Why this series of articles?
Well, in my conversations with colleagues, friends, and former teammates, I sensed that becoming data-driven remains a genuine challenge for most companies and the people inside them. Despite all the effort that might be put into that topic (strategies get written, tools get bought, dashboards get built, reports are sent out), it somehow does not fully pay off. Neither for non-technical people, nor for the analytics specialist. And most often not for the company itself when looking at return on investments (ROI).
The reasons can be manifold, but, usually, they are not technical. All too often, it is that the people supposed to create value from data, or to work more efficiently with it, in some form or other, were missing from the strategy in the first place.
The same pattern keeps recurring with AI. The latest model gets rolled out before anyone has checked that these people have the minimum data literacy required to work with it responsibly. Tooling outpaces judgement, and the gap shows up in the resulting outcome manifesting in bugs, false numbers, or even less productive patterns.
Now, these are my own observations, drawn from years of working at the analyst–business interface. My background is in scientific research and data analytics, which informs how I approach the topics here: lead with evidence, hedge honestly where evidence is thin, treat opinion as one input among several rather than the whole argument. My aim is to spark thinking and discussion that earn the time spent.
Articles
- Becoming Data-Driven Is a Relationship, Not a Hand-Off. Especially With AI. The two-way relationship between business and data teams is the real foundation of data-driven work; AI raises the stakes without changing the structure.
More to come…