and rephrase them, where it seems helpful:

• Offering → Data Offering: This comprises data products, analytics products or services related to data value creation.
• Customers → Data Customers: People, who are served with the data offering. These are most likely to be internal stakeholders or groups within the company but can also be external users.
• Company → Data service provider: People, who create the data offering
• Competition: Alternatives, that data customers might choose, other than the data service provider. This can range from data customers not served at all, serving themselves or using external services.
• Geography → Focus Areas: Which departments, teams, domains or areas of the organization your data strategy will focus on.
• Channels → Data Delivery Channels: How data customers get access to the data offering.
• Stages of production → Data Lifecycle Management: Which stages of the data lifecycle are done in-house or outsourced.

In addition, the strategy definition of the P2W framework can directly be augmented to data strategy:

Data Strategy is an integrated set of choices that uniquely positions a firm in its industry so as to create sustainable advantage and superior value relative to the competition, by leveraging data as an asset.

5.2 The Data Strategy Choice Cascade

Once the core elements have been translated and we have defined what data strategy means to us, we can finally define the Data Strategy Choice Cascade:

1. Winning Aspiration: The definition of what winning with data, analytics and AI looks like for your organization.
2. Where to Play: The playing field on which you will, and will not, choose to compete with data, analytics & AI. This typically includes five dimensions: i) Focus Areas, ii) Data Customers, iii) Data Delivery Channels, iv) Data Offerings, v) Data Lifecycle Management.
3. How to Win: The strategic advantage your data strategy will create. How you will win with data customers sustainably. This boils down to either lower costs or differentiation.
4. Must-Have Capabilities: The critical data-related capabilities needed to build a competitive advantage.
5. Enabling Management Systems: Infrastructure (systems, processes, governance, metrics & culture), which is needed in order to support and sustain your data strategy.

Some more detailed thoughts on data monetization options as part of the Where to Play for data strategies can be found in [11].

5.3 The Cheat Sheet for the Data Strategy Choice Cascade

If data strategy is about making choices, what are these choices for each element of the Data Strategy Choice Cascade in detail? The following Cheat Sheet for the Data Strategy Choice Cascade compares and contrasts the cascade for corporate and data strategy in more detail and provides example choices to be made for the data strategy design.

5.4 When to Use the Data Strategy Choice Cascade

Similar to the cascade for corporate strategy, the data strategy choice cascade can be applied in several situations, e.g. it can be utilized to:

1. Document your current data strategy: This helps to build a joint understanding of the status-quo amongst stakeholders. This is typically done when starting or re-starting the data strategy design process as in phase 1 (identify the problem) of the Strategy Process Map.
2. Detailing different possibilities: as part of your data strategy design process (phase 3: generate possibilities of the Strategy Process Map). Here it usually suffices to detail the heart of the strategy, the Where to Play and the How to Win.
3. Documenting your final strategy: After different possibilities for the data strategy have been rigorously assessed, the final strategy can be documented and communicated using the choice cascade. In practice, this documentation is often complemented with more detailed texts and other forms of communication, depending on the particular needs of the audience.
4. Describing the data strategy of your competitors: The data strategy choice cascade can be used to sketch a rough version of the data strategies of your competitors, which might always be a good idea during the design process of your own data strategy.

6. Two Example Data Strategies

In order to illustrate the application of Data Strategy Choice Cascade, we create two (fictitious) possibilities for the data strategy of the company OŪRA. Recall that OŪRA is a health technology company producing a ring, which captures body data similar to a smart watch. A corporate strategy formulation for this company using the P2W framework can be found in [7].

We consider two possibilities, which are quite different, in order to illustrate how the Data Strategy Choice Cascade can be utilized during the data strategy design process to uniformly document, effectively compare and communicate different strategy possibilities.

The first strategic possibility focuses on data-driven features for end-users as external data customers.

The second possibility focuses on analytics for operational excellence of internal data customers.

Note, that the organizational and technological choices are made in the the fifth element — Enabling Management Systems — that certainly depend on the core strategic choices made in the preceding elements. The choice cascade prevents users from falling into the common trap of jumping into technological solutions, before the actual strategy has been defined.

7. Conclusions & Outlook

We have seen that the renowned and practice-proven Playing to Win framework for strategy design can be modified for data strategy development leading to the Data Strategy Choice Cascade. The cheat sheet provided can guide data strategy design teams to rapidly apply the framework for their strategy work. Consequently, when it comes to data strategy design, there is no need to re-invent the wheel, but you can stand on the shoulders of giants to ensure your data strategy becomes a success, allowing your company to win.

Is this all? Are we now set to develop our own data strategy?

The cascade is merely a tool, which is used to express many possibilities, that we create during the data strategy design process. If you just use the cascade to write down one possibility for your data strategy, which you think is a good idea, how do you know that this is the best option for your company to win with data?

To find out, you need to apply the Strategy Process Map for your data strategy design, which I will detail in one of my next articles.

References

[1] Sebastian Wernicke, Data Inspired (2024), book in German language published by Vahlen

[2] Caroline Carruthers and Peter Jackson, Data-Driven Business Transformation (2019), book published by Wiley

[3] Jens Linden, Datenkultur — Was & Warum? (2024), LinkedIn post

[4] A. G. Lafley and Roger L. Martin, Playing to Win (2013), book published by Harvard Business Review Press

[5] Michael Goitein, The “Playing to Win” Framework, Part II — The Strategy Process Map, blog

[6] IDEO U, Strategic Planning: How to Get Started, blog

[7] Michael Goitein, The “Playing to Win” Framework — Part III — The Strategy Choice Cascade, blog

[8] Roger Martin, Decoding the Strategy Choice Cascade (2023), Medium article

[9] Roger Martin, A Plan Is Not a Strategy (2022), video

[10] Jens Linden, Daten — Digital — Technologie: ist das alles das Gleiche? (2024), LinkedIn post

[11] Jens Linden, Datenmonetarisierung — Das Where to Play für Datenstrategien (2024), LinkedIn post

[12] Christena Antony, Beyond the Buzzwords: Crafting a Data Strategy that Works! (2024), LinkedIn article

[13] Deloitte & Google News Initiative, Digital transformation through data: a guide for news and media companies to drive value with data (2019), website

[14] Marc Sniukas, What the heck is strategy anyway? (2024), LinkedIn post

[15] Roger Martin, (Playing to Win) x 5 (2024), Medium article

[16] intrinsify Content Team, So unterscheiden moderne Manager: Komplex vs. Kompliziert (2023), website

[17] Roger Martin, Playing to Win/ Practitioner Insights (2024), website with list of articles

Unless otherwise noted, all images are by the author.

Side note: ChatGPT-4o was used to complement some elements of the cheat sheet example choices and the choices of the example data strategies to ensure anonymity.

The Data Strategy Choice Cascade was originally published in TDS Archive on Medium, where people are continuing the conversation by highlighting and responding to this story.

1 What is Data Valuation?

In today’s data-driven world, organizations have come to recognize that data is no longer merely a byproduct of operations, but a STRATEGIC ASSET of immense value. Data valuation, the process of assessing the economic worth of a company’s data, has emerged as a critical practice for unlocking the full potential of this valuable asset [1,4].

2 Why Bother About Data Valuation?

Valuating data offers a wealth of benefits for organizations [1–4]:

1. Making Informed Decisions About Data Investments: Data valuation provides actionable insights to prioritize data-driven initiatives, such as data & AI transformation programs. Furthermore, it can help to allocate resources effectively, and optimize data infrastructure. This can lead to better return on invest (ROI) on data investments and improved business outcomes.
2. Assessing Company Value: Data valuation contributes to a comprehensive understanding of a company’s overall value, recognizing intangible assets like data as key driver of business success. This is particularly relevant in mergers and acquisitions (M&A) scenarios, ensuring fair valuations and strategic decision-making.
3. Prioritizing Data-Related Activities: Data valuation helps organizations focus their efforts on the data assets and initiatives that hold the highest potential value. This includes prioritizing data governance initiatives to ensure data quality and security, optimizing data usage to maximize insights, and prioritizing data-driven projects that align with strategic goals.
4. Raising Data Awareness: Data valuation fosters a data-driven culture within an organization, encouraging managers and employees to recognize the importance of data as a strategic asset and to take responsibility for its effective management. This awareness promotes a data culture across the organization and mitigates the risks associated with data breaches and misuse.
5. Identifying Data Monetization Opportunities: Data valuation can identify data assets with the potential for direct monetization, such as selling data sets or licensing data access to third parties. It can also reveal opportunities for indirect monetization, such as leveraging data to develop new products or services. This can generate additional revenue streams and enhance the overall value proposition of data as an asset class.

In summary, data valuation is not just about assigning a monetary value to data; it’s about understanding its true worth and unlocking its potential to drive business growth, improve decision-making, and enhance strategic positioning. By valuing data, organizations can make informed decisions, prioritize strategic initiatives, and maximize the return on their data investments.

3 Navigating the Landscape of Data Valuation: Approaches and Considerations

Data valuation is a complex process that requires a thoughtful approach. There isn’t a one-size-fits-all method for valuing data. Different methods may be needed depending on the specific use case. Here are some key dimensions to consider when valuing data:

• Monetary vs. Scoring: One can evaluate data assets in terms of currency or give a scoring value. Monetary valuation assigns a direct financial value to data, while scoring uses a relative scale to rank the value of different data assets.
• Current Value vs. Incremental Value: This involves evaluating how data nowadays already contributes to the value generation in the organization in contrast to what further potential by leveraging analytics and AI use cases is possible.
• Intrinsic Value vs. Contextual Value: This involves evaluating the inherent value of the data itself (accuracy, completeness, etc.) versus the value it can bring in a specific context or use case.
• Risk vs. Reward: This involves evaluating the potential risks associated with data (such as privacy concerns or potential misuse) versus the potential rewards or benefits that can be gained from the data. This dimension emphasizes the need to balance the pursuit of value from data with the management of associated risks.

By considering these dimensions, organizations can develop a robust approach to data valuation that aligns with their strategic goals and maximizes the return on their data investments.

4 How Data Valuation is Done

Data valuation is not a precise science, but rather a structured and repeatable methodology. It involves developing consistent data valuations based on clear underlying assumptions and hypotheses [1]. There are several approaches to data valuation, and the choice of approach can depend on the specific use case and need of the organization [1–4]. Some of these approaches include:

Dataset-Driven Approach

The dataset-driven approach is a methodology for data valuation. This approach recognizes that a single person’s data is not very valuable, but combining the data generated by thousands of people is a completely different story. In general, combining datasets creates new insights and hence new value for different actors and stakeholders.

Initiative-Driven Approach

The initiative-driven approach focuses on the role data plays in creating value (e.g. data use cases using Business Intelligence or Artificial Intelligence). By valuing your data based on the role it plays in creating value, it becomes clear where to focus limited resources to get the best ROI from data.

Stakeholder-Driven Approach

The stakeholder-driven approach is another methodology for data valuation. This approach recognizes that different stakeholders may have different views on the value of the same data. Therefore, it seeks to value data from the perspective of different stakeholders.

With-and-Without Method

The with-and-without method estimates the value of data assets by comparing the financial outcomes of the business with and without the data [6].

The Cost Approach

The cost approach assesses the value of data assets by approximating the cost of recreating the data.

In conclusion, data valuation is a process that requires a structured and repeatable methodology. The choice of approach depends on the use case, and different approaches can be used depending on the situation.

5 Data Valuation as a by-product of a company’s data-driven business transformation

Data valuation emerges as a natural byproduct of a company’s data-driven business transformation. As organizations embark on this journey, they identify and prioritize data use cases that drive tangible value, such as cost savings, revenue growth, risk mitigation, or the creation of new data-driven products and services. To effectively measure and track the impact of these initiatives, data leaders (such as Chief Data Officers) must estimate the financial impact of each use case.

This process reveals the specific data sources required to support each use case. By establishing a clear link between use cases and data sources, organizations can assign a value to each data source, resulting in an inherent monetary valuation for the data assets involved. This detailed process is outlined in [5].

I am Jens, a business-minded data expert with two decades of experience in implementing data and AI use cases. I help leaders across industries design strategies and foster cultures that unlock the full potential of data and algorithms.

References

[1] How To Value Your Data Assets | A Methodology; Andy Neely; https://www.anmut.co.uk/how-to-value-your-data-assets/

[2] A Review of Data Valuation Approaches and Building and Scoring a Data Valuation Model; Mike Fleckenstein, Ali Obaidi, and Nektaria Tryfona; https://hdsr.mitpress.mit.edu/pub/1qxkrnig/release/1

[3] Data Valuation | What Is Your Data Worth?; Andy Neely; https://www.anmut.co.uk/data-valuation-what-is-your-data-worth/

[4] Data Valuation | Why It Matters & How It’s Done; anmut; https://www.anmut.co.uk/an-introduction-to-data-valuation/

[5] How to Build and Manage a Portfolio of Data Assets; Willem Koenders; https://towardsdatascience.com/how-to-build-and-manage-a-portfolio-of-data-assets-9df83bd39de6

[6] Data valuation: Understanding the value of your data assets; Deloitte; https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Finance/Valuation-Data-Digital.pdf