Data governance and Master Data Management (MDM) are two related but distinct disciplines for managing data in an organization. While there is some overlap between the two, they have different areas of focus and objectives.
Quick Answers
Here are quick answers to key questions about the difference between data governance and MDM:
What is data governance?
Data governance is the overall management and oversight of data assets across an organization. It focuses on establishing processes, roles, policies, standards and metrics to ensure data is high quality, consistent, accessible and used efficiently.
What is Master Data Management (MDM)?
MDM is a set of practices and techniques focused specifically on managing master data – the core business entities, like customers, products, suppliers, etc. MDM creates a “golden record” view of these entities.
How are they related?
MDM is a subset of data governance, focused on master data. Effective MDM requires strong data governance foundations like policies, roles and standards.
How are they different?
Data governance has a broader scope, setting strategy for all data. MDM focuses on creating authoritative master data sets specifically. They have different stakeholders and objectives.
Understanding Data Governance
Data governance establishes the processes, roles, policies, standards and metrics for properly managing data as an enterprise asset across an organization. Some key elements of data governance include:
- Defining data policies, standards and procedures
- Establishing data stewardship roles and responsibilities
- Developing data quality standards and controls
- Enforcing data management procedures and policies
- Providing strategic oversight for data and analytics initiatives
The goals of data governance are to ensure data is high quality, consistent, accessible and used efficiently to enable business objectives. Data governance focuses on the “what” – defining the policies and procedures for data. It provides strategic oversight for data management.
Benefits of Data Governance
Effective data governance provides many benefits for an organization, including:
- Improved data quality and availability
- Greater consistency across systems and functions
- Reduced risk from poor data practices
- Better utilization of data assets
- Increased employee productivity and collaboration
- Enhanced strategic decision making
Elements of a Data Governance Program
A comprehensive data governance program involves the following key elements:
- Vision and Strategy – Defining the long-term vision, strategy and business objectives for governing data.
- Policies – Developing formal policies for data management covering access, security, lifecycle, quality etc.
- Standards – Choosing shared data standards, models, definitions and metadata.
- Processes – Establishing processes for managing, integrating, transforming, extracting data.
- Roles and Responsibilities – Designating data stewards and clearly defining governance roles.
- Metrics and Monitoring – Measuring data quality, usage and value and monitoring compliance.
- Issue Resolution – Defining escalation procedures for data issues and disputes.
Challenges of Data Governance
Some common challenges faced in implementing data governance include:
- Lack of clear business vision and data strategy
- Undefined policies, standards and procedures
- Poor communication and training on governance program
- No central ownership and accountability
- Compliance and adoption issues
- Measuring the business value and ROI
Understanding Master Data Management (MDM)
Master data management (MDM) refers to the tools, processes and policies for creating and maintaining consistent lists of master data across an organization. Master data represents the core business entities that are critical for business operations. Examples include:
- Customers
- Products
- Suppliers
- Employees
- Accounts
MDM creates a “golden record” or authoritative, trusted view of these core business entities by managing duplication and data conflicts. The objectives of MDM are to:
- Create a single source of truth for master data
- Improve data quality and consistency
- Enable better reporting and analytics
- Increase operational efficiency
- Reduce costs from data errors and redundancies
Unlike data governance, MDM focuses on the discipline of actually maintaining master data – the “how” rather than the “what”. It combines business processes, specialized software tools and data management techniques.
How MDM Works
MDM solutions work by consolidating master data from different source systems, identifying duplicates, cleansing data and resolving conflicts to create authoritative master data sets. The key capabilities include:
- Master data integration – Aggregating master data from source systems into MDM hubs or repositories.
- Matching and linking – Identifying records that relate to the same master entity across systems.
- Data cleansing – Correcting or standardizing fields through validation, editing and parsing.
- Golden record creation – Resolving conflicts to create a single best record for each entity.
- Lifecycle management – Managing master data changes, updates and deletions.
This MDM process eliminates data silos and inconsistencies, providing trusted, high-quality master data to downstream applications.
MDM Architecture Styles
There are three main architectural styles for MDM solutions:
- Registry – A centralized metadata registry stores links between records and source systems.
- Consolidated – Master data is physically consolidated into an MDM repository.
- Hybrid – Combines registry and consolidated approaches.
The choice depends on an organization’s needs, infrastructure and budget. A hybrid MDM architecture is common, consolidating some master data while providing a registry for other domains.
Key MDM Components
A typical MDM solution architecture includes the following components:
- Data integration and ETL processing
- Matching and deduplication engine
- Workflow and decisioning engine
- Hierarchy and relationship management
- Data repository and/or registry
- Access services and data delivery
- Metadata management
- Data quality and cleansing tools
- Lifecycle and version management
- Data stewardship tools
Comparison of Data Governance vs MDM
While data governance and MDM are complementary disciplines, they have some key differences:
Criteria | Data Governance | MDM |
---|---|---|
Focus | Strategic oversight for data assets across an organization | Operational process of managing master data domains |
Scope | All enterprise data assets | Specific master data domains like customers, products etc. |
Approach | Top-down strategy and policy-driven | Bottom-up focus on master data management procedures |
Objectives | Data quality, availability, compliance, risk reduction | Creating trusted “golden records”, increase operating efficiency |
Activities | Developing policies, standards, metrics, issue resolution | Integrating, cleansing, matching and consolidating master data |
Governance | Provides oversight for all data management programs | MDM is one program under data governance oversight |
In summary, data governance sets the strategy, policies and standards, while MDM focuses on the actual implementation and maintenance of master data domains.
Relationship Between Data Governance and MDM
Data governance and MDM are very complementary disciplines that both focus on improving data quality and consistency, but from different levels:
- Data governance sets the foundation needed for effective MDM by defining master data strategy, policies, principles, accountabilities and oversight.
- MDM implements governance directives specifically for master data domains through operational processes and technology.
- Data governance provides high-level oversight and direction, while MDM is an execution discipline.
- Data governance operates on all data types; MDM deals with validated master data.
- Data governance ensures policies are followed; MDM puts policies into practice for master data.
Some key ways they work together:
- Data governance establishes master data standards which MDM implements.
- Data governance metrics measure MDM program effectiveness.
- Data governance stewards assist MDM process design and compliance.
- Data governance councils oversee and approve MDM programs and projects.
For long-term master data success, strong collaboration between data governance and MDM teams is essential.
Best Practices for Data Governance and MDM
Some best practices for maximizing the impact of data governance and MDM include:
Data Governance Best Practices
- Secure executive sponsorship and tie governance to strategic objectives
- Start with foundational elements like policies and data stewards first
- Focus on business outcomes not just technology and process
- Use metrics to measure adherence and business impact
- Promote through training, communication and incentives
- Leverage data governance office or council for oversight and issue resolution
MDM Best Practices
- Define master data strategy and get agreement on domains, uses and priorities
- Establish master data requirements and information models
- Leverage reference data for validation and standardization
- Automate data profiling, cleansing, matching and consolidation
- Assign data stewards to oversee master data quality and lifecycle
- Start with high priority domains and expand program iteratively
Challenges of Combining Data Governance and MDM
Some typical challenges faced when combining data governance and MDM include:
- Unclear alignment between high-level governance strategy and MDM execution
- Weak data governance maturity and foundation hampers MDM results
- Selecting the right MDM tools and architecture
- Lack of shared standards, policies and procedures
- Getting business user engagement and managing change
- Integrating with existing legacy infrastructure and data silos
- Measuring ROI across both programs
To overcome these, organizations should focus on strong executive sponsorship, starting small and building on successes, extensive communication and training, and selecting MDM solutions that support data governance disciplines.
Benefits of Combining Data Governance and MDM
Potential benefits of taking an integrated data governance and MDM approach include:
- Greater consistency, quality and trust in master data
- Reduced errors, costs and redundancies from inconsistent data
- Faster MDM adoption enabled by data governance foundations
- Centralized oversight for sustainable data practices
- Business aligned master data program tied to analytics and operations
- More accurate analytics and reporting
- Compliance with regulations and reduced risk exposure
Overall, integrated data governance and MDM can help organizations maximize the value of their data assets, providing a central, reliable “single source of truth.”
Conclusion
Data governance and MDM are distinct but highly complementary disciplines for managing data. Data governance provides high-level oversight and strategy for enterprise data assets, while MDM delivers a focused set of practices for managing master data. By leveraging data governance’s foundations of policies, standards, metrics and stewardship together with MDM’s operational processes and technology, organizations can drive greater business value through trusted, consistent and accurate master data.