Metrics with one definition
Revenue, churn and margin mean the same thing in every report, enforced in code.
Data Analytics
Business intelligence
Governed metrics, self-serve dashboards and reporting your teams trust enough to stop exporting to Excel.
What we do
Business intelligence fails when every department computes its own version of revenue. We fix the definitions first: a governed semantic layer where each metric has one owner, one formula and one test, then build the dashboards on top in Looker, Power BI or Metabase. The result is reporting the whole company can argue from, not about.
We start from the decisions, not the data: which questions does leadership ask weekly, which numbers do teams argue about. The first governed dashboard ships within weeks on live warehouse data, and each following iteration retires another spreadsheet. Every number is validated against the old reports with the people who own them, so trust transfers instead of breaking.
The change is cultural as much as technical: meetings start from the same dashboard instead of dueling exports. Analysts stop reconciling numbers and start answering why they moved, and new questions are self-served instead of queued behind a ticket. We measure adoption, weekly active viewers per dashboard, and treat it as the success metric of the engagement.
Why it pays off
Self-serve for real
Teams answer their own questions from curated data models, without SQL and without tickets.
Dashboards people open
Views designed around decisions, so they get used on Monday morning, not just in the demo.
Trust you can audit
Every number traces back through lineage to its source, so disputes end with evidence.
Less analyst toil
Recurring reports are automated, freeing analysts for the questions that actually need them.
Data Analytics
How we work
01
Metric workshop
We catalogue the numbers that run the business, find where definitions diverge and agree owners and formulas.
02
Semantic layer
Definitions are encoded in dbt and your BI tool's model, tested and versioned like any other code.
03
Dashboard delivery
Dashboards ship in priority order, each validated against the old numbers with the people who use them.
04
Adoption and self-serve
Training, office hours and curated datasets until self-serve is the default and exports are the exception.
Deliverables
Governed metric catalogue with owners
Semantic layer encoded in dbt and BI
Self-serve dashboards for priority domains
Validation against legacy reports
Training and adoption playbook
Questions, answered
Which BI tool do you recommend?
The one your team will actually use. Looker for a strong semantic layer, Power BI in Microsoft shops, Metabase when speed and cost matter. We work in all three and advise against a switch when yours already fits.
Our reports disagree today. Where do you start?
With the two or three metrics that cause the most arguments. We trace each conflicting report to its source, agree one definition with the owners and encode it in the semantic layer so the fight ends.
Can we still use Excel?
Yes, as a consumer. Analysts can pull governed data into spreadsheets for one-off work. What ends is Excel as the source of record for company numbers.
End the spreadsheet arguments
Show us the two reports that never match and we will show you how to make them agree.
Book a discovery call