The Challenge
Across the ecosystem, every team built its own data extracts and dashboards. The result was predictable: no two reports agreed, executives received conflicting numbers, and analysts spent more time reconciling sources than analysing them. There was no governed, trusted place to ask a question of the data.
The Solution
We built a governed data lakehouse that ingests data from across the platform into one store, curates it into trusted tables, and exposes it through a single semantic layer — so 'active customers' or 'cleared volume' means exactly one thing everywhere. Lineage and access controls make the data both trustworthy and safe to self-serve. The approach connects directly to data-driven decision making.
- One lakehouse ingesting data from every domain.
- Curated tables that are tested and documented.
- A semantic layer so every metric is defined once.
- Lineage and governance for trust and safe access.
Architecture
ELT pipelines land raw data in the lake, then build curated, tested tables. A semantic layer sits on top, defining metrics and dimensions centrally so every dashboard and model reads the same definitions. Lineage tracks each field back to its source, and access governance controls who can see what — the data-platform foundation behind our AI solutions work.
Technology Stack
Lakehouse storage, ELT pipelines, a semantic layer, data lineage, and access governance — delivered through our cloud solutions and enterprise software practices.
Results
Conflicting reports were eliminated — there is now one trusted source. Time to define and ship a new metric fell by roughly 65%, and the whole organisation works from a single governed catalog instead of a sprawl of private extracts.
Lessons Learned
The semantic layer is what turns 'one source of truth' from a slogan into reality — without it, you just centralise the disagreement. Governing access and lineage from day one avoided a painful retrofit. And self-serve analytics only earns trust when the underlying data is documented and tested.