Data intelligence,
decisions from data.
Modern data platforms, semantic layers, self-serve BI, embedded analytics and decision science, engineered as a product, not delivered as a stack of dashboards.
Analytics has one job: move a decision. Programmes that ship value are the ones that treat the decision as the deliverable, and treat the platform, semantic layer and governance as the substrate that makes the decision defensible.
Frame the decision
Which decision are we trying to move, and what does 'moved' look like? Everything else follows.
We work backwards from the decisions the business actually makes, who acts on the number, when, with what confidence. The analytics stack follows.
Dashboards without a decision behind them are how analytics teams lose the mandate. We don't run that pattern.
The highest-leverage investment in most analytics programmes is a semantic layer nobody argues with.
Model & govern
Warehouse-native modelling with dbt. A semantic layer nobody argues with. Governance and quality as first-class engineering.
Modelled data with tests, freshness SLAs and lineage. One definition of every business metric, expressed as code, served everywhere.
Cataloguing, access, masking and consent designed alongside, because retrofitting governance is expensive.
Ship insight
Self-serve BI, embedded analytics and decision science, engineered against outcomes, not dashboards.
Governed self-serve on Looker, Tableau, Power BI. Embedded analytics inside your product where they change the customer decision. Decision science engineered against the workflows they inform.
Every surface ties back to the decision it was designed to move.
Operate
Data quality, cost and adoption monitored as ongoing engineering. Enablement designed in.
Data incidents are triaged like software incidents. Cost per query, per workload, per team is attributed. Adoption is measured against the decisions moved, not the dashboards viewed.
The team using the platform tomorrow is not the team that built it, because enablement was engineered from day one.
Toolkit
Framework, platform and vendor selection is driven by the shape of the work and the team who'll own it, not by whichever ecosystem is loudest this year. The list below is representative, not prescriptive.
- Warehouse
- Snowflake · Databricks · BigQuery · Redshift
- Modelling
- dbt · SQLMesh
- BI
- Looker · Tableau · Power BI · Superset
- Semantic
- Cube · LookML · dbt Semantic Layer
- Quality & governance
- Monte Carlo · Great Expectations · OpenLineage
Selected work
What clients ask us first
01Why insist on a semantic layer?
Because in most organisations, 'revenue', 'active user' and 'margin' quietly mean different things in different dashboards. A metrics layer expresses those definitions once, in code, and serves the same numbers everywhere. Highest-leverage investment in most programmes.
02Warehouse or lakehouse?
Warehouse-first is usually the fastest path to trusted analytics. Lakehouse gets pulled in where ML, unstructured data or open-format economics earn it. Components should be swappable, not one-way doors.
03How do you handle data quality?
As engineering, not clean-up. Data contracts, tests in CI, freshness and volume SLAs, lineage that traces a number to the source. Incidents get triaged and post-mortemed like software incidents.
04Where does AI fit?
As a way of using the data, forecasting, propensity, anomaly detection, and as a way of interacting with it, natural-language querying, semantic-layer-aware assistants. Value is bounded by the governed data model beneath.
Let's discuss your data & analytics programme.
Share a brief overview of the decisions you're trying to move, we'll arrange an introductory conversation with the relevant practice leadership.