The visibility paradox

The modern organization very rarely suffers from a lack of information. If anything, the complete opposite problem has cropped up: they have more visibility than ever before, but far less certainty about what that visibility means. The prevailing assumption is simple: if we can see more, then we should be able to make better decisions. More data leads to better decisions.

But data availability hasn’t been the core problem for quite some time now. It seems to me that it’s data interpretation. Data systems excel at capturing and organizing information, but aren’t able to resolve ambiguity just on their own. For instance, metrics can show something has changed (but why?) and dashboards can identify a problem (but who owns the solution? What tradeoffs should be considered?). In other words, visibility does not automatically create understanding.

Part of the problem is structural. Every metric represents a decision about what matters to the company. They choose what to measure, how to define success, and which results deserve attention; while necessary, they also become simplistic. The more that the organization measures, the more representations of reality they create. This leads to a more difficult problem when teams operate from different versions of the truth. A product team likely will prioritize engagement metrics, while a compliance team will focus on risk indicators, but an engineering team will emphasize reliability measures. Every team holds accurate data, all the while answering different questions. So if this is the case, then what is assumed to be transparency can very quickly morph into something like competing interpretations of the same reality.

Let’s consider how this happens. We want to treat visibility as the solution because information inherently feels objective. Neutral (at least on the surface), maybe even authoritative. But metrics are really only useful when they are connected to shared context and definitions. Without this foundation, we end up debating whose data is correct when we should be working to understand what this data is telling us. That leads us to an accountability problem; when decisions are widely distributed across dozens of teams and dashboards, it becomes harder to pinpoint ownership and responsibility.

Human decision-making, unfortunately, has limits. When information grows, we naturally simplify. Teams inevitably will focus on a handful of key indicators and status trends that can be communicated quickly. (Again, necessary.) But without context, the same metrics create conflicting interpretations. Over time, organizations develop a false sense of confidence. We feel informed when we are surrounded by information. However, information abundance doesn’t automatically guarantee understanding.

Therein lies the real paradox. The challenge is to build the structures and shared context needed to interpret the data accurately and consistently. After all, we are only made wiser when we understand what we know.