Data governance in different industries
Financial
Financial institutions face extensive regulatory requirements that mandate comprehensive data lineage, audit trails, and regulatory reporting. Governance focuses heavily on risk data aggregation, financial reporting accuracy, and demonstrating compliance.
Financial services governance emphasises data quality at the source, reconciliation across systems, and complete audit trails. Lineage must demonstrate where figures in regulatory reports originate and how they transform. Customer data governance addresses both privacy regulations and KYC (know your customer) requirements.
Large banks often implement centralised governance operating models with strong data architecture teams, comprehensive data dictionaries, and sophisticated MDM for customer, product, and reference data. Real-time fraud detection and AML monitoring require governance that balances access for detection algorithms with privacy and security controls. Take a look at how the Agile Data Engine improved data quality for Skandia.
Health and wellbeing
Healthcare governance is dominated by patient privacy, clinical safety, and regulatory compliance for drug development and medical devices. Protected Health Information (PHI) requires stringent access controls, audit logging, and consent management.
Clinical data governance must balance research enablement with privacy protection. De-identification and anonymisation techniques allow researchers to use patient data while protecting privacy. Governance frameworks define what constitutes appropriate de-identification and who can access identified versus de-identified data.
Life sciences companies developing drugs must maintain comprehensive data lineage for clinical trial data to satisfy FDA validation requirements. Data integrity, audit trails, and controlled access to trial data are governance priorities. Electronic data capture systems, laboratory information management systems, and patient registries all require governance integration.
Retail and e-commerce
Retail governance focuses on customer data privacy, personalisation enablement, and supply chain optimisation. Customer 360 initiatives require MDM to create unified customer views across online, in-store, mobile, and call centre touchpoints.
Product data governance ensures consistent product information across channels. Inconsistent product descriptions, pricing, or availability information damages customer experience and erodes trust. Governance defines authoritative product data sources and distribution processes.
E-commerce generates vast clickstream and behavioural data for personalisation and analytics. Governance must balance data collection for personalisation with privacy compliance, providing transparent consent mechanisms and honouring customer preferences. Read how data travels faster and better between suppliers and retailers.
Manufacturing and industrial
Manufacturing governance centres on operational data: sensor data from IoT devices, production systems, quality management, supply chain, and maintenance data. Quality and safety requirements in industries like automotive, aerospace, and pharmaceuticals demand rigorous governance.
Industrial IoT generates massive data volumes requiring governance focused on data retention, aggregation strategies, and quality monitoring. Sensor drift and calibration issues affect data quality, requiring monitoring and alerting.
Supply chain governance addresses master data for suppliers, parts, and materials. Complex multi-tier supply chains require consistent product identifiers and shared data standards. Blockchain is increasingly used for supply chain traceability, requiring governance for shared ledgers.
Public sector
Government agencies face unique governance challenges: public records laws requiring transparency, privacy regulations protecting citizen data, extended retention requirements, and legacy systems spanning decades. Governance must balance open data initiatives with security and privacy.
Cross-agency data sharing requires governance frameworks addressing data exchange standards, consent, and security. Federated identity management enables citizens to interact with multiple agencies securely.
Government data has public value beyond operational use. Open data initiatives publish datasets for public use, requiring governance ensuring appropriate anonymisation, documentation, and accessibility. Governance must distinguish public datasets from sensitive operational data.
What are the best available tools and technologies for data governance?
While data governance is primarily about people, processes, and policies, technology platforms enable governance at scale. Selecting tools that integrate with your existing technology stack and support your governance operating model is critical.
Data catalog and metadata management platforms
Data catalogs provide searchable inventories of data assets with business and technical metadata. Leading platforms include Collibra, which offers comprehensive governance capabilities, including workflow management, policy documentation, and integration with BI and data platforms. Alation emphasises collaborative data intelligence with automated metadata harvesting and behavioural analytics. Microsoft Purview integrates with Azure and Microsoft ecosystems while supporting multi-cloud environments. Atlan focuses on modern data stacks with strong support for data mesh architectures and active metadata.
Catalog selection should consider metadata coverage (can it harvest from your data sources?), collaboration features (glossaries, annotations, social features), lineage capabilities, and integration with your BI and analytics tools.
Data quality tools
Data quality platforms profile data, define quality rules, monitor quality continuously, and manage remediation workflows. Talend data quality provides profiling, standardisation, and matching capabilities integrated with Talend’s data integration platform. Informatica data quality offers comprehensive quality capabilities for structured and unstructured data. Great Expectations, an open-source framework, enables data engineers to define expectations as code and integrate quality checks into data pipelines.
Modern data observability platforms like Monte Carlo, Datakin, and Anomalo detect data quality issues automatically using machine learning, alerting teams to anomalies, schema changes, and freshness issues.
Master data management platforms
MDM platforms create and maintain golden records for critical business entities. Informatica MDM, SAP master data governance, and Reltio offer comprehensive MDM capabilities. Architecture choice (centralised, registry, consolidation, or coexistence) drives platform selection and implementation complexity.
MDM implementations are among the most challenging governance initiatives, requiring significant business engagement on data standards, matching rules, and workflow design. Organisations should validate clear business value before undertaking MDM.
Data lineage and integration
Automated lineage tools like Manta and Octopai parse code, query logs, and ETL definitions to construct lineage graphs showing how data flows through systems. Cloud data platforms increasingly provide native lineage capabilities: Snowflake, Databricks, and Google BigQuery offer lineage features integrated with their platforms.
Access governance and privacy
Tools like Immuta, BigID, and OneTrust automate access policy enforcement, discover and classify sensitive data, and manage privacy compliance. These platforms integrate with data warehouses, lakes, and cloud platforms to enforce fine-grained access controls dynamically based on data classification and user context.
Privacy management platforms help organisations manage consent, fulfill data subject requests (access, deletion, portability), and maintain privacy compliance documentation.
