The Problem
A mid-market company reaches a point where every leadership meeting starts the same way: two people present the same metric with different numbers, and the first twenty minutes go on working out whose spreadsheet is right.
The data exists. It is in the CRM, the billing system, the product database, and four operational tools nobody centrally owns. Each team extracts what they need, applies their own assumptions, and builds reporting on top. None of it is wrong exactly. None of it agrees.
Analysts spend most of their week rebuilding the same extracts and reconciling numbers instead of doing analysis. Leadership makes decisions on figures they do not fully trust, or defers decisions waiting for a number everyone accepts. The cost is not the analyst time — it is the decisions that get delayed or made badly.
The Solution We'd Build
A governed data platform: pipelines that land source data reliably, a dimensional model that encodes agreed definitions, and a semantic layer that makes those definitions the only way to query a metric.
The technical work is not the hard part. The hard part is the definition workshops — getting Finance, Sales, and Operations into a room to agree what active customer means, and recording the answer somewhere both auditable and enforceable.
What We Would Not Do
- We would not start with the warehouse. We would start with a data audit. Buying a platform before knowing what your data can support is how teams end up with an expensive warehouse full of numbers nobody trusts.
- We would not migrate everything at once. We would move the highest-value domain first — usually revenue reporting — prove the pattern, then expand. Big-bang migrations fail slowly and expensively.
- We would not build real-time unless a decision needs it. Streaming carries real operational cost. If nothing changes when data is fifteen minutes old rather than fifteen seconds, batch is the better engineering choice.
- We would not skip the definition workshops. They are uncomfortable and they are the actual work. A perfect pipeline serving contested definitions solves nothing.
Delivery Timeline
What Good Looks Like
These are targets based on the architecture and on what comparable platforms achieve — not results we are claiming from a specific past engagement. The audit in week one is what tells us which of them are realistic for your situation. Sometimes the finding is that your data cannot yet support the reporting you want, and the useful work for a quarter is fixing what sits underneath.
Services Involved
Can You Trust Your Numbers?
Tell us where your data lives, which metrics are contested, and what decisions depend on them. We will assess honestly what your data can support today and what it would take to get further — no obligation.