Data Governance & Quality
Data Quality Governance & Assurance
Establish clear accountability, assess how data-quality risks are governed, and strengthen the controls and evidence supporting reporting, research and operational decisions.
Governance, management and assurance of data quality — who owns it, how it is controlled and how leadership knows it can be relied on.
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When this service fits
For organisations whose reporting, funding, research or operational decisions depend on data, and who need clear ownership, workable processes and evidence that data-quality risks are being managed.
Reporting, funding or contract data is challenged or inconsistent
Nobody clearly owns the quality of critical datasets
A new platform or data warehouse needs quality governance designed in
Research or analytics outputs depend on data of uncertain reliability
An auditor, regulator or board has asked how data quality is assured
Data-quality work exists but is not evidenced or reported
The service
Governance and assurance, not data engineering
Poor data quality is usually a governance problem before it is a technical one: unclear ownership, inconsistent standards, processes nobody checks and no evidence leadership can rely on. We help establish accountability, policies and procedures, review how data-quality processes operate, and report on whether controls are working.
Our experience includes managing a specialist NHS trust’s payment-by-results data-quality process and its Data Quality team, and delivering the related policies, strategies and procedures. Approaches are adapted to each sector’s data and decisions; we do not assume identical delivery across every environment.
This service focuses on accountability, policies, controls, oversight and assurance of data-quality processes. Technical profiling, cleansing, data engineering and lineage implementation are outside its scope.
What changes for your organisation
Outcomes the service is built to deliver
Accountability and ownership
Data owners, stewards and decision rights for critical datasets.
Policies, strategy and procedures
Proportionate data-quality policy, standards and working procedures.
Process review
How data-quality checks, corrections and escalations operate in practice.
Controls and risk
Where data-quality risks sit and which controls address them.
Team and process leadership
Interim management of data-quality processes and teams, where scoped.
Assurance and reporting
Evidence and reporting that show leadership whether data can be relied on.
How we deliver
Assess → Build → Manage → Assure → Improve
Assess
Identify critical data, owners and how quality is currently governed.
Build
Set accountability, policy, standards and procedures.
Manage
Optional — manage data-quality processes or teams, where scoped.
Assure
Review controls and report on reliability to leadership.
Improve
Reassess as systems, data and decisions change.
Improve feeds the next Assess cycle, so evidence stays current.
What you receive
The working records this service produces
Illustrative examples of the registers, records and executive reporting that may support the engagement.
- Framework components in place
- 7 / 10 ▲ 2 this period
- Open IG gaps
- 8 ▼ 3 vs last review
- Policies approved
- 12 / 15 ▲ 4 this period
- Owners assigned
- 14 / 16 ▲ 3 this period
Gaps by priority
Evidence complete
72%
- Overdue actions
- 2
- Policies awaiting approval
- 3
- Matters for escalation
- 1
Illustrative output
IG Programme Dashboard
Programme view
A single view of IG framework progress, so leadership can see what is in place and what remains.
What it helps you see
- Framework progress
- Open gaps by priority
- Policy approval status
- Ownership and escalations
Likely format
Dashboard / reporting view · Supporting registers and evidence records · Executive PDF summary where agreed
| Dataset | Owner | Control |
|---|---|---|
| Activity data | Named | Monthly check |
| Contract returns | Unclear | None |
Illustrative output
Data-Quality Governance Assessment
Assessment
Where data-quality governance stands against the agreed scope.
What it helps you see
- Critical datasets
- Ownership
- Process and controls
- Gaps
Likely format
| Action | Owner | Priority |
|---|---|---|
| Assign data owners | Owner A | High |
Illustrative output
Improvement Plan & Assurance Summary
Plan
Sequenced actions and a concise view of whether data can be relied on.
What it helps you see
- Priority actions
- Owners
- Evidence required
- Leadership view
Likely format
Illustrative structure — not a client document. Exact outputs and formats depend on the agreed scope.
Scope boundaries
What is included, and what is scoped separately
Included in the core service
- Data-quality governance assessment
- Ownership and accountability model
- Policy, strategy and procedure development
- Process and control review
- Prioritised improvement plan
- Leadership assurance summary
Separately scoped where required
- Technical data profiling or cleansing
- Data engineering or pipeline build
- Lineage tooling implementation
- Statistical validation of research outputs
- Formal audit opinions
Scope, deliverables and assumptions are agreed in writing before work begins.
Relevant evidence
Relevant data-quality and governance evidence
Royal Brompton & Harefield is the primary evidence for data-quality governance and management. Other engagements show related information-governance capability.
View the evidence: Royal Brompton & Harefield NHS Foundation TrustNamed client · Healthcare / NHS
Royal Brompton & Harefield NHS Foundation Trust
Three-year managed IG, clinical safety and data-security service
Three-year managed IG, clinical safety & security service
View the evidence: National Institute for Health and Care Research (NIHR)Named client · Higher Education / Research
National Institute for Health and Care Research (NIHR)
Data-sharing consultancy and information governance strategy
National multi-centre research data-sharing framework
View the evidence: UCLNamed client · Higher Education / Research
UCL
IG audit, improvement plan, training and physical-security audit
Helped shape and focus the final DSPT submission
Explore all case studies →Request Data Governance Evidence →
Your IG-Smart team
Specialist expertise, coordinated around your requirement
Subject-matter expertise is paired with a clear client and programme contact from initial scoping through delivery.
Client & programme contact
Julia Andrade
Coordinates scope, practitioners, delivery and stakeholder communication.
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Buyer decisions
Questions before you engage
Does this include cleaning or fixing data?
How is this different from Data & Information Governance Consultancy?
Can you manage our data-quality team or process?
What do we receive?
Who remains responsible for data quality?
Investment
Scoped to requirement
Scope, deliverables, assumptions and fees are agreed in writing before work begins.
The fee depends on
- Number and criticality of datasets
- Assessment, design or interim management
- Existing policies and processes
- Stakeholders and sites involved
- Timescale
Procurement or supplier-assurance review?
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Still defining your requirement?
Discuss Data Quality Governance
Discuss the requirement, risk, scope and the right engagement model with an experienced practitioner.
Discuss Data Quality GovernanceHave a defined scope, tender or RFP?
Submit a Data Quality Requirement
Share a defined requirement, RFP, tender, statement of work or existing scope for senior review.
Submit a Data Quality RequirementProcurement or supplier assurance
Prepare for Procurement Review
Access company, security and assurance information for supplier review, with controlled evidence available on request.
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