Overview
Who it is for
Dashboards and analytics are for leaders who have stopped trusting the numbers. Reports built by copy-paste. Two versions of the truth from two teams. A weekly meeting that starts with an argument about whose spreadsheet is right. A BI tool that was bought with good intentions and now shows twelve dashboards nobody opens.
It is also for operations teams who need to see what is happening now, not last month: orders in flight, exceptions waiting, machines down, trucks late, tickets aging. The people who run the business on Monday morning.
What we deliver
One source of truth, and dashboards built on top of it:
- A data model built around the business’s real entities and designed to survive the next reorg.
- A warehouse or operational store fed reliably from the systems you own, with the pipelines monitored and the loads recorded.
- Defined metrics. Each metric named, defined, owned, and documented, so that every dashboard shows the same number for the same question.
- Operational dashboards that leadership will actually open, built in the tool your team already uses where possible.
- Real-time views where the decision needs them, batch where it does not, chosen per metric rather than by default.
- Alerts on the numbers that matter, so the dashboard tells you when to look at it.
- Documentation that explains where each number comes from, in language a new leader can follow.
The deliverable is not a set of charts. It is the end of the argument about which number is right.
How the engagement runs
- Listen. A 60-minute call about the decisions the business makes weekly and which numbers it does not trust. A one-page scope within 48 hours.
- Discover. We trace the three most contested metrics back to their sources, define them with the people who own them, and build a working slice of the data model with one dashboard on top. The sprint ends with a metric catalog, a data model, and a build plan.
- Build. Pipelines, model, and dashboards land week by week, each metric reconciled against the existing spreadsheet until the business trusts the new number more than the old one.
- Hand off. An internal owner runs the pipelines and owns the metric catalog for two weeks while we taper, then a 90-day standby.
Typical stack
A managed cloud data warehouse or a well-indexed relational store, depending on volume; pipelines built as code with scheduling, testing, and alerting; a transformation layer that keeps metric definitions in version control; and the BI tool your team already has, or a mainstream one if you have none. For real-time views we use streaming only for the metrics that need it, feeding a small operational store that the dashboard reads directly.
Everything is in your repository and your cloud account. The definitions are text files your analysts can read and change through review.
The Monday morning test
A dashboard has worked when a leader opens it on Monday morning without being asked to, understands it without a walkthrough, and makes a decision from it. We design toward that test: fewer charts, named metrics, the exceptions up front, and the detail one click away. We also instrument usage, so you know which dashboards are opened and which ones can be retired.
Dashboards depend on data that moves reliably. Integrations covers the feeds, and workflow automation covers the processes that generate the numbers in the first place.