Your data is scattered across CRM, finance, operations and marketing platforms. We bring it together into a single, reliable source of truth — so you can report, analyse and act with confidence.
When your sales data is in your CRM, your financial data is in accounting software and your operational data is in a third system, you can't get a complete picture of the business.
If producing a management report requires hours of copying data between spreadsheets, you're wasting time and introducing errors.
Machine learning and advanced analytics require clean, centralised, well-structured data. Without a proper data foundation, AI initiatives will fail before they start.
From raw data to reliable business intelligence.
Dimensional modelling, schema design and data architecture that supports both current reporting needs and future analytical requirements.
Automated extract, transform and load pipelines that move data from your source systems into the warehouse — reliably, on schedule and without manual intervention.
BigQuery, Snowflake and Amazon Redshift — vendor-agnostic platform selection, setup and configuration matched to your infrastructure and team.
Looker, Power BI and Metabase — dashboards and reports built to surface the metrics your team actually needs, in a format they can use.
We map your current data landscape — source systems, data quality, reporting requirements and gaps — to define what needs to be built and in what order.
A data architecture document covering platform selection, schema design, pipeline approach and the phased delivery plan — agreed before any build begins.
Warehouse setup, ETL pipeline development and source system integrations — delivered iteratively with regular checkpoints to validate data quality and completeness.
Dashboards and reports are built and handed over — with documentation and training so your team can use, adapt and extend them independently.
One version of the data — no more arguments about whose spreadsheet is correct.
Data flows automatically from source to warehouse — no manual downloads or copy-paste.
Dashboards that reflect current data — not yesterday's export or last week's batch run.
Built on cloud platforms that grow with your data volumes — no hardware to manage.
Clean, structured, centralised data — the prerequisite for any serious AI or ML initiative.
Free your team from manual reporting so they can focus on analysis and decision-making.
A data warehouse is a centralised repository that brings together data from multiple source systems — your CRM, ERP, e-commerce platform, finance software and more. Unlike a transactional database, it's optimised for querying and analysis, enabling fast, reliable reporting across your entire business.
Transactional databases (like those behind your CRM or ERP) are optimised for writing and retrieving individual records quickly. A data warehouse is optimised for reading large volumes of data, aggregating it and answering complex analytical questions — like "how has revenue trended by product line over the past three years?"
The right choice depends on your existing infrastructure, team skills and budget. BigQuery (Google), Snowflake and Amazon Redshift are all mature options. We recommend based on your specific situation — we're not tied to any vendor.
A first phase — covering the key data sources and an initial set of dashboards — typically takes 8–16 weeks depending on the complexity of your data landscape and the quality of source data. We work iteratively, delivering value early rather than waiting for a big-bang launch.
Start with a data audit. We'll map your current data landscape and show you what's possible.
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