Data Quality Management
Numbers people stop arguing about.
Data quality management services for UK businesses. Profiling, rules and monitoring that convert messy source data into a reporting layer everyone actually trusts.
Data quality is one of those topics that everyone agrees matters and almost nobody has an actual plan for. The result is a slow bleed of confidence in reporting. Every board pack has a footnote. Every operations review has someone challenging a number. Every new dashboard gets met with polite scepticism. Fixing it is less complicated than most people think.
Our data quality management services combine the practical basics with enough tooling to keep the improvements permanent. We start with a proper profile of the source data, agree the quality dimensions that matter, write a small number of measurable rules and stand up the monitoring. From there it becomes routine, and quality scores start moving in the right direction week on week.
We do this work inside your existing stack wherever possible. Microsoft Purview, Power BI, Fabric data quality, Great Expectations, dbt tests and the odd bespoke SQL check. The point is to make quality visible and owned, not to sell you another platform.
The six quality dimensions
A small vocabulary that covers almost every real problem.
Accuracy
Does the value in the system match the real world? Wrong postcodes, mistyped invoice totals, misspelled customer names.
Completeness
Are all the fields we depend on actually populated? A customer with no country, a sale with no product, a case with no owner.
Consistency
Do the same values look the same everywhere? Country codes, currency codes, product hierarchies and unit conventions in one form only.
Timeliness
Is the data current enough to make the decision it supports? A daily report on data that is a week stale is worse than useless.
Validity
Does every value obey the rules it should? Dates in valid ranges, statuses from an allowed list, VAT numbers that pass a check digit.
Uniqueness
Is every real world entity represented once? Duplicate customer records are a special kind of quiet disaster.
How we run a data quality engagement
Profile, rules, monitor, own.
A first phase usually takes four to six weeks and covers one or two data domains. Customer, product, finance transactions, HR records. We pick the ones where poor quality is doing the most damage and get them into a healthy monitored state. Then we widen out.
Ongoing stewardship is a small monthly commitment. Half a day a week for a nominated steward, with our support behind them. Nothing exotic is needed to keep the improvements holding.
Deliverables
- Profile report per domain
- Written rule catalogue
- Automated rule enforcement in pipelines
- Quality scorecard in Power BI
- Exception queue and workflow
- Steward roles and RACI
- Monthly executive summary
Get started
Tell us where the quality is hurting most.
Send us a short note about the domain that keeps causing arguments in board packs. We will come back within a day with a proposed first phase.