Mapping the Communication Landscape

This model influences what data is considered important, and why. Buried under layers of internal myth and assumption, the business model is frequently misunderstood. Most teams work from an assumed understanding of what success looks like, but those assumptions aren't always shared or accurate.

Without explicit understanding of the business model, decision-making becomes disjointed from those that execute the work and productivity becomes a proxy for progress.

The operating model is a system of hidden dependencies, invisible queues, and resource competition. When teams only understand their piece of the larger system, they optimize one step in a process while often transferring inefficiencies to other teams. They can't anticipate the cascading effects of their decisions.

Since the operating model spans roles and functions, no one sees the whole thing. And when it changes, which it does constantly, those changes are often invisible to analytics teams trying to measure and improve it.

But the information model isn't a mirror, it's an interpretation. Every schema decision reflects assumptions about how the business works and what matters. It's not neutral, it embeds beliefs about reality. When the business model is misunderstood and the operating model is siloed, building an information model becomes like playing whack-a-mole.

Teams create metrics that seem logical in isolation but don't connect to how value actually flows through the organization. As a result, what gets measured is often disconnected from how value is created, and analytics teams find themselves constantly rebuilding systems that never quite capture what the business actually needs.

Executive dashboards compress complex operations into health and velocity metrics. Operations dashboards surface exceptions requiring immediate attention. Data explorers help teams understand patterns invisible in aggregate views. Presentations give data stories a stage to influence decisions.

Trying to compress all views into one format leads to confusion, and visibility of data alone doesn't create value. Organizations mistake monitoring for managing, accumulating a compounding liability of aging data artifacts while still struggling with the treadmill of ad hoc analysis.

Even when the right data exists, it's often scattered across tools, teams, and interpretations that make synthesis nearly impossible. Each optimization represents a hypothesis about how the business works, embedded in algorithms and automated decisions. But, without shared understanding of the business and operating models, these hypotheses often conflict with each other.

Three overlapping perspectives on the same reality. This compounds complexity, erodes trust, and creates cycles of reactive over-corrections. Equilibrium is not a one-time fix. It's a state of dynamic alignment where these three models are brought into stability, which requires accepting that analytics strategy is really organizational strategy, how people combine their efforts to create value.

Success means designing interfaces that serve as navigational maps, helping people see the same system from different roles with enough shared structure to act together. These interfaces must align with the data that supports operational rhythms, enabling decisions that accelerate the whole organization forward.

When this alignment exists, investment becomes intentional. You can distinguish between data as a routine cost of business (Optics) and data as speculative investment in improvement (Optimization). Data communication isn't just a technical challenge, it's an organizational one. It's how people build shared understanding in a fragmented environment and align effort even without perfect information.