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Data Management and Data Governance
The bridge between privacy and AI compliance. Data governance frameworks, data maturity assessments and data quality management along DAMA-DMBOK.
Without a clean data foundation, there is no working GDPR compliance and no defensible AI governance. A DPIA or FRIA depends on data lineage that you actually know; a processing record relies on a current data catalogue; ISO 42001 requires AI systems to run on controlled data. Data management is therefore not a parallel track — it is the foundation that lets privacy and AI compliance function.
DCBS builds that foundation along international standards (DAMA-DMBOK) and ties it explicitly to your GDPR and AI Act tracks. Not as a standalone data strategy exercise, but as a compliance enabler.
Data governance in concert
Four substantive areas: data governance framework, data maturity, data quality and data strategy. Aligned with your privacy and AI compliance, not as a parallel programme.
Data governance framework with data ownership, stewards, policy and decision-making structure. Aligned with DAMA-DMBOK domains and linked to privacy and AI governance.
Data maturity assessment against a validated model (CMMI for Data Management or equivalent). Output: scorecard per DAMA domain with concrete priorities for the next 12–24 months.
Data quality management programme: dimensions (completeness, accuracy, consistency, timeliness), measurement cadence, dashboards and escalation paths. For datasets with GDPR or AI Act impact.
DAMA-DMBOK implementation as a shared language and structure. Not a one-to-one book translation, but selective adoption of the domains that deliver value for your organisation.
Master data management consultancy for organisations with multiple systems that do not consistently identify critical entities (customer, supplier, product). Often the blocker for GDPR rights execution (access, erasure) and for AI training-data quality.
Processing record relies on unowned data
The GDPR processing record refers to datasets that have no clear owner, no quality oversight and no documented lineage. Privacy and data management have drifted apart.
AI system on uncontrolled data
An AI system (in production or in development) runs on datasets whose provenance, quality and governance do not hold up. ISO 42001 or a FRIA surfaces this and requires a fix.
Data governance started, then stalled
A data governance initiative started some years ago but has remained at presentation level without operational embedding. External reinforcement needed to bring it across the line.
Data management is bespoke work
Data management does not lend itself to an SKU list. The shape follows from maturity level, organisation size and the bottleneck being addressed. A few typical engagement shapes:
Stand up a data governance framework
Greenfield track: name data owners, assign stewards, design policy and decision-making structure, bring the first data domains under governance. Typically 4–6 months.
Data maturity assessment
Structured scan of the existing data organisation: scorecard per DAMA domain, priorities for the next 12–24 months, business case for follow-up. Typically 4–8 weeks.
DAMA-DMBOK implementation track
Selective implementation of the DAMA-DMBOK domains that deliver the most value for your organisation. Not a book translation, but methodological grounding. Typically a multi-year programme with phased planning.
Compliance-driven data quality programme
Programme focused on datasets with direct GDPR or AI Act impact: define quality dimensions, set up measurement cadence, build dashboards and escalation paths. Enables audit trail. Typically 6–12 months.
1. Discovery
Conversation with business, IT and privacy stakeholders. Locating the pain: GDPR, AI compliance, audit findings, or strategic ambition.
2. Assessment
Maturity scoring on the relevant DAMA domains, with scorecard and gap report. Not an open-ended study — fixed lead time.
3. Implementation
Roadmap execution with clear milestones. Steering committee, ownership assigned internally, external reinforcement where capacity falls short.
4. Embedding
Ongoing monitoring, annual cycle, linkage with privacy and AI governance. External role phases itself out gradually.
Organisations where data is the bottleneck
DCBS works for organisations where data management is a blocker for GDPR or AI compliance: financial institutions, public sector organisations, critical infrastructure and international corporates with complex data landscapes. For specific case examples — see our cases.
Get data management in order?
Free 30-minute intake call. The conversation explores whether an assessment, a framework or a more specific track delivers the most impact.