Unifying Metadata and Master Data for Business Success

Managing different data types becomes more complex as your organization’s systems and data sources grow. Your business information needs a complete view from proper metadata and master data management. Poor management of either can lead to systemic operational problems, reduced efficiency, and weak decision-making.

For clarity and completeness, let’s look at how Gartner defines master data and metadata.

Master data is the consistent and uniform set of identifiers and extended attributes that describe the enterprise’s core entities, including customers, prospects, citizens, suppliers, sites, hierarchies, and chart of accounts.

Metadata is information that describes various facets of an information asset to improve its usability throughout its life cycle. It is metadata that turns information into an asset…it is the metadata definition that provides the understanding that unlocks the value of data.

The Evolution of Data Management Approaches

Your business probably started like many others, storing data in conventional databases that worked well for simple tracking and customer relations. Organizations processed and stored structured, relational data through traditional methods.

Data volumes grew exponentially and revolutionized the digital world. Here’s a striking fact: 90% of all existing data was created in just the last two years. Traditional data management systems couldn’t keep up, especially when handling unstructured data such as audio, video, and text formats. These growing complexities led to metadata and master data becoming more important to provide data with the context necessary for use.

Your company now faces several modern business data challenges:

• Data quality problems exist because only 3% of organizational data meets simple quality standards.
• Security risks persist as 70% of employees have access to data they shouldn’t.
• Systems and departments struggle with integration.

The rapid growth of data volumes presents new challenges in data integration, quality assurance, and privacy protection. These challenges become increasingly crucial as businesses seek to extract actionable insights while ensuring data remains accurate and secure.

Master Data Management: Building Your Foundation

Building a strong master data management (MDM) foundation requires understanding its core components. A good MDM solution should manage data that is accurate, consistent, and available.

Core Components and Requirements

A successful MDM implementation needs three main components:

• Data governance framework to maintain integrity and reliability.
• Quality management system for validation and enrichment.
• Integration capabilities for smooth data flow between systems.

Implementation Strategies and Best Practices

Note that your MDM implementation needs a clear roadmap. Successful organizations follow these key steps:

1. Define clear business requirements and objectives.
2. Set up resilient data governance with designated stewards.
3. Select appropriate MDM tools that match your priorities.
4. Start with pilot implementation in one domain.
5. Build capability through training and change management.

Data stewards protect data quality. Your MDM solution should manage this data well. Better integration and interoperability create smoother operations.

Measuring Success and ROI

Good implementation requires tracking specific metrics to measure MDM success. Your key performance indicators should include:

Data Quality Metrics:

• Data record error rate.
• Percentage of duplicate data.
• Completeness of customer accounts.

Business Impact Metrics:

• Total expense per thousand data records.
• Cycle time for new customer/product setup.
• Data compliance rate.

Financial benefits take several quarters to show up. Focus on both direct benefits like lower operational costs and indirect benefits such as better customer satisfaction when you evaluate your MDM investment.

Metadata Management: Enhancing Data Value

Metadata management acts as a compass throughout your data experience. Understanding the relationship between metadata vs master data helps you get the most value from your information assets. Companies that implement resilient metadata management can cut data management costs by up to 40%.

Key Metadata Management Principles

Your metadata strategy needs clear goals that match business objectives. Much like a library’s catalog system, metadata management works when you have:

• Standardized schemas for consistent description.
• Active maintenance throughout the data lifecycle.
• Automated capture and monitoring processes.

Good metadata management helps you improve operational efficiency and ensures data quality throughout your organization.

Tools and Technologies

A complete metadata management toolkit should contain:

• Data catalogs for centralized access.
• Business glossaries for common terminology.
• Data lineage tracking systems.
• Repository management platforms.

Modern metadata tools now make use of AI and machine learning capabilities. Automated data profiling can perform up to 2.5 million controls per minute and verify over 60 million records.

Success Metrics and KPIs

These key metrics help measure your metadata management’s effectiveness:

• Metadata accuracy and completeness rates.
• Number of registered data assets and usage patterns.
• Processing speed and automation rates.
• Compliance rates and audit success.

Companies without a metadata-driven approach spend much more on data management. These metrics help you spot areas that need improvement and support your metadata management investments.

Creating a Unified Data Strategy

A unified framework must bring together your metadata and master data initiatives to create an effective strategy. Research shows up to 68% of data isn’t analyzed in most organizations, which points to the need for a complete approach.

Integrating Metadata and Master Data Initiatives

Your unified strategy should connect business and data priorities through clear frameworks. Research shows that CDOs who link data and analytics to prioritized business outcomes are more successful than their peers.

The key integration points include:

• Implement a knowledge catalog for standardized nomenclature.
• Create cross-organizational common glossaries.
• Establish unified data topology.
• Define clear data objectives.

Resource Allocation and Budgeting

Proper resource allocation becomes significant as data volumes grow. Your budget should account for:

• Data storage and processing capabilities.
• Integration platforms and management solutions.
• Employee data literacy programs.

Long-Term Maintenance Planning

High-quality data demands continuous attention. Your maintenance plan should focus on these elements with regular audits:

• Implementing resilient governance policies.
• Establishing data quality standards.
• Creating central catalogs for insight sharing.
• Monitoring critical data elements.

The strategy needs a metadata and governance layer that increases visibility throughout your organization. This standardizes nomenclature and helps everyone follow consistent data quality and compliance guidelines.

Conclusion

Organizations must excel at both metadata and master data strategies to manage data effectively. Implementation of these complementary approaches prepares your organization to tackle modern data challenges while you retain control of quality and compliance.

The numbers tell the story – organizations using unified data strategies cut management costs by 40% and achieve better operational outcomes by a lot. Master data serves as your single source of truth, and metadata makes proper context and findability available across systems.

Your path to success will need careful planning and consistent execution. Your roadmap should focus on:

• Clear data governance frameworks.
• Regular quality monitoring.
• Detailed training programs.
• Measurable performance metrics.

Data management is an ongoing experience, not a destination. Begin with small, focused implementations and grow based on measured results. Your attention to both metadata and master data today creates the framework for continued success tomorrow.

About Traci Curran

Traci Curran serves as Director of Product Marketing at Actian focused on the Actian Data Platform. With more than 20 years of experience in technology marketing, Traci has previously held senior marketing roles at CloudBolt Software, Racemi (acquired by DXC Corporation), as well as some of the world’s most innovative startups. Traci is passionate about helping customers understand how they can accelerate innovation and gain competitive advantage by leveraging digital transformation and cloud technologies.