“Let’s define a data strategy.”
Translation: let’s argue about tools for 6 months.
Here’s the thing I don't like to admit openly:
I've been a data leader for almost 20 years.
In my first 6 years, I believed that data strategy was mostly about data stacks.
Over those years, I realized (the hard way):
If you want a strategy that survives the next reorg, AI hype wave, or exec sponsor change - start here:
- Problem: What are we solving?
- Users: Who are you serving?
- UVP: What's our data team's secret sauce?
- Solution: What are we building and what are we NOT building?
- Distribution: How will we get this in front of users without chasing them down?
- Systems: What helps this scale without breaking us?
- Outcomes: What will users actually do differently?
- Costs: What’s the real investment? (Spoiler: it’s not just the software)
- People: How will we grow a team that wants to stick around?
- Vision: What guides our decisions when priorities clash?
Answer those 10?
You’ve got a data strategy.
Ignore them?
You’ve got a dashboard backlog and a stack no one uses.
I know because I've been there and lived through the pain...
👉 Follow me, Sebastian Hewing, for daily insights on data strategy.
♻️ Repost if you've ever watched a “data strategy session” turn into a Power BI vs Tableau debate.
Narendra Cherlopalli Question seven is the one that changes everything in finance. In financial close, FP&A, and treasury, nobody asks what will users actually do differently once the AI agent is live. The reconciliation gets automated. The variance report generates faster. But if the controller still reviews the same way and the treasury team still makes the same calls, nothing changed. A strategy that does not alter a decision is just a faster way to produce outputs nobody acts on. Mar 30
Arthur Feriotti You can feel the scars behind this post. When we start with clarity everything else becomes a lot easier to defend when priorities inevitably shift. Mar 27 1 like