Shared metrics
Align teams on definitions and calculations.
Analytics & Business Intelligence
Numbers you can act on.
Shared metrics, useful dashboards and teams who can make sense of their data.
When can we help?
The situations in which clients most often call us on this subject.
Dashboards
Put useful information within reach of decision makers.
Self-service analytics
Help business teams explore trusted data.
Existing reporting
Simplify reports and make refreshes reliable.
What we deliver
Which decisions should your data support?
We start with the decisions you need to make, then define metrics, build analytical models and design reporting around your business teams.
- Shared metric definitions
- Analytical models and dashboards
- Handover to business teams
- Talk to us
The tools we use
We choose with you, based on what your team already knows how to run. No imposed technology.
Analyse and report
- Power BI
- Tableau
- Looker
- Google Analytics
Define metrics
- SQL
- DAX
- LookML
- Semantic modelling
Frequently asked questions
The technical questions we get in a first meeting, with our candid answers.
Do we need a new BI tool to get reliable numbers?
Rarely. In most audits the problem sits upstream: diverging definitions of the same metric, calculations redone in every dashboard, data that is never tested. We centralise calculation rules in a semantic layer (dbt models, versioned metrics) that Power BI, Tableau, Looker or Metabase then consume. Changing tools without that work just moves the problem.
How do we define a metric so it stops being disputed in meetings?
With one sheet per metric: business definition, exact formula, sources, filters, frequency, owner, and the edge cases settled (does a cancelled order count? as of which date?). The sheet is versioned next to the code that computes the metric, and a test checks that the two stay aligned.
Self-service BI: how do we give autonomy without creating chaos?
By separating what is certified from what is exploratory. Certified models (governed, tested, documented) are the only basis for official dashboards; business teams explore freely from those models, never from raw tables. An exploratory metric that gains importance goes through a short certification path, not an IT ticket.
What can generative AI bring to analytics?
Two uses hold up in production today: natural-language questions on certified data (text-to-SQL constrained by the semantic layer, never on raw tables) and generated dashboard commentary reviewed by a human. Without a reliable semantic layer the model produces plausible, wrong answers; that is why we start with the foundations.
Shall we talk?
A project, a question, a doubt about an architecture: email us or book a slot. The first conversation is for understanding, not selling.