“Break the data silos” is a slogan, not a diagnosis. Most messy data isn’t caused by malicious territorialism, but by well-intentioned people solving the problems in front of them.
The siloed executive jealously guarding a spreadsheet is a convenient villain. More often, however, that’s a comforting myth we tell ourselves to avoid a harder reality. It isn’t that departments refuse to share; it’s that they lack a common language—a universal grammar—for describing and measuring the same underlying facts.
Consider Marketing, building models for acquisition perfectly tailored to their funnel. At the same time, Finance constructs its own universe for revenue forecasting, drawing from different systems and metrics. Marketing may count a “customer” after first purchase while Finance only counts one after revenue is recognized. Both are rational, both are solving critical business challenges. But inevitably, their data architectures diverge.
This isn't a failure of collaboration; it's a triumph of individual initiative. Teams, facing an objective, will find the most efficient path. If enterprise data is cumbersome and the team needs answers now, they’ll route around it. That’s tactical agility. But each local optimization—perfectly sensible on its own—adds to a quilt that looks chaotic when you step back.
Lamenting these silos mistakes the symptom for the diagnosis. Sure, politics exist, but they are seldom the primary driver. Incentives are. When individual success isn’t aligned to a collective goal, competent people will reliably create the very fragmentation the company later tries to undo.
The solution isn't better collaboration tools or unified data platforms. It's structural: making the easiest path for any team the governed path—the one that uses shared definitions, default data, and incentives that reward reuse over reinvention.
Until individual incentives align with collective needs, data silos aren’t a failure of the system. They’re proof the system is working exactly as designed.