Analytics & BI
One version of the truth, wired to decisions.
Not more dashboards — fewer, trusted ones: metrics defined once, agreed by everyone, and connected to the decisions your team makes weekly.
What we build
- A metrics layer with definitions agreed once
- Dashboards your team opens on their own
- Self-serve for the questions that repeat
- Alerting on the numbers that demand action
When teams come to us
- Two dashboards, two different answers
- Decisions made on gut because the data takes too long
- A BI tool everyone pays for and nobody opens
The dashboard nobody opens
Every company we work with has one: a BI tool that everyone pays for, that took a quarter to set up, and that shows up in nobody's week. It is not that the charts are wrong. It is that they were built to show what the data could show, not to answer a question someone actually has on Monday morning — and a chart that answers no recurring decision has no reason to be opened twice.
The fix is to start from the decisions, not the data. The weekly pipeline review, the capacity call, the monthly number the board asks about: each of those needs a specific set of numbers, at a specific cadence, defined in a way everyone in the room accepts. We build those first, one dashboard per decision, and we watch what gets opened. What nobody uses in a month gets removed rather than defended. What remains is small, trusted, and part of how the company decides — which is what "business intelligence" was supposed to mean.
How we build it
Definitions agreed once
every metric with an owner and a formula
Model the semantic layer
one version of the truth, by construction
Wire dashboards to decisions
every chart answers to a recurring decision
Adopt and prune
what nobody opens, dies
Why dashboards die
Two answers
the same metric computed two ways in two tools, and a meeting spent arguing which is right. Nobody trusts either after that. Fix: one definition, one owner, one formula, and every chart reads it.
No decision attached
a chart that does not answer to a recurring decision is furniture. Every dashboard we build names the decision it serves and who makes it; the ones that can't get pruned.
Too slow to matter
the number arrives after the decision was made on gut. The fix is rarely real-time; it is usually a refresh that matches the decision's cadence, and an alert for the one number that can't wait.
Nobody opens it
the honest signal. We watch usage from launch, and what nobody opens in a month is removed rather than defended.
Fewer dashboards, trusted, beat more dashboards, ignored — every time.
Works well with
FAQ
We already have dashboards — why doesn't anyone use them?
Usually trust and definitions, not charts. Fixing that is the actual work.
Can AI answer questions from our data?
Once definitions are solid, yes — that’s the natural next step.
Do we need real-time?
Where a decision needs it. Most don't, and real-time everywhere is expensive theater.
How is this different from buying a BI tool?
The tool is the last decision, not the first. Trust comes from definitions agreed once and modelled so that every chart reads the same number; that work is the same in any tool, and it is the work most BI rollouts skip. We design for your case and your team — we don't sell licenses.
What does "wired to decisions" mean in practice?
Each dashboard is built for a named, recurring decision: the Monday pipeline review, the weekly capacity call, the monthly board number. It shows what that decision needs and nothing else, and it is judged by whether the decision got faster or better — not by how many people looked at it.
Can the dashboards feed an AI assistant later?
That is the natural next step, and it is why definitions come first. An assistant that answers "what was revenue last month" is only as trustworthy as the definition of revenue it reads; once the semantic layer exists, wiring a conversational interface to it is a small project, not a new one.