ContactBook Discovery Call
Menu

Confidence in critical data

Data Quality Management

Improve confidence in your most important data through profiling, quality rules, controls, remediation and monitoring.

Discuss this serviceAll services
A data governance evidence workspace with scorecards and operating-model documents.
Evidence-led deliveryEvidence into action.

Challenge

When to use this service.

Reports, dashboards and operational processes are affected by missing, duplicated, outdated or inconsistent data.

Decision-maker insight

Data Quality Management in plain terms.

Data quality management is not a one-off clean-up exercise. It identifies critical data, defines quality expectations, tests dimensions such as accuracy and completeness, fixes root causes and monitors whether data remains fit for purpose.

Management framework

Data quality framework: dimensions, rules, controls and remediation

Decision makers need to know which data must be fit for purpose, how quality is measured, who owns improvement and where quality risk is accepted or remediated.

01

Data quality dimensions

Measure accuracy, completeness, consistency, validity, uniqueness, timeliness, integrity and conformity based on how the data is used.

  • Dimensions are selected by business use
  • Thresholds are agreed with owners and users
  • Known limitations are communicated with reports
02

Critical data elements

Focus quality effort on the fields and records that affect material decisions, compliance, operations or customer outcomes.

  • Critical fields are mapped to processes and reports
  • Definitions and permissible values are documented
  • Materiality and risk criteria are agreed
03

Business rules and controls

Convert business expectations into validation rules, profiling tests, reconciliation routines and preventative controls.

  • Rules are implemented close to source where possible
  • Exceptions are logged with impact and owner
  • Manual workarounds are visible and prioritised
04

Issue and root-cause management

Move from symptoms to root causes by connecting exceptions to process, system, supplier, policy or people causes.

  • Issue log includes severity, owner and target date
  • Root-cause categories are tracked
  • Repeat issues trigger process or control change
05

Scorecards and monitoring

Make quality visible through trend reporting that shows confidence, exceptions, improvement actions and accepted limitations.

  • Scorecards are reviewed by data owners
  • Threshold breaches have escalation paths
  • Quality trends influence investment priorities

Lifecycle

Data quality lifecycle

Quality risk can enter at any stage. The lifecycle helps leaders see whether the organisation prevents defects, detects issues early and communicates quality honestly.

01

Plan and define

Clarify user needs, purpose, critical elements, definitions, standards, dimensions and acceptable thresholds.

Evidence: User needs, CDE list, glossary, quality requirements and acceptance criteria.
02

Collect and ingest

Prevent defects at source through validation, required fields, reference data, metadata capture and supplier controls.

Evidence: Input controls, data contracts, validation rules, ingest checks and metadata requirements.
03

Prepare and maintain

Standardise, match, deduplicate, reconcile and maintain data so later users understand lineage and limitations.

Evidence: Profiling reports, lineage view, reference data rules, matching logic and change history.
04

Use and analyse

Assess whether data is fit for the specific decision, report, model, process or statutory return.

Evidence: Quality scorecard, exception log, reconciliation results and sign-off notes.
05

Share and improve

Communicate quality, protect meaning during sharing and feed lessons back to source teams.

Evidence: Data quality statement, sharing notes, retention record and feedback backlog.

Engagement scope

What we can cover.

Data profiling and assessment

Critical data element identification

Data quality dimension definition

Business rule development

Validation controls

Duplicate and uniqueness analysis

Completeness, accuracy and consistency reviews

Data reconciliation

Root-cause analysis

Issue management and remediation planning

Monitoring and dashboards

Data quality framework development

Deliverables

Outputs your teams can use.

Data quality assessment report

Profiling findings

Data quality rule catalogue

Data quality issue log

Root-cause analysis

Remediation plan

Data quality scorecard

Monitoring dashboard

Data quality operating procedure

Expected outcomes

What improves.

Better reporting confidence and fewer avoidable corrections

Quality expectations defined for critical data elements

A controlled improvement process for recurring quality issues

Decision guide

Test readiness before you invest.

Distinguish embedded capability from disconnected activity.

Leadership questions

  1. Which reports, processes or obligations fail if this data is wrong, late, duplicated or incomplete?
  2. Which data quality dimensions matter most for each critical data element?
  3. Are checks preventative at source, detective after processing, or only manual at the end?
  4. Who owns quality improvement when the issue crosses systems or departments?
  5. Can leaders distinguish accepted quality limitations from uncontrolled quality risk?

Signals of maturity

  • Quality is defined by business use, not generic technical preference.
  • Critical data elements have rules, thresholds and owners.
  • Quality checks run through the lifecycle, not just before reporting.
  • Root causes are fixed through process and control changes.
  • Scorecards are used in management decisions and funding conversations.

Evidence to prepare

  • Critical reports, dashboards, extracts and operational processes
  • Known quality issue logs, reconciliation notes and audit findings
  • Data dictionaries, definitions, reference data and validation rules
  • Examples of manual fixes, rework, duplicate records or late reporting
  • Current data quality dashboards, profiling outputs or improvement plans

Process

From evidence to implementation.

01

Identify priority datasets, reports, processes and critical data elements

02

Profile data against dimensions such as accuracy, completeness and timeliness

03

Define business rules, thresholds, controls and issue ownership

04

Trace recurring problems to process, system, supplier or policy causes

05

Create monitoring routines, scorecards and remediation governance

Related training

Data Quality Practitioner

Build the role capability needed to sustain the change.

View training route

Resource

Data Quality Rule Template

Prepare the evidence for a productive first conversation.

Browse insights

Scope note

Evidence first, claims second.

No claims of certification, approval or compliance without evidence.

Enquiry form

Enquire about Data Quality Management

Share the priority, risk or decision. We will suggest a practical next step.

Ready to move?

Turn data risk into a clear next step.

Start with a focused discovery call or readiness assessment.

Book a Discovery CallRequest an Assessment