Make Your Data Tell the Truth — Every Day, Automatically.
You probably have dashboards. The question is whether anyone trusts them.
Book a 30-Minute CallIt’s not a dashboard problem
Leadership meetings still devolve into debates about whose numbers are right. The analyst team is still rebuilding reports by hand on Friday afternoons. The dashboard exists, but when something important is happening, nobody checks it — they call the analyst.
This isn’t a dashboard problem. It’s a foundation problem. The rebuild fixes the layer that all of that is sitting on.
Four components, built as a system
- KPI framework redesign. A connected set of measures that trace back to your actual business objectives — what you’re trying to grow, what you’re trying to protect, what decisions each number is supposed to inform. This is often the most important conversation in the engagement, and it’s almost never a technical one.
- Data pipeline cleanup. Automated pulls from all source systems. No more manual exports. No more spreadsheets stitched together with VLOOKUP. Data comes from the source of record, automatically, on a defined schedule.
- Executive dashboards. Live. Self-updating. Built in the BI tool you already have. The metrics they’re seeing reflect the KPI definitions we agreed on — not whatever was easiest to pull.
- Analyst handoff. Full documentation. Your team knows how the pipelines work, what to do when something breaks, and how to add new metrics without calling me.
The architecture matters more than the vendor
I work in Power BI, Tableau, Looker, Salesforce, dbt, SQL, Python, and Excel. I don’t have a preferred vendor. You don’t need a new tool — you need someone to make the one you have actually work.
Real numbers from past engagements
At Paytronix, the reporting process for weekly business reviews was running 14 hours per cycle. After rebuilding the pipeline and dashboard layer: 3 hours. That’s 11 hours a week back for actual analysis, every week, indefinitely.
A national restaurant group’s month-end close was a five-day process. After the rebuild: same-day, from the first close after the system went live.
Six stages, in order
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Assessment
If not already done. Diagnostic of your current stack, reporting process, and KPI definitions before we write a line of code.
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KPI Definition
Align on what you’re measuring and why. Involves your leadership, your analytics team, your business objectives. This is often the most important conversation in the engagement.
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Data Architecture
Design the pipeline before touching tools. Define the data model, source systems, transformation logic, and refresh schedule. You review and approve before anything gets built.
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Build
Pipelines, data model, dashboards — built in sequence, with defined checkpoints where you review what’s been built before I move forward.
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Testing
Every number validated against source systems before anything goes live. You don’t take my word for it — the outputs are tested against what your team already knows to be true.
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Handoff
Full documentation. Training for whoever owns it internally. 30-day check-in after we’re done. You’re not dependent on me to keep it running.
Five things your team owns when I leave
- Defined KPI framework connected to your business objectives
- Automated data pipelines from all relevant source systems
- Live executive dashboards built on your existing BI tool
- Complete documentation for internal maintenance
- 30-day check-in after handoff
Three situations where this is the right engagement
- Companies that have outgrown their reporting but haven’t outgrown their tools
- Companies whose Power BI or Tableau deployment isn’t delivering
- Companies whose leadership meetings devolve into data debates
The fastest path is an assessment first.
So we’re building the right thing. Book a call and let’s talk about scope.
Book a 30-Minute Call