DATA MANAGEMENT AND GOVERNANCE
Data management and governance services
Our data management and governance work establishes ownership, catalogues, lineage and quality controls so teams can find, use and explain their data.
- A catalogue so people find and trust data instead of rebuilding it
- Automated lineage, so any number can be explained and impact-assessed
- Data contracts enforced in CI, so upstream changes cannot break you silently
Each failure is preventable. None is prevented by good intentions.
They are prevented by controls that are owned, automated, and enforced. Governance is the trust layer under everything else. It makes data democratisation safe, keeps data engineering pipelines accountable and is a prerequisite for any system where AI acts on your data. It is owned durably through a cloud centre of excellence. Governance works when it is built into the platform, not bolted on as policy PDFs. We implement four control layers that operate together, with classification, stewardship, and a policy model around them.
No single source of truth
Two teams, two definitions of "revenue," and decisions that stall in reconciliation arguments. A catalogue gives one owned definition.
Invisible lineage
No one can trace a number to its source, so errors go undetected and audits drag on. Lineage explains and impact-assesses any change.
Silent quality decay
Nulls, duplicates, and stale loads slip through, and dashboards and models quietly go wrong. Quality tests alert before bad data lands.
Unmanaged access
Everyone can see everything, or no one knows who can, and compliance exposure follows. Classification and access controls close the gap.
Broken producer trust
An upstream schema change breaks ten pipelines, and firefighting replaces delivery. Data contracts hold producers accountable.
Undefensible AI
A model trained on unexplained data makes decisions no one can defend to a regulator. Governed data makes AI accountable.
Make your data an asset you can trust and defend.
Data management and governance deliverables.
- Governance operating model: roles, decision rights, and stewardship that make ownership real.
- Catalogue and lineage established on your platform, populated and integrated with delivery.
- Data quality framework: rules, automated tests, and monitoring wired into pipelines.
- Data contracts between key producers and consumers, enforced in CI.
- Access and classification controls aligned to privacy and compliance requirements.
Put ownership and controls into practice.
Assess
Find where trust breaks today: definitions, quality hotspots, access risk, and lineage gaps.
Prioritise
Sequence the domains and controls that carry the most risk and value first.
Implement
Build catalogue, lineage, quality and contracts as platform capabilities, not documents.
Enforce
Run governance through automation and CI, so it works without slowing teams.
Steward
Own it with a dedicated team, or place it with a [cloud centre of excellence](/cloud-center-of-excellence).
Four control layers, built into the platform, operating together.
A representative view by layer. We build on your platform and existing tooling where it is sound rather than replacing it.
Governance is a hard prerequisite for any system where AI acts on your data, keeping every decision explainable and defensible.
The trust layer under everything else.
Data Engineering
The pipelines governance keeps accountable, with quality and lineage on every table.
Data Democratization
Self-service that governance makes safe by giving people data they can trust.
Data Streaming and Real-Time Analytics
Real-time data held to the same catalogue, quality and contract standards.
Cloud Centre of Excellence
The team that owns governance standards durably across the estate.
Databricks
Where Unity Catalog provides catalogue, lineage and access control in one place.
Data and Analytics Services
The parent practice this trust layer belongs to.
What data leaders ask us first.
Not when it is automated. Catalogue, lineage, quality tests and contracts run inside the platform and CI. They reduce the firefighting that slows delivery.
Yes. A warehouse stores data; a catalogue makes it findable, trustworthy and governed. Without one, people rebuild datasets they cannot find or trust.
Enforced agreements on the schema and quality a producer guarantees to consumers. They stop an upstream change from silently breaking downstream pipelines and models.
Put catalogue, lineage, quality and contracts in place, so teams can trust the data and explain every decision.
Four control layers built into the platform and enforced in CI, so governance runs without slowing teams.