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Data governance & quality
Data governance and data quality consultancy
Know who owns your data, what it means and whether it can be trusted. Konsolute sets up data ownership, classification, access and quality rules, maps lineage and implements Microsoft Purview and Microsoft Fabric governance where they fit.
WHAT THIS HELPS YOU ACHIEVE
Data with clear meaning, accountable owners and visible quality
Connect governance decisions to the controls and working practices that make data understandable and appropriate for its intended use.
Know who is accountable
Define owners, stewards and decision rights for the data products and business terms that matter.
Make data easier to understand
Document meaning, origin and dependencies so consumers can assess whether a dataset fits their task.
Expose quality issues
Use agreed checks, thresholds and issue records to identify problems and prioritise corrective action.
Control use and change
Define access, sharing and lifecycle responsibilities, with reviews and evidence that continue after implementation.
OUR DATA GOVERNANCE & QUALITY SERVICES
Data governance, Microsoft Purview and quality improvement
Start with a priority domain, assess an existing governance approach or implement the controls and working practices needed for a wider data programme.
Governance assessment & operating model
Review the data estate, existing policies, ownership and current practices. Prioritise the material gaps and define decision rights, review forums and responsibilities across business and platform teams.
Data ownership & business definitions
Assign owners and stewards to domains, data products and critical information. Build a business glossary and definition standards, including who approves changes and resolves conflicting meanings.
Data cataloguing & lineage
Assess metadata discovery and lineage across sources, pipelines, models and reports. Curate useful asset descriptions, with scanning and maintenance owned, rather than assume every dependency is found.
Classification & access governance
Map information sensitivity and permitted use to the right controls. Define access approval and review, assessing labels and platform permissions separately so metadata is not mistaken for enforcement.
Data quality rules & remediation
Profile priority datasets and agree validation rules around business needs. Define thresholds, exception owners and correction routes, linking quality results to the assets and people they affect.
Fabric & Purview implementation
Configure agreed governance and quality capabilities in Microsoft Fabric and Microsoft Purview. Validate sources, regions, licences and access, then hand over administration, monitoring and review.
CAPABILITIES & DELIVERY SCOPE
Governance translated into decisions, evidence and action
Ownership, metadata and quality checks need to connect to how people find, use and change data. These workstreams establish the responsibilities and implementation behind the policy.
Domains, ownership & stewardship
Define the business areas and data products in scope, with named decision makers. Separate accountability for meaning and use from responsibility for pipelines and platforms, and agree how exceptions and risks are escalated.
- Domains and priority data products
- Owner and steward responsibilities
- Decision and escalation routes
Business glossary & data contracts
Agree definitions for critical terms, identifiers and measures with the people who use them. Record source authority, expected use and limits, and define how changes are approved so technical teams build to a stable contract.
- Business terms and source authority
- Definitions, usage and limitations
- Approval and change responsibilities
Asset discovery & metadata curation
Identify the assets that matter for the priority outcomes. Assess scanning, credentials and metadata access, then curate names, descriptions and owners, recording coverage gaps rather than treating the catalogue as complete.
- Asset inventory and metadata scope
- Supported scans and connection access
- Curation ownership and coverage gaps
Lineage & impact analysis
Map source-to-consumer dependencies using automated lineage, plus documentation where scans fall short. Validate key transformation and reporting paths, and agree how teams use the map to assess change and investigate issues.
- Source-to-consumer dependency views
- Lineage validation and missing links
- Change impact and investigation use
Classification, access & information use
Define sensitivity and intended use with accountable owners. Check how labels relate to the controls that actually enforce access at storage, processing and reporting layers, and test access paths with real consumers.
- Classification and permitted use
- Layer-specific access and sharing controls
- Access review and exception evidence
Profiling, quality rules & remediation
Profile completeness, uniqueness and validity, then set business-approved rules and thresholds. Define how failures are surfaced and corrected; accuracy may need reconciliation against an authoritative source.
- Profiling and agreed quality dimensions
- Thresholds and validation evidence
- Issue ownership and correction workflow
Microsoft Fabric & Purview configuration
Assess which built-in Fabric controls and Purview capabilities meet the requirement. Configure agreed domains, catalogue and quality features, recording source, region, licence and permission prerequisites and validating the result.
- Capability and licensing assessment
- Governance and quality configuration
- Source, region and permission validation
Monitoring, review & continuous ownership
Define recurring checks for stale metadata, missing owners, access and quality issues. Link results to named actions and review dates, notify affected consumers, and manage governance changes through controlled delivery.
- Quality and governance review cadence
- Consumer communication and issue backlog
- Administration and change ownership
TECHNICAL DESIGN DECISIONS
Make the policy enforceable and the quality result useful
Governance works when business decisions, platform controls and operational responsibilities are connected.
Metadata and enforcement
Classification and catalogue entries describe assets and how they should be handled. Confirm which controls actually enforce access and protection at each layer.
Coverage and confidence
Record which sources, transformations and consumers have been assessed. Partial discovery or lineage should never be presented as complete coverage of the estate.
Quality and business fitness
Agree what acceptable means for each dataset's intended use. A high rule-pass rate does not prove every value is accurate or that the data suits another purpose.
Accountability and correction
Assign the owner who can approve a definition or remediation decision. Administrators operate the controls but cannot settle every business dispute about meaning.
OUR APPROACH
How we deliver data governance and quality
Six stages start with one priority domain and leave you with named owners, tested controls and a review cycle that keeps going after handover.
Agree the governance outcomes
Identify priority domains, data products, risks and the decisions the engagement needs to support.
Assess assets and responsibilities
Review source assets, existing metadata, permissions, quality evidence and current ownership.
Define ownership and standards
Agree stewardship, definitions, classifications, quality rules and exception or approval processes.
Implement metadata and controls
Configure the supported Fabric, Purview and related platform capabilities within the agreed scope.
Validate access and quality
Test discovery coverage, lineage, relevant access paths and quality scenarios, documenting limitations.
Embed review and improvement
Hand over administration and establish the review cadence, issue backlog and accountable owners.
Own.Control.Review.
WHERE DATA GOVERNANCE CAN HELP
Start where unclear ownership or poor quality affects decisions
Governance can begin with a focused domain or critical dataset, then expand using the working practices and controls validated in that scope.
- Assign accountable owners and agreed definitions to critical business datasets
- Establish a curated catalogue with relevant source-to-report lineage
- Review classification and access before widening data sharing
- Profile quality issues and define measurable validation rules and remediation
- Implement suitable Fabric and Microsoft Purview governance capabilities
- Create recurring stewardship, access and quality reviews with clear action owners
See it in practice: Konsolute built a governed Azure data platform on a medallion architecture for Nuclear Waste Services, with validated data layers that give the organisation trusted data for reporting.
WHAT WE DELIVER
Governance assets your teams can use and maintain
The agreed scope defines the operating model, configuration and validation evidence needed for handover.
Ownership and standards
Domain and stewardship responsibilities, glossary terms, data definitions and approval or exception processes.
Catalogue and lineage records
The agreed metadata, asset descriptions and dependency views, with supported coverage and gaps recorded.
Controls and quality evidence
Configuration records, access validation, quality rules, profiling results and prioritised corrective actions.
Operating procedures
Administration guidance, review schedules, communication routes and an owned governance improvement backlog.
FAQ
Data governance and quality FAQs
Ownership, Purview, classification, lineage, quality and handover.
The scope can include assessment, ownership and stewardship, business definitions, metadata, lineage, classification, access review and quality management. We agree which outcomes need operating-model changes and which need configuration or engineering.
Governance defines responsibilities and decisions about how data is understood, used and maintained. Quality work assesses whether the data meets agreed requirements and how issues are corrected. The two connect through accountable owners, standards and operational review.
A tool can support discovery, metadata, controls and quality checks, but owners still need to agree definitions, acceptable use and correction decisions. We design the working practices and responsibilities alongside the technology implementation.
Yes, for the capabilities agreed in scope and supported by the relevant sources and environment. We assess Fabric’s built-in controls alongside Purview catalogue, governance and quality options, with prerequisites, licensing and configuration responsibilities recorded.
This service governs data in analytics platforms such as Microsoft Fabric and Azure. For Teams, SharePoint and Microsoft 365 workspace governance, Konsolute offers Reflekt, which automates ownership, reviews and lifecycle across the tenant.
Not in every scenario. Classification, labels and access controls have different roles, and enforcement depends on the capability, workload and configuration. We identify and test the actual controls instead of treating a catalogue classification as a universal access restriction.
We assess supported automated lineage and the dependencies required for the governance outcome. Where coverage is partial, we record gaps and add appropriate documentation or engineering tasks. Important paths are validated rather than assumed complete after scanning.
We define who can approve access, the intended use and the relevant platform permissions, with review and exception processes. Tests consider storage, processing and reporting access paths. A restriction in one layer is not assumed to secure every other route.
We profile the relevant data and agree dimensions, rules and thresholds with owners. Checks can address completeness, validity, uniqueness, consistency or timeliness, as appropriate. Accuracy may need reconciliation and business evidence rather than inference from a rule score alone.
We can assess causes and define remediation with source and business owners. Some corrections can be implemented through controlled engineering; others need changes in the source process or an approved business decision. Issues and consumer impacts remain visible during resolution.
Yes. A focused domain or data product can establish ownership, metadata and quality practices before expansion. The initial scope identifies the dependencies and controls needed, with acceptance evidence and a plan for extending coverage.
That depends on the Fabric and Purview capabilities, supported sources and execution requirements. We assess entitlements, consumption and implementation prerequisites for the proposed scope rather than assume all features are included in an existing subscription.
Business owners and stewards retain the agreed decision responsibilities, alongside platform and technical teams operating the controls. Handover defines review schedules, issue escalation, administration and ongoing support requirements so the work has accountable owners.
TAKE THE NEXT STEP
Where do ownership, access or quality need to be clearer?
Tell us which data is difficult to trust, which definitions are disputed or where sharing and change lack clear accountability. Konsolute will help define the governance, quality controls and implementation needed to make progress.