Assessment planning and scope
We define a testable question with clear assessment boundaries.
- Specify datasets and periods
- Agree criteria and references
- Identify samples and limitations
- Hand over a test plan with contacts
Definitions, implementation and traceable results
Data Quality Audit: Assess Datasets and Evidence Findings
We assess an agreed dataset against documented criteria and deliver traceable findings.
- 01Define the assessment and its boundaries
- 02Establish access and evidence
- 03Test criteria against documented cases
- 04Discuss and prioritise findings
11+Years of experience
120+Employees
540+Projects
ISO 27001certified
Data Quality Audit: Assess Datasets and Evidence Findings
The audit is a bounded assessment of the existing data. It covers planning, access, traceable tests and discussion of findings. It replaces neither ongoing data quality management nor a separately defined certification process.
We assess an agreed dataset against documented criteria and deliver traceable findings. Assessment date, scope, samples and limitations are disclosed so you can prioritise improvements on a substantiated basis.
4 service modules
What we take on for you
Bookable individually or as an end-to-end programme.
Data checks and evidence
We perform agreed tests using a traceable measurement population.
- Record data timestamps and test versions
- Test technical and business cases
- Evidence anomalies with examples
- Keep untested areas visible
Findings and cause indications
We discuss results and potential impacts with accountable teams.
- Separate confirmed errors from suspicions
- Describe affected uses and scope
- Label cause indications as such
- Deliver prioritised findings for discussion
Report and follow-up
We provide a traceable basis for decisions and subsequent retests.
- Combine boundaries and results
- Propose actions with owners
- Define criteria for retesting
- Hand over a report without blanket certification
5 phases
Our Approach
We agree the dataset, period, criteria and evidence. Technical checks and business examples are documented. The final report distinguishes confirmed errors, unresolved anomalies and untested areas and identifies recommended next steps.
Define the assessment and its boundaries
Establish access and evidence
Test criteria against documented cases
Discuss and prioritise findings
Hand over the report and retesting needs
Your contact
Asan Stefanski
Head of Digital Transformation
11+ years of experience, Applied Computer Science degree, Strategic planning and management of AI projects, Cyber Security, Secure Software Development, AI
A data quality audit is far more than a technical data analysis – it is a strategic instrument for gaining transparency about the actual value and usability of your data. It becomes particularly valuable when technical findings are linked to concrete business impacts, enabling fact-based investment decisions for quality improvements. Our clients especially appreciate the practical, value-driven nature of our audit results and recommendations.
Our Strengths
- 01Hand over a test plan with contacts
- 02Keep untested areas visible
- 03Deliver prioritised findings for discussion
- 04Hand over a report without blanket certification
Review note
Assess an audit report by its boundaries as well as its findings: which data and rules were actually tested? A sample or a good metric does not establish that the entire dataset is error-free.
5 QUESTIONS, BRIEFLY ANSWERED
Frequently asked questions about Data Quality Audit: Assess Datasets and Evidence Findings
What does the audit report contain?
It records scope, data timestamp, criteria, method and evidenced results. Confirmed errors, unresolved anomalies and untested areas remain separate; no blanket assurance of error-free data is issued.
How are duration and required resources determined?
Scope, data access, reference information and business contacts determine effort. A test plan and dependencies are agreed after scoping, with missing access or references documented as limitations.
Is the audit a certification or regulatory approval?
No. The engagement provides results for agreed criteria and data. A specific certification process or regulatory assessment would require an explicitly defined framework; the word audit alone does not establish that status.
How does the engagement differ from an internal review?
We agree the role, criteria and evidence for the external assessment. An external perspective can complement existing review; independence, completeness and control suitability are not inferred from the appointment alone.
Which data domains are suitable for assessment?
Suitable datasets have clear boundaries, named uses and testable criteria. We prioritise known issues and important decisions; assessing one domain does not establish the state of all organisational data.
Certificates, partners and more
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