Expertise II

Data Governance, MDM & Data Quality

Reliable reference data, clear responsibilities and measured quality: the precondition for any data or AI project that lasts.

Governance is not one more committee. It is the set of rules, roles and tools that make a piece of data mean the same thing to finance, operations and IT. We put it in place pragmatically, starting with the data that weighs most on your decisions.

The symptoms we encounter

Customer, product or supplier reference data that differs from one application to the next
Duplicates that distort analyses and complicate operations
Indicators defined differently by finance, operations and sales
Nobody is accountable for a piece of data; corrections get lost between departments
Data that is hard to find, with no catalogue or documentation
Weak traceability: where does this figure come from, which transformations did it go through?
Quality discovered through incidents rather than measured
AI projects slowed down, or blocked, by the state of the data

What we put in place

Governance framework: roles (data owners, data stewards), bodies, policies, priorities
Business glossary and data dictionary: one definition per indicator and per concept
Data catalogue and lineage, to find a piece of data and understand where it comes from
Master Data Management: customer, supplier, product, item and organisation reference data
Data quality: rules, measurement, remediation, quality dashboards
Business rules formalised and enforced in processing
Personal data compliance and protection (GDPR)
AI governance: training and reference data, authorised uses, model traceability

Customer, product and supplier reference data

Reference data is built from the uses that depend on it: invoicing, logistics, reporting, customer relationship. We define the model, matching and de-duplication rules, responsibilities and the update process with you, then tool the whole according to your context.

Typical use cases

Single customer reference for a multi-subsidiary group
Product master data clean-up ahead of an ERP migration
Regulatory data quality programme in banking or insurance

How we work

Pragmatically: we start with the data that weighs most on your decisions, not with an exhaustive programme
With your business teams: responsibilities and rules are defined with the people who produce and use the data
With tooling suited to your context, chosen after the needs analysis
With hybrid France / Casablanca delivery for clean-up and remediation work