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Embrace The Data Schlep First
Schlep exists in every data project, however, you begin to look out for it everywhere cool data projects exist
Here’s an uncomfortable truth (or an annoying reminder, if you’re an old hand at this): before you can get anything interesting done in data, you need to get past a period of data schlep first.
What is data schlep? Schlep is an informal term that means tedious, boring work. You can already see where we’re going with this: data schlep refers to all the boring data cleaning and plumbing and transforming that you do at the start of any data project.
At Holistics, we sometimes talk to business leaders in South East Asia who are at the beginning of their digital transformation journeys. More often than not, they talk about exciting data projects that they want to see in their companies — like recommendation systems or predictive modeling, or all the cool applications of machine learning that we read about in the popular press.
And, just about as often, we find ourselves warning them about the data schlep problem that they’ll inevitably face.
“Do you have a data warehouse?” we’d say.
“No,” they’d answer.
