Analytics that leaders actually act on.
Most dashboards get built once, admired in a meeting, and never opened again. I build analytics, dashboards, and predictive models the other way around — starting from the decision someone actually has to make, then working backward to the data that should change their mind. Fifteen-plus years in marketing and data analytics, more than $50M in ad spend tracked to the dollar, taught me the same lesson every time: if a number can't be traced to an action, it isn't insight, it's furniture.
Ask most companies what their reporting stack is for and you'll get a shrug or a list of tools. Ask what decision it changed last quarter and the room goes quiet. I spent years managing eight-figure ad budgets where every dollar had to justify itself in a number someone could defend — that's the standard I hold analytics work to now. A chart that doesn't move a decision is a cost center with good typography.
I build the reporting and forecasting layer around the call it's meant to inform: spend it here or there, staff up or hold, chase this segment or drop it. If nobody's decision changes when the number moves, the number shouldn't exist.
What decision intelligence actually covers
This isn't "put data in a chart." It's three connected disciplines that only pay off together — miss one and the other two are just decoration with better math.
- Analytics & dashboards — reporting built from the decision backward, with clean data pipelines, real definitions, and no vanity metrics that can't be defended in a room.
- Predictive models — forecasting and scoring that gives leaders a number before the outcome happens, not a postmortem after it's too late to act.
- Decision workflows & instrumentation — the thresholds, alerts, and owners wired around the metric, so a number crossing a line actually triggers an action instead of a shrug.
Data that doesn't change a decision is decoration. I don't ship a dashboard until I know exactly which decision it's meant to move, and I instrument it so we can prove whether it did.
Four pieces, one decision.
From spreadsheet chaos to a number you trust.
Name the decision, then the number
We start with the call someone actually has to make, agree on the metric that should drive it, and rule out every vanity number that won't.
Build the pipeline and the model
Clean data pipelines, real definitions, and any forecasting or scoring model the decision needs, validated against your own history before it ships.
Attach a threshold and an owner
Every dashboard gets a workflow behind it — a threshold, an owner, and an escalation path, so a number crossing a line does something.
Check the decision, not the chart
We track whether the decision actually changed when the number moved, and we keep tuning the model or the dashboard until it does.
Let's build a dashboard that changes a decision.
If you've got reporting nobody trusts or a call being made on gut feel, that's where we start. I'll tell you which number should be driving it before we build anything.
