Fixing broken enterprise data starts with a business question, not a cleanup tool: what decision or process must this data support, and what would make it reliable enough for that use? Identify the critical data, assign accountable people, trace recurring defects to their causes, and monitor quality over time. A one-time cleanup may hide symptoms; lasting improvement changes the processes that keep creating them.
What does “broken data” mean?
Data is not simply good or bad in the abstract. It is fit—or unfit—for a particular purpose. A customer address might be adequate for a broad regional report but not for sending a physical notice. Different users of the same asset can therefore have different requirements.
The UK Government Data Quality Framework advises teams to understand users and their needs, assess quality throughout the data lifecycle, communicate quality, and anticipate change. It also recognizes that data is unlikely to be equally fit for every purpose. Apply that principle by defining the use before choosing a score or tool.
Describe the use before measuring quality
- Name the business decision, service, or process the data supports.
- Identify the users and downstream systems that depend on it.
- Specify which fields matter and what errors or omissions would make the data unusable.
- Agree acceptable tolerances with the people responsible for the outcome.
For example, a finance team and a customer-support team may use the same account records but need different fields to be complete. A quality rule should reflect each meaningful use rather than impose an arbitrary universal score.
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How do you decide what to fix first?
Start with the data assets that matter most to business objectives—not every defect in every system. UK Government action-plan guidance defines critical data in relation to objectives such as service delivery, legal or contractual requirements, and policy or decision-making. That guidance applies directly to central government departments; its prioritization approach can be useful elsewhere, but it is not a universal legal requirement.
Rank issues by impact, risk, scope, and effort
For each known issue, consider the importance of the affected data, how much is affected, the risk of acting on it, the cost of improving it, and the cost of leaving it unresolved. A small defect in a field used for a consequential decision may deserve attention before a widespread but low-impact formatting inconsistency.
- Business impact: Which decision, service, or process could be delayed or wrong?
- Risk: Could the issue lead to a consequential error, or make a required process unreliable?
- Scope: Which assets, fields, teams, and downstream users are affected?
- Remediation cost: What people, process, or technical changes would a durable fix require?
- Cost of inaction: What workarounds, repeated corrections, or operational risks will persist if the issue remains?
Build a short, visible asset-and-issue register
Record critical assets, their important fields and uses, known defects, affected users, and the person responsible for the next action. This gives teams a practical boundary for improvement and makes it possible to explain why one issue is being addressed before another.
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Who owns data quality?
Data quality needs named accountability, not a general instruction for “the data team” to fix everything. The owner or process owner should be accountable for an asset-level action plan, with business and technical subject-matter experts, analysts, and relevant information owners contributing as needed. Organizations can map owner, steward, and custodian responsibilities to their own role names.
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- Owner or process owner: Accountable for the asset’s quality priorities and action plan.
- Steward: Helps maintain business definitions, rules, and issue handling.
- Custodian or technical team: Supports the systems, storage, pipelines, and controls involved.
- Data users: Report problems and explain the operational impact of defects.
Keep an issue log that records the defect, its business effect, the suspected or confirmed cause, a named action owner, and a target date. Make quality context visible to people who use the data so they can understand known limitations rather than unknowingly treating every value as trustworthy.
How do you fix bad data without repeating the cleanup?
Diagnose how a defect entered the data journey before changing the records. A failed rule may come from a recurring process problem, a system or transformation, unclear definitions, or a one-time event such as a flawed migration or import. More than one cause may be involved. Repeatedly correcting the visible symptom consumes effort and can entrench the process that caused it.
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Trace the defect through its lifecycle
- Describe the failure: State which agreed rule failed, where it was observed, and what use it affects.
- Trace where the value came from: Follow the record through collection, entry, import, transformation, storage, and downstream use as far as the available information allows.
- Test whether the cause recurs: Check whether the issue is systemic or limited to a particular event, batch, source, or change.
- Assign the root cause: Identify the process or technical change that can prevent recurrence, not only the team that noticed the defect.
- Repair and verify: Correct affected data where appropriate, address the cause, and rerun the relevant checks to confirm the result.
The Government Data Quality Framework guidance recommends fixing problems as close to the source as possible. If an immediate downstream correction is necessary to protect a process, treat it as a short-term mitigation and plan the upstream repair. Directly editing poor-quality records without understanding the cause can introduce new problems.
How do you keep data quality from slipping again?
Use repeatable checks tied to agreed business rules. Controls can include validation at data entry, improved architecture or storage, staff training, automation, and accountability practices. Repeat assessments using consistent methods so teams can compare results over time.
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- Profile: Examine in-scope sources and assets to understand their contents and spot patterns or anomalies.
- Define rules: Turn business requirements into checks for the fields and uses that matter.
- Set thresholds: Decide what level of failure needs attention, based on the agreed use and risk.
- Schedule checks: Run them at a cadence appropriate to how quickly the data or process changes.
- Alert and assign: Route meaningful failures to a responsible person with enough context to investigate.
- Review results: Use dashboards or recorded results to see whether defects are recurring and whether interventions are working.
AWS guidance describes quality dashboards, thresholds, alerts, documented and automated processes, and preventive, detective, and corrective controls as operational examples. Microsoft Purview documentation describes profiling, quality rules, scheduled scans, monitoring, and alerts as product capabilities. These examples show possible functions; they do not establish that either product is best for a particular organization.
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How do you break down data silos?
When information crosses teams or systems, different definitions, formats, storage practices, and access decisions can make it inconsistent or hard to share. The UK Data Sharing Governance Framework identifies siloed working, uneven maturity, and isolated problem solving as contributors to misalignment.
Shared data therefore needs both technical interoperability and governance: common standards for how information is represented, described, stored, shared, and accessed, plus clear decision rights for the people responsible. A platform connection alone does not settle what a field means, which version is authoritative, or who can approve a change. Treat those questions as organizational design work as well as integration work.
What should you look for in data-quality software?
Tools can help discover assets, profile data, apply checks, monitor results, and make quality context visible. They cannot decide what “good” means for a business use or create accountability where responsibilities are unclear. Evaluate products against the sources, risks, and operating practices you actually have.
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- Can it profile the real sources and assets in scope?
- Can teams define, version, and manage rules, quality dimensions, and thresholds?
- Can checks run in or near the relevant data creation and transformation pipelines?
- What monitoring cadence, alerts, dashboards, and result history are available?
- Does it support cataloguing, metadata, lineage, discovery, and consumer-facing quality context?
- How does it handle governance roles, ownership workflows, access controls, and audit records?
- Does it fit existing architecture and security or risk requirements?
- What implementation and ongoing operating effort will it require, and how will the organization measure business outcomes?
Use product documentation to understand stated capabilities, then test fit against requirements and procurement criteria. Buying a catalog or quality platform by itself will not repair weak definitions, unclear ownership, or a flawed source process.
A practical starting point
Choose one critical data asset and one consequential use. Agree what reliable data means for that use, record the most important known issues, assign an owner and actions, trace a recurring defect to its cause, and establish repeatable checks with visible results. Expand the approach when teams can see which changes improve the data and which causes remain unresolved.
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