Building an effective data management system across the organisation
Managing the data lifecycle
Establishing a data strategy to support business goals and long-term growth
Introducing metadata management and data lineage
Ensuring data quality and trustworthiness for critical decision-making
Defining data roles and responsibilities across the organisation
Implementing data governance in line with applicable regulations (e.g. GDPR, the EU AI Act)
Securing data and setting up access controls
• Enterprise data governance strategy aligned with business objectives
• Design and implementation of governance frameworks aligned with DAMA, including data policies, standards and procedures
• Establishing data oversight, data classification and data taxonomy
• Data quality management processes
• Defining metrics and KPIs to measure data-governance performance and effectiveness
• Defining key roles (Chief Data Officer, Data Owners, Data Stewards)
• Establishing a Data Governance Office and steering committees to coordinate and govern data activities
• Clear accountabilities and decision rights
• Implementing escalation processes for data issues
• Setting up incentive mechanisms linked to data quality
• Training and change management for all data roles
• Implementing GDPR requirements (incl. the right to erasure)
• Preparing for new and emerging regulations (e.g. EU AI Act)
• Classifying AI systems and aligning with sectoral regulations (e.g. Basel III, Solvency II, DORA)
• Processes for data audits, regulatory reporting and data-retention policies
• Regulatory horizon scanning and timely response to legislative change
• Implementing a data-quality framework with monitoring and automated controls
• Establishing data catalogues and central metadata repositories to ensure transparent governance
• Data lineage and impact analysis to increase trust in data flows
• Processes for data cleansing and enrichment
• Master Data Management (MDM) implementation to ensure consistency across the organisation
For a financial-sector client, KPMG designed a comprehensive data governance framework aligned with data-management and security regulations, implemented data steward roles, set up data-quality processes and built automated regulatory reporting.
For a retail client, KPMG mapped reporting data sources, established a unified data taxonomy, analysed differences in internal and external reporting of performance KPIs, and proposed report consolidation and improved data workflows and interpretation.
During a migration of core systems and databases, KPMG helped ensure data reporting consistency across two systems and flawless data transfer into the new database.
