DQ framework and standards
A data quality policy, standard and dimension definitions, with thresholds tied to business impact and risk appetite.
DQ · Data Quality
Data quality you can measure and prove
We build data quality management that moves beyond one-off cleansing — standards, rules and scorecards that detect problems early, route them to the right owner and fix the root cause.
The challenge
Poor data quality is rarely a technology problem. It is an ownership and measurement problem that shows up as rework, regulatory findings and lost trust.
- Quality is discussed anecdotally — nobody can say whether it is getting better or worse.
- Rules live in spreadsheets and ETL code, duplicated and inconsistent across teams.
- Issues are fixed downstream in reports while the source keeps producing the same errors.
- Scorecards exist but measure what is easy, not what matters to the business.
What we deliver
Data Quality
Profiling and rule design
Profiling of critical data elements to establish baselines, then business rules written in plain language and translated into executable checks.
Scorecards and dashboards
Board, domain and steward-level views that show trend, threshold breaches and ownership, designed to be read in under two minutes.
Issue management
A logging, triage and escalation workflow linked to owners and SLAs, integrated with your existing service management tooling.
Root cause analysis
Structured analysis that traces defects to process, system or control failures so fixes are made once, at source.
Controls and monitoring
Automated quality controls embedded in pipelines with alerting, plus evidence packs for audit and regulatory review.
The six dimensions of data quality
- AccuracyData correctly reflects the real-world object or event it describes.
- CompletenessAll required data is present, at the level of detail needed.
- ConsistencyData agrees across systems, reports and points in time.
- TimelinessData is available when it is needed and reflects the right point in time.
- ValidityData conforms to defined formats, types, ranges and business rules.
- UniquenessEach entity is recorded once, without unintended duplication.
Outcomes you can measure
Outcomes you can measure
60%
typical reduction in recurring defects on critical data elements after root-cause fixes
Weekly
automated quality scoring replacing quarterly manual reconciliations
100%
of critical data elements with an owner, rules and a published threshold
Related case study · Illustrative engagement
A Solvency II data quality framework the board reads
58%
fewer recurring data defects
A data quality framework and board scorecard aligned to Solvency II data requirements, with root-cause fixes that removed most recurring defects in reserving data.
Read the case studyFAQ
Frequently asked questions
What are the six dimensions of data quality?
Accuracy, completeness, consistency, timeliness, validity and uniqueness. We use them as a shared vocabulary for writing rules and reporting scores, weighted to what matters for each data domain.
How do you decide which data quality rules to write first?
We start with critical data elements and work back from the business impact of failure — a regulatory misstatement, a failed payment, a mis-priced policy — so the first rules protect the highest-value outcomes.
Do we need a dedicated data quality tool?
Not always. Many organisations can start with rules in their existing platform, such as Databricks or Informatica. We assess whether a dedicated tool such as Ataccama would pay back given your volume and complexity.
What should a data quality scorecard include?
A trend line, a threshold, an owner and a clear call to action for each critical data element or domain. Anything else belongs in drill-down, not on the first page.
How do you make data quality improvements stick?
By fixing root causes at source, assigning issues to accountable owners with SLAs and reporting progress to a governance forum that has authority to prioritise remediation.
Related insights
Related insights
Why lineage is the fastest route to BCBS 239 evidence
Start with a material risk report and work backwards. Evidence-led lineage connects reported numbers to the controls, decisions and accountable owners behind them.
Building a data quality scorecard your board will read
A board scorecard should explain exposure and the decisions required, not display every validation rule. Build it around critical outcomes, transparent measures and accountable action.
DORA and the data governance function
DORA creates important dependencies on reliable operational information. Data governance should strengthen that evidence without becoming a parallel ICT risk or resilience function.
Discuss your priorities with a practitioner
A 45-minute working session, no sales deck. You will leave with at least three actionable recommendations.
Book a consultation Email us: info@crestqa.co