Data intelligence
Data intelligence and analytics services
Your dashboard is a rear-view mirror with very good resolution. The useful work starts one step later: what to do about what it shows, while there is still time.
The problem
Visibility was solved. Acting in time was not
Two decades of business intelligence made the numbers visible. The report still arrives after the month it describes, the insight still goes into a deck, and three weeks later the conditions have changed. More dashboards do not close that gap.
How we work on this
Prototype first, then the build you approved.
We begin with the decision you want to make differently and work backwards to the data it needs. That usually means joining a few systems, agreeing what the measure actually means, and putting the answer where the decision happens rather than in a separate reporting tool. If the history cannot support the question, you hear that first, before anyone spends.
What this covers
Capabilities, and where each one stops.
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Operational reporting
The numbers that drive a weekly decision, current enough to act on and agreed on definition.
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Forecasting
Where enough history exists, a forward view with its uncertainty stated rather than hidden.
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Data readiness assessment
An honest answer on whether your data can support the question, before a project is built on the assumption that it can.
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Metric definitions
Agreeing what a term means across departments, which is often the real blocker rather than the technology.
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Analysis in the workflow
Answers surfaced inside the tool where the decision is made, not in a separate portal nobody opens.
If your problem is that nobody can see the numbers, a better dashboard is the cheaper answer and we will say so. Our work starts when you can see the number and still cannot act on it in time.
How it is built
Decisions we make the same way every time.
- Built on your existing systems and exports
- Definitions documented and agreed before dashboards are built
- Uncertainty shown, not smoothed away
- Open export of every dataset we produce
- No shared or public model trained on your data
Where this applies
Industries we have gone deep in, and the rest of the ecosystem.
Questions
The questions people ask about this work.
We already have Power BI and an analyst. Why would we need this?
You may not. If the problem is visibility, buy a better dashboard. Our argument starts one step later: you can see the number, it still takes three weeks to act on it, and that gap is costing you.
How much history do you need?
It depends on the question. What blocks a forecast is usually not messy data but too little history for the thing you want to predict, or never recording the outcome you care about. We check that before you commit.
All questions, including the ones that might lose us the deal
Tell us the decision you keep making late
Describe the decision and the systems it depends on. We will tell you whether your data can support it.