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MDM and Metadata Management: What’s the Difference?

By Scott Moore, Director of Presales, Semarchy

Managing metadata is a crucial aspect of data management. It involves organizing, structuring, and storing metadata –  in other words, data about data.

It plays a vital role in maintaining consistent and reliable master data, which is the core asset that fuels business processes.

Master Data vs. Meta Data

Metadata management refers to the strategy or approach for organizing, structuring, and storing metadata – contextual information, or data, about data. It includes details such as data types, classifications, table names, content tags, and more.

The aim of metadata management is to improve the usability of information assets throughout their lifecycle. It ensures that there are no metadata silos and that metadata from various tools and systems in the data stack is compiled, assimilated, and stored in a way that’s easy to access and understand.

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It shouldn’t be confused with its corollary, Master Data Management (MDM), which involves building a unified view of master data or key business attributes. Master data describes products, customers, partners, employees, locations, and other essential business entities. It serves as the foundation for all transactional processes and is shared between systems of record like ERP, CRM, HR, or plant maintenance systems. MDM aims to ensure a single, high-quality source of truth for these critical data assets across business systems.

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Put simply: metadata contextualizes master data.

The Challenges of Master Data and Metadata Management

Effective MDM and metadata management come with their own set of challenges. Metadata management often faces the issue of metadata silos, where data catalogs compile passive metadata from various sources, but in turn create yet another silo.

To overcome this challenge, active metadata management sends enriched metadata back into every tool in the data stack, eliminating silos and providing context wherever and whenever it’s needed.

MDM also presents challenges such as data consolidation, data integrity, and data synchronization. These challenges can be addressed through effective data governance frameworks, data stewardship, and data quality management strategies. It’s essential to establish clear processes and guidelines for creating and updating master data, ensuring compliance with internal controls and external regulations.

Key Concepts in Metadata Management

In metadata management, there are three types of metadata that must be collected and organized:

  • Physical: Physical metadata describes the physical location and contents of data sets.
  • Logical: Logical metadata provides information about the flow of data through an organization’s system.
  • Conceptual: Conceptual metadata conveys the meaning and purpose of each data set as it applies to the business.

Metadata governance is another key concept, involving the establishment of policies, processes, and standards for managing metadata throughout its lifecycle.

Deriving Business Value From MDM and Metadata Management

Effective metadata management improves data quality by providing context and meaning to data, and  makes it easier to find and use relevant information. Additionally, it increases operational efficiency by streamlining workflows and facilitating collaboration between data users.

MDM, on the other hand, ensures consistent and reliable master data, enabling accurate and informed decision-making. It supports regulatory compliance by maintaining data integrity and providing a single source of truth. Moreover, it enhances the customer experience by ensuring accurate and up-to-date information for personalized interactions and targeted marketing campaigns.

Implementing MDM and Metadata Management

Implementing MDM and metadata management requires careful planning and execution. It is essential to define what constitutes master data and how it supports the views of each business unit or stakeholder.

Collaboration between business and IT is crucial to ensure successful implementation.

Best practices for metadata management include using enterprise-grade technology to automate the collection of physical and logical metadata. Conceptual metadata requires human input. Active metadata management should be adopted to eliminate silos and provide context throughout the data stack. Additionally, data governance frameworks and data stewardship play a vital role in maintaining data integrity and compliance..

Future Trends and Considerations

The future of MDM and metadata management is influenced by emerging trends and technologies. Artificial intelligence and machine learning can be leveraged to automate metadata management processes and improve data quality. Semantic metadata, which focuses on the meaning and relationships between data elements, is gaining importance in ensuring accurate and context-rich metadata.

Evolving data privacy regulations and data governance frameworks also impact metadata management and MDM practices. Implementing robust data governance solutions and leveraging advanced technologies, including MDM software, will be crucial in adapting to these changing requirements.

In conclusion, master data metadata plays a vital role in ensuring consistent and reliable master data. Effective metadata management and MDM enable organizations to improve data quality, enhance data discoverability, and increase operational efficiency. By implementing best practices and staying abreast of emerging trends, organizations can unlock the full potential of their data assets and drive better business results.

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