Turn Raw Data Into
Decisions That Drive Revenue.

Not necessarily — but for most organisations processing more than a few million rows or with more than a handful of analytics users, a cloud data warehouse delivers significantly better performance, reliability, and cost-efficiency than on-premises databases or spreadsheets. Snowflake, BigQuery, and Redshift each have different strengths. We evaluate your workload, query patterns, existing cloud investments, and team capabilities to recommend the right platform — or to confirm that your existing setup is adequate if it is.

dbt (data build tool) is the standard for analytics engineering — it allows you to transform raw warehouse data into clean, modelled, tested, and documented datasets using SQL and version control. Before dbt, transformation logic lived in undocumented stored procedures, one-off scripts, and Excel formulas that nobody could audit or test. With dbt, every transformation is a versioned, tested, documented model that can be reviewed, deployed, and rolled back like software code. If your data team is not using dbt, you probably have transformation logic you cannot fully trust.

A focused single-domain data platform — one data source, one warehouse, key dashboards — can be delivered in 6 to 10 weeks. An enterprise data platform covering multiple source systems, a governed semantic layer, and self-service analytics typically takes 4 to 6 months. We always phase delivery so you have working analytics outputs within the first 4 to 6 weeks, even for larger programmes — business users should see value long before the full platform is complete.

Yes — and usually significantly. Snowflake, BigQuery, and Redshift bills that have grown without active management typically have 30% to 60% waste from inefficient queries, over-sized warehouses, unpartitioned tables, and missing result caching. We run a FinOps assessment that identifies the specific waste and the specific fixes — query rewrites, clustering keys, warehouse policies, and result cache configuration. We typically recover our engagement cost in reduced infrastructure spend within the first quarter.

A dashboard is a visualisation. A data product is a reliable, governed, documented data asset that business users can trust without calling a data analyst to validate it. Most organisations have many dashboards and no data products — which is why dashboard adoption is low and data teams spend most of their time fielding ad-hoc requests rather than building new capability. We build data products: defined metric logic in a semantic layer, automated quality testing, documented lineage, and governance that makes the data trustworthy before it reaches a visualisation.

We work alongside existing data teams — either as specialist delivery resource for a specific project (building a new data platform or migrating to dbt), or as advisory resource helping the team adopt better practices and tooling. We do not replace data teams; we enable them to work on more valuable problems by solving the infrastructure and engineering challenges that consume disproportionate time. Many of our engagements end with a structured knowledge transfer so the internal team can operate and extend what we have built independently.

Dashboard adoption is a design and training problem, not a technology problem. We design dashboards around the specific decisions each audience needs to make — not around all the data we have available. We run stakeholder workshops before building anything to establish the key questions each dashboard needs to answer, the right level of detail for each audience, and the definitions of success. We also run dashboard training sessions and produce usage documentation. We track adoption metrics after launch and iterate on designs that are not driving the decisions they were built for.