Applied AI
Data Analytics
Overview
Analytics works when the numbers agree and the dashboards answer a real question. We start with the decision and work backwards to the data.
Outcomes we measure
- Dashboard adoption and active users
- Time to answer a new business question
- Metrics defined once and reused
- Report production hours saved
Problems we solve
Sound familiar?
- 01
Every department has its own revenue number.
- 02
Analysts spend most of their week producing the same reports by hand.
- 03
Dozens of dashboards exist and nobody knows which ones are still correct.
What we deliver
Concrete deliverables, not slideware.
Metrics layer
Business definitions as code — one place to define revenue, churn or utilisation.
Dashboards and reports
Designed around decisions and owners, with performance and accessibility in mind.
Self-serve enablement
Curated datasets, documentation and training so teams answer their own questions.
Report automation
Scheduled, validated distribution replacing manual spreadsheet work.
How it works
From first call to production.
- 01
Decision mapping
Which decisions, which owners, which metrics.
1–2 weeks
- 02
Metric definitions
Agree and codify definitions in the semantic layer.
2–3 weeks
- 03
Build dashboards
Iterate with users on real data.
3–6 weeks
- 04
Enable
Training, office hours and a retirement plan for stale reports.
Ongoing
Tech & frameworks
Tools we use — in your stack.
- Power BI
- Tableau
- Looker
- dbt Semantic Layer
- Cube
- Metabase
- Snowflake
- BigQuery
- Databricks SQL
Related projects
Where we've done this.
Case studies
Case studies for this service are being prepared. Ask us for references on a call.
FAQ
Frequently asked questions
Applied AI
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Scope it with a senior practitioner
Book a 45-minute readiness call.
Where you are, what to fix first, and what it would take.
