Data Silos Don’t Just Block Information – They Block Progress
In many utility organizations, what shows up as “a reporting problem” is actually a business problem disguised as a technology problem: unclear ownership, inconsistent definitions, and limited trust across teams.
The real issue isn’t where data lives, it’s how it’s governed.
It’s easy to blame the systems—AMI data in one place, outage data in another, work management somewhere else, finance in its own world. But where data lives isn’t always the root issue. With modern approaches (and platforms like data lakes), organizations can connect data across environments. The harder question is organizational: who decides what a metric means, who can use it, and how it’s governed.
When metrics multiply, trust collapses.
When governance isn’t clear, uncertainty and doubt fill the gap. Teams start making “just-in-case” copies of data. Different groups define the same KPI differently—SAIDI/SAIFI, truck roll rates, revenue leakage, bad debt, interconnection cycle time, you name it. The differences might be small, but the impact is huge: when leaders see competing numbers in a meeting, trust drops fast. Instead of acting, the organization debates definitions.
Gatekeeping creates “bandit spreadsheets.”
Another common pattern is gatekeeping. A central team controls access so tightly that frontline teams can’t get what they need without asking for a report. To keep operations moving, people build workarounds—manual extracts and “bandit spreadsheets” that become the real source of truth because they’re accessible, not because they’re reliable. Over time, you can invest heavily in platforms and pipelines and still run critical processes on emailed attachments.
Why this becomes business risk (fast).
In regulated environments, misalignment across metrics isn’t just inefficient—it can create compliance exposure, audit headaches, and delays in responding to regulators or boards. It also slows operational performance: analysts spend their time reconciling versions instead of identifying drivers, forecasting load, or pinpointing reliability issues.
First steps toward a connected data environment.
The goal of unifying data isn’t to “buy an integration tool.” It’s to create connection that enables collaboration, transparency, and speed. A connected environment—supported by integrated platforms like data lakes—helps establish a shared source of truth, clear definitions, and visible lineage so teams can trust what they’re using.
Practical first steps don’t require perfection:
- Map what exists (systems, key datasets, and the reports that drive decisions).
- Assess governance maturity (definitions, ownership, quality, access).
- Prioritize high-stakes metrics first (reliability, regulatory reporting, financial and operational KPIs).
- Publish shared definitions in a catalog and design access that enables safe use without bottlenecks.
Because when data becomes connected and governed, the organization stops arguing about whose numbers are right—and starts moving faster on the work that matters.
