Before reporting
Power BI and management reports will not solve conflicting definitions, bad source fields or spreadsheet logic that only one person understands.
Data quality consultant for UK SMEs
Find the records, rules and ownership gaps that make business data unreliable.
Quick answer
Digital Adaption helps SMEs identify and fix data-quality problems before they damage reporting, ERP or CRM migration, Power BI, workflow automation or management decisions.
Most data-quality problems keep returning because nobody owns the field, report definition or source-system rule. The work is not only finding bad records. It is creating a practical cleanup backlog, assigning owners and putting checks in place so the same defects do not keep coming back.
When data quality becomes a business problem
A data-quality review is useful when poor records are slowing decisions, blocking migration or making reports impossible to trust.
Before migration
ERP, CRM and finance-system migrations need source data that can be mapped, cleansed, validated and signed off before cutover.
Before automation
Power Automate workflows and approval routes fail when the underlying fields, statuses, owners or exception rules are unclear.
What gets delivered
The aim is a working evidence pack, not a theoretical data-governance document.
Data-quality findings
- Duplicate, blank, stale and invalid records.
- Fields with unclear owners or update routes.
- Reports and processes affected by each issue.
Cleanup backlog
- Issues ranked by risk, effort and dependency.
- Named owners and decisions required.
- Quick fixes separated from structural problems.
Control recommendations
- Ownership matrix and validation rules.
- Master-data checks and exception handling.
- Reporting definitions and review cadence.
Methodology
The review uses source-system exports, report outputs, sample records, spreadsheet dependencies, user interviews and control totals to separate symptoms from root causes. The evidence links each data issue to a business impact: delayed month-end, failed migration testing, unreliable forecasts, manual rework or poor automation decisions.
Map the source
Identify where customer, supplier, product, finance and operational data starts, where it changes, and which reports depend on it.
Measure defects
Check duplicates, blanks, invalid values, inactive records, code conflicts, naming variation and broken report assumptions.
Control recurrence
Define owners, rules, validations and review points so the cleanup does not disappear as soon as the next project starts.
Related data resources
These pages help connect data quality work to migration, reporting and governance.
Data management consultant UK
Practical ownership, governance and data-quality improvement for UK SMEs.
Data quality audit checklist
A checklist for finding risky records, unclear ownership and reporting defects.
Migration readiness review
Use data-quality evidence before an ERP, CRM or finance-system cutover.
FAQs
Is this the same as data governance?
It is the practical part of governance: finding defects, assigning owners, agreeing rules and putting checks in place where the business actually uses the data.
Can this start from spreadsheets?
Yes. Many SME data-quality reviews start from Excel, CSV exports, Access databases, finance reports, CRM lists and ERP extracts.
How long does a first review take?
A focused first review usually takes 5-10 working days depending on the number of systems, reports and owners involved.
Need to know why the numbers are unreliable?
Start with a focused review of the records, fields, reports and ownership gaps creating the problem.
Book a data quality review