The challenge
Data was spread across many systems and departmental repositories, each with its own definitions. Teams often produced different answers to the same question, which eroded trust in reporting and slowed decisions.
Analysts spent much of their time locating and reconciling data rather than analyzing it. Without clear governance, it was hard to know which figures were authoritative or how they had been derived.
The company wanted a governed, enterprise data foundation that could serve analytics and reporting consistently while respecting the strict controls required in a regulated industry.
Technology landscape
Numerous source systems across commercial, operational and research functions, with overlapping data and inconsistent definitions.
Departmental data marts and spreadsheets built independently, leading to divergent metrics and duplicated effort.
Regulatory and data privacy requirements that demanded strong governance, lineage and access control over any central platform.
Our approach
We defined a governed data architecture with a clear model, agreed definitions for key metrics and explicit ownership, so the platform would be trusted rather than becoming another silo.
We prioritized high value data domains that underpinned the most important reporting, establishing patterns for ingestion, quality and lineage that later domains could reuse.
We worked with data stewards across functions to agree definitions and controls, embedding governance into the platform rather than treating it as an afterthought.
Implementation
We built the cloud data platform with layered ingestion, transformation and curated zones, applying data quality checks and capturing lineage so figures could be traced back to their sources.
We onboarded priority domains, consolidating divergent definitions into agreed standards and retiring duplicative departmental extracts where the central platform could serve the need.
We implemented role based access aligned to governance policy and enabled self-service reporting on the curated layer, with training to help teams use the trusted datasets correctly.
Outcomes
- Teams increasingly drew on consistent, governed datasets, which reduced the conflicting numbers that had undermined confidence in reporting.
- Analysts spent less effort locating and reconciling data and more on analysis, improving the value of the insight they delivered.
- Clear lineage and access controls strengthened confidence that reporting met the governance expectations of a regulated environment.
- Reusable ingestion and quality patterns made it faster to onboard new data domains as needs grew.