Service
Data analytics: one metric definition your team actually trusts
We fix number disagreements, build a single semantic layer, and make BI dashboards stop being something people argue about in meetings.
- Apache Superset
- dbt
- Metrics layer
- SQL
Why this matters
If the same metric is calculated three different ways in three places, that's not a dashboard problem — it's a semantics problem. Each team has its own definition of "active user", "revenue" or "order", and half of every meeting is about whose number is actually right.
We walk the data flow end-to-end: where numbers diverge, where the logic breaks, how sources and metrics connect. You leave with one semantic layer, one definition per key metric, and BI that doesn't lie.
What we do
Four directions of work
- 01
Data and metric audit
We trace sources to dashboards, find every diverging number and its root cause: wrong join, lost update, two different definitions of the same metric.
- 02
Semantic layer
We build canonical metric definitions (in dbt or in the BI semantic layer), align wording with the business, and pin ownership so "whose metric is this" has an answer.
- 03
BI stabilization
We fix the dashboards you already have: remove duplicates, standardize filters and period logic, wire key reports to the semantic layer — without "let's rebuild everything".
- 04
Self-serve analytics
We set things up so business teams can slice the data they need themselves: clean data models, a metrics catalog and dashboard templates instead of tickets to analytics.
Stack
Teams arguing about numbers?
Let's map where your metrics break — and how to land one definition
On the call we look at which metrics are contested, where their logic lives and what your BI stack is. You leave with an audit scope and a plan to consolidate — without "rewrite everything".
- Map of key metrics and their sources
- Root cause of diverging numbers
- Semantic layer model
- BI stabilization plan
- Path to self-serve analytics
On the call we look at which metrics are contested, where their logic lives and what your BI stack is. You leave with an audit scope and a plan to consolidate — without "rewrite everything".