There is no generic data
Becoming data-driven is not trading intuition for KPIs. Data always has a shape, a source and a life cycle, and it takes a culture to keep it meaningful.
“My role […] was a glorified customer support agent for SQL.” — Chad Sanderson
Truth is, most organizations realize they “should” become data-driven, whatever that means, but people within an organization have very little incentive to understand and develop the practices required to operate around and with data.
We like to talk about data as valuable, as the new oil, as a resource that can be moved and transformed like physical materials. But data happens to be a beast of its own kind.
Data has a shape
There is no such thing as “generic” data. Data always has a shape, a source, a life cycle. It can convey meaning or become completely meaningless. Extracting and holding data has a cost, and stale data that isn’t used or connected holds no value and depreciates very fast.
Data can flow through other systems that make things from it, or that change their own behavior because of it. But data can also be the thing itself: modeled, built for a purpose and exposed as valuable on its own.
Not a technical detail
The work of transporting, modeling and making sense of data in any sustainable way will never be achieved by treating data engineering as a “backend implementation” or a “technical detail”.
Building systems and processes that keep leveraging the value of data across an organization requires cultural practices that have yet to mature and become common sense. Simply applying an off-the-shelf product management approach doesn’t improve the process of increasing knowledge and understanding. It may be easier at first, in a narrow scope like a small team, than across organizational boundaries. But it soon hits a ceiling that keeps anything more meaningful from emerging.
What data-driven means
Becoming data-driven is not about trading intuition and experience for KPIs and probability.
It’s about taking into account many kinds of signals from many sources, designing processes and systems to gather and move those signals around, and composing living representations that let several perspectives of a problem be observed and reasoned about systematically.
It’s an ongoing, ever-evolving practice. It’s most valuable when you cultivate it alongside the core business and daily operations, instead of building it to fit every specific use case.