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    Data governance and quality

    Reports agree on the number because ownership and definitions are fixed upstream. We implement domain owners, lineage and automated quality gates inside the data platform you already run.

      Data & Analytics
      1 Foundational → Developing
      2 Developing → Scaling
      3 Scaling → Advanced
      Why it matters

      Every meeting starts by arguing about the number

      Poor data quality is cited by 68% of executives as the main barrier to adopting AI. Definitions drift between teams, no one owns the domain, and monthly clean-up scripts correct the output while the broken ingestion rule that produced it stays in place.

      Quality only holds when someone is accountable for it. A named owner per domain, lineage that traces a figure back to its source system and quality gates that reject bad records before they reach a dashboard or a training set turn cleaning into prevention. The decision you make is not whether to clean the data, but where in the pipeline you stop accepting it broken.

      68%of executives cite poor data quality as the top barrier to AI adoption
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      Is this for you?

      What breaks trust in data, and what restores it

      Five patterns behind an estate nobody quite believes.

      No owner per domain. Everyone consumes the data and nobody is accountable for it being right.

      Definitions that differ. The same metric means three things depending on which team calculates it.

      Quality checked manually. Problems are found by whoever notices something odd in a report.

      No lineage. When a number is questioned, nobody can reconstruct where it came from.

      Governance as bureaucracy. A committee that approves requests and slows everyone down.

      How quality is enforced

      Gates, not clean-up campaigns

      Quality applied where the data moves, so problems are caught at the source instead of discovered in a report.

      Define

      Ownership, definitions and the quality rules that matter per domain

      Validate

      Automated checks on freshness, completeness and consistency

      Trace

      Lineage from source to consumption, so any number can be explained

      Operate

      Issues routed to the owner, with a cadence that keeps the register alive

      Deliverables

      What you take away

      Governance implemented in your data platform, not filed as a policy document. Not for organisations without a consolidated data platform: governance rules need somewhere to run and a catalogue to register them.

      01

      Governance framework

      02

      Quality rules in production

      03

      Catalogue and lineage

      04

      Ownership and review cadence

      The engagement

      What working together looks like

      DurationAgreed after the quality and ownership review; framework definition first, then implementation
      TeamData architect, data engineer and a governance lead, working with your domain analysts
      What we need from youRead access to source systems and the data platform, plus the business owners of the disputed metrics
      What you take awayA governance framework, a domain ownership and lineage map, and quality rules running in your platform
      Not includedData platform migration, master data consolidation, licence costs and permanent stewardship of your domains
      Recommended next step

      Start with a governance review

      Two working sessions with your data and business owners. You leave with the disputed metrics mapped, their owners named and the quality gaps ranked.

      Book the governance review