Reports show different numbers
GA4, CRM, ERP and Excel disagree, and reconciling a report takes longer than the decision itself.
Data Governance · diagnosis and implementation
Data Governance that ends disputes about the numbers.
I organise KPI definitions, sources of truth and data ownership so that management, marketing, sales and IT work with the same numbers.
We begin with concrete discrepancies across GA4, CRM, ERP, BigQuery, Power BI and spreadsheets—not with a year-long programme, a shopping list of tools or a policy written only for an audit.
20+years working with analytics and data
Directlyyou work with one accountable consultant
Business + ITdefinitions and rules embedded in systems
Starting point
Does this sound familiar?
Data Governance matters when inconsistent data begins to affect decisions, cost or risk.
Every team defines the KPI differently
“Conversion”, “revenue” and “active customer” have several definitions depending on the department.
Customers or products have several IDs
CRM, ERP and the store cannot join records because they use different identifiers.
Personal data locations are unclear
There is no classification, retention rule or explicit access accountability.
Official reporting is not trusted
Teams maintain their own spreadsheets and manually fix data before meetings.
AI has no trustworthy input
Models and automation inherit errors, duplicates and disputed definitions.
Entry product
The first 10 days: data and KPI diagnosis.
I do not begin with platform selection or a long policy document. First I locate where data diverges from decisions and what should be fixed first.
Time10 working days
Scopekey systems and 5–10 KPIs
Formatanalysis, interviews and decision workshop
- 01
System and flow map
I identify where key data originates and changes, and which manual steps increase the risk of error.
- 02
KPI and ownership map
For critical metrics we define meaning, source of truth, business owner and readers.
- 03
Priorities and decisions
I separate quick corrections, risks requiring a decision and work that can wait.
After the diagnosis you receive
- Current-state mapsystems, flows, owners and places where data is corrected manually
- KPI discrepancy listdefinitions, sources of truth and decisions requiring agreement
- Risk mapimpact on reporting, privacy, cost and AI data readiness
- 30/90-day planwork order, accountability and recommended next-stage scope
Diagnosis scope and price depend on the number of systems, data domains and stakeholders required for agreement.
Describe your data sourcesAfter diagnosis
The next scope follows the problem—not a service catalogue.
Quick corrections
KPI alignment, identifier mapping, validation rules and removal of costly manual steps.
Strategy and accountability
Data owner and steward roles, quality, classification and access rules, and a business–IT decision rhythm.
Implementation and maintenance
Business glossary, data catalogue, quality monitoring, documentation and reviews after system changes.
Possible scope
Elements of a Data Governance implementation.
Not every project needs every element. Scope follows the diagnosis and organisational priorities.
Definitions and standards
KPI glossary, naming conventions, classification, retention and validation rules.
Data quality
Profiling, deduplication and measures of completeness, freshness, consistency and validity.
Metadata and lineage
A catalogue showing where a metric comes from, how it is calculated and who owns it.
Master data and identifiers
Customer or product records and mapping EAN, SKU, ERP indexes and CRM identifiers.
Access, retention and privacy
Access roles, masking and rules for personal data agreed with compliance stakeholders.
Analytics and reporting
GA4, BigQuery, CRM, ERP and Power BI based on shared definitions and quality controls.
Fit
When the project makes sense—and when it does not.
This is a good time when
- report discrepancies affect budget, margin or executive decisions,
- you are integrating systems, building a warehouse or preparing for AI,
- key data has no owners or dispute-resolution rules,
- you want to begin with one domain such as customer, product or sales.
A full programme is unnecessary when
- the problem concerns one well-defined report,
- you only need a technical correction in GA4, GTM or Power BI,
- the organisation cannot yet identify KPI decision-makers,
- you expect a tool purchase to replace process and ownership decisions.
Measuring the result
How we know the operating model works.
Measures follow the problem. I do not promise percentage improvements before knowing the baseline.
Agreed KPIsmetrics with a definition, source of truth and owner
Manual correctionstime spent fixing and reconciling reports
Quality incidentscount, detection time and resolution time
Data controlscompleteness, freshness, consistency and validity in critical sources
No anonymous delivery team
Experience with direct accountability.
I lead the project personally, connecting business and IT—from metric definitions and product identifiers to GA4, BigQuery, Power BI, access and retention.
20+years of experience
150+analytics and data projects
50+clients
- One accountable consultant. The project is not passed between departments or account managers.
- Practical embedding. Rules must work in reports, integrations and daily operations—not only on slides.
- Confidentiality. NDA and careful handling of sensitive data are standard.
Figures cover my broader analytics and data practice, not only Data Governance implementations.
Before we talk
Frequently asked questions.
Do we need to buy a Data Governance platform immediately?
No. Diagnosis can use existing systems and documentation. A tool makes sense only after we understand the process, owners and requirements it must support.
Who should participate in the diagnosis?
A business sponsor and people familiar with key reports and systems. This usually includes analytics, finance or sales, marketing and IT; the exact group depends on the selected data domain.
Is this a legal GDPR audit?
No. I organise flows, roles, access and retention, but I do not replace legal counsel or a Data Protection Officer. Legal decisions are translated with the appropriate compliance stakeholders into system rules.
Can we begin with one area?
Yes. One domain—customer, product, sales or a set of management KPIs—is often the best starting point before scaling the operating model.
How is the engagement priced?
First I establish the number of systems, domains and stakeholders required. I then propose a diagnosis stage and separately priced options for further work. A full implementation is not required from the start.
First step
Describe where your reports stopped speaking the same language.
I will return with questions, a proposed diagnosis scope and the information needed for pricing.
Not ready for a conversation?