Data & Intelligence
Dashboards
We build dashboards your leaders and teams open when a decision is due. Every screen maps to a recurring decision, and every number to one definition.
The problem
You paid for a dashboard, and people still walk into meetings with their own spreadsheets.
Dashboards usually get built from whatever fields the connectors expose, so they show what data exists without serving any particular decision. They look complete at launch, then one figure disagrees with finance and trust goes quickly.
The usual response is a redesign, yet the visual layer is rarely the cause. Metrics calculated differently in each report, sources joined inside the BI tool and no owner when a pipeline breaks will sink a new design just as fast.
You may recognize this
- The same KPI shows two values in two reports.
- Someone assembles the weekly report by hand from several exports.
- Every new question becomes a ticket for the one analyst.
How we work
Map decisions to screens
We list recurring meetings, who decides what in each, and what they need to see. That yields executive, operational and diagnostic tiers, each with a named owner.
Model the data first
Sources land in a warehouse such as BigQuery and move through staging and mart layers. Metric logic lives there once, so the BI tool only displays it.
Build in trust signals
Every page shows a last-refreshed time. Automated freshness and row-count tests alert a named person when a pipeline stalls, so a stale number can't pass as current.
Retire what isn't used
After launch we review usage logs with team leads and cut pages nobody opens.
What we deliver
- Decision-to-screen map
- Metric definitions sheet
- Warehouse models and transformation code
- Executive, operational and diagnostic dashboards
- Access and row-level permission matrix
How we measure
- Weekly active viewers: shows the work is in use, which delivery alone can't tell you.
- Variance against finance: the basic test of whether leaders can trust the page.
- Hours spent on manual reporting: the direct operating saving.
A services firm with regional branches
Illustrative scenario: Consider a home services company with several regional branches. Bookings live in a job system, revenue in accounting software, and each manager keeps a private spreadsheet. We'd join those sources in one model, give managers a weekly view of their own branch, and give the owner one page that ties to the accounting close.
Common questions
Which BI tool should we use?
Looker Studio is a low-cost start for marketing-heavy reporting. Power BI tends to fit finance-led teams on Microsoft. Metric logic sits in the warehouse, so you can switch later without rebuilding it.
Do we need a data warehouse on day one?
Not with a few sources and simple joins. You'll want one once you need history beyond what a platform keeps, or shared metric logic across reports.
Who maintains it after launch?
Every dashboard gets a business owner and a technical owner. We hand over documentation and tests, and can stay on if you lack the capacity.
Before we work together, let's think together.
Requests are answered by the founder.