Data & Decision
Intelligence.
Turn data into decisions you can measure and defend, not dashboards nobody opens.
You have the data You are still guessing
Every client we meet has more data than they did two years ago. This is where that gap usually comes from.
Dashboards nobody opens
A dozen commissioned, two checked in a crisis, none checked on an ordinary Tuesday. A dashboard nobody acts on is just a report with a login screen.
Attribution too fragile to spend against
Change the model slightly and the winning channel changes with it. Finance stops trusting the number, and the budget conversation reverts to opinion.
Insight that never changes a decision
The deck is thorough, the analysis is correct, and nothing downstream actually moves. Call it expensive trivia instead of insight.
How we turn data into decisions
We work backward from the decision. The warehouse comes second. Four principles that hold across every engagement we run, no exceptions.
Start from the decision
We map the decisions people actually make, and who makes them, before we touch a single pipeline. Data follows the decision, never the other way round.
Instrument once, properly
One measurement layer the whole company reads the same way, instead of a new definition of "revenue" in every team’s spreadsheet.
One number per decision
Every decision gets the smallest set of numbers that can actually settle it. If a metric cannot change an action, it does not earn screen space.
A cadence, not a screen
We build the rhythm, who looks, when, and exactly what they are expected to decide, so the insight has somewhere real to land.
What lands on your desk.
Six things a decision-maker can act on this week. None of them is a report that gets skimmed once and archived.
A measurement model
The metric tree connecting a daily action to the number your board actually watches, one written definition per metric.
Decision-grade dashboards
Few, opinionated, and built around the decisions people actually make rather than every field the warehouse happens to return.
Attribution a CFO accepts
A model with documented, defensible assumptions, so finance can spend against it without taking it on faith.
An insight cadence
A standing rhythm of reviews tied to real decisions, so every piece of analysis gets an owner and a deadline.
A clean data foundation
Pipelines, definitions and governance underneath, documented so the next question does not start from zero.
Enablement for your team
Your analysts run the model themselves after we leave, and can defend it in a room without us there.
Four disciplines, one accountable lead.
You are not briefing an analytics vendor, a data-engineering shop and a strategy firm separately and reconciling their reports. One team owns whether the number actually moves.
See all servicesData & Intelligence
The measurement model, the attribution, and the dashboards people actually use.
PrimaryAI & Intelligent Solutions
Forecasting, propensity and the models that turn description into a decision worth automating.
PrimaryStrategy & Consulting
Which decisions matter, in what order, and what a better one is actually worth.
SupportingTechnology & Product
The pipelines and instrumentation that make the numbers trustworthy in the first place.
SupportingData that changed
a decision.
“A dashboard is not the deliverable. A better decision is. If the number never changes what someone does on Monday, we have not finished the work.”
The questions
we always get.
The four that come up in nearly every first conversation.
Ask us anythingDo we need a data warehouse before you start?
No, we start from the decisions you need to improve and work backward to the data underneath them. Often that data already exists, just defined five different ways across five teams.
How is this different from hiring a BI vendor?
A BI vendor delivers you dashboards. We start from the decision and answer for whether it actually improves, and treat the dashboard as a by-product of that work rather than the point of it.
Our attribution keeps getting argued over. Can you fix that?
Nobody can make attribution certain. We can make it documented, consistent and defensible, so the argument shifts from "is this number real" to "what do we do about it."
Will finance actually trust the output?
That is exactly the test we build against. We build the model with finance in the room, so the assumptions belong to them too. A number the CFO did not help shape is a number the CFO will not spend against.