Solution
Agreed metric definitions, modelled tables and a small number of views tied to decisions, so a meeting argues about the action rather than the number.
Overview
Reporting is a definition problem wearing a visualisation costume. When two departments quote different revenue figures, no chart library fixes it.
We start by writing definitions, build the model that produces them consistently, then create the smallest set of views that change what someone does.
The result is fewer dashboards than most organisations expect and considerably more trust in them.
Managers reconciling several systems before they can answer an operational question.
Start with a metric definition and its source records. Choose extraction cadence, join keys and handling for corrections. A dashboard should show data freshness and incomplete feeds rather than present every number as current.
In practice
Illustrative examples. Each one is built from the modules below.
Heating and plumbing company, 3 branches
The Monday view shows callback rate by technician against a 4% target. One technician is at 6%, and six of his eight callbacks involve the same thermostat model. That is a training fix, found in a week instead of a quarter.
What goes in
Travel agency group, 40 agents
Sales counted a booking when it was made. Finance counted it when the trip was taken. After one written definition, both teams quote the same monthly figure, and the dashboard shows margin by agent with cancellations taken out.
What goes in
Wholesale distributor
The owner gets a 7 AM email with yesterday’s orders, items below their reorder point and invoices past 60 days. The numbers match the dashboard exactly, and nobody has to remember a password.
What goes in
Modules
Each measure defined once with its filters, time basis, and exclusions, published where anyone can read it.
Incremental extraction from operational databases, software platforms and files, with checkpoints and retry handling.
Raw, cleaned and presentation layers, with conformed dimensions for customer, product, location and date.
Point in time reporting where the business needs to see what was true then, not only what is true now.
Freshness, row count, null rate and referential checks that block publication rather than publishing a wrong number quietly.
Executive summary, operational views for each team, and exception reports that only appear when something needs attention.
Reports delivered by email on a schedule for people who will never log in, with the same numbers as the dashboard.
Documented tables, saved queries and a data dictionary so new questions do not require a project.
Architecture
A typical implementation flow, with explicit responsibilities at each boundary.
Load source changes with a watermark and retained lineage.
Apply tested definitions, dimensions and reconciliation checks.
Expose reports with drill-through records and visible freshness.
Security and controls
Restrict row and column visibility by role. Minimize personal data in reporting stores and protect exports. A BI account should not inherit operational write permissions.
We select the final stack after reviewing your existing systems and operating requirements.
Security and IP practicesFit
FAQ
Fewer than you think. One executive view, one per operational team, and a set of exception reports that stay silent when things are fine. Anything nobody acts on is maintenance cost.
Yes. The modelled tables are the deliverable and any tool that speaks SQL can read them, which also means changing tool later is not a rebuild.
One definition per metric, computed in the model rather than in each report. If a team needs a variant, it becomes a separate named metric with its own definition.
The quality gates catch it, usually as a null rate or referential failure, and the load stops before the wrong figure reaches a dashboard.
Yes, where the business needs it. Point in time modelling is a design decision because it changes the table structure, so we agree it early.
Usually not. At moderate volume, managed PostgreSQL with modelled tables is cheaper, simpler and fast enough. We size it against your real data.
Tell us where the current process breaks down. We can map the workflow, examine the integration requirements and define a practical scope for the build.