Building a Successful Metadata Management Strategy within a Data Governance Framework

How a Solid Foundation Helps Turn Metadata into Analytic Insights

Matt GuschwanNovember 6, 2019

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In my last blog on metadata management, we explored how businesses define and organize metadata across an enterprise. In this blog, we will discuss how a data governance framework can support an industry-leading metadata management strategy.

Metadata provides crucial information that enables accurate analytics for powerful business insights. By leveraging metadata to classify, manage and organize massive amounts of enterprise data, organizations can better understand and effectively deploy resources to support their analytics efforts.

However, establishing a successful metadata management strategy requires a proper foundation, transparent processes and the right people to execute the work. In other words, metadata management needs to operate within a comprehensive data governance framework that combines people and processes, fosters open communication and creates an enterprise-wide, data-centric culture.

Building a Data Governance Framework

An effective data governance framework should promote collaboration between data owners and data consumers to give both business and technical users complete transparency into their data. When done right, data governance produces a community approach to data understanding, empowering diverse team members to work together to define and document data. As a result, teams build consensus regarding data assets, reduce confusion and ensure appropriate data usage.

By assigning data ownership for critical data assets and instituting policies to regulate data access, data governance ensures that business users have appropriate access to the right data, with links to resources that are easily identified and readily available to field questions and address issues. With a foundation of understanding, support and collaboration, business users across the enterprise can easily leverage data to generate actionable business insights.

Once a fundamental framework is in place, modern data governance initiatives may integrate advanced analytics and machine learning to enable the automatic capture and monitoring of additions or changes to the data. Once uncovered, these changes can be  investigated and resolved to offer additional insights on data.

Effective data governance will use metrics to track the currency of policies and of metadata, the performance of the data governance team and the overall effectiveness of the organization.

With a solid data governance backbone, organizations can build a prosperous metadata management strategy.

Designing a Metadata Management Strategy

Metadata management can sound like an overwhelming initiative, but the increase in data value makes these efforts critical to any successful enterprise data management strategy. The first steps should be to adopt a metadata model, establish oversight, manage metadata and acquire diverse types of metadata.

  1. Adopt and Adapt a Metadata Model: All businesses are different. Each one must customize their metadata model around their specific business needs. A metadata architect can make sure that the organization collects the right inventory of metadata to solve individual business dilemmas.
  2.  Establish Oversight and Management of Metadata: Another key role in metadata management is the metadata specialist who acts as a project manager to ensure everything goes as planned. The metadata specialist must understand the metadata model to guarantee the business accumulates and maintains the correct data, while making sure all work is done properly and on time. The position requires technical expertise in metadata to plan, design and implement a comprehensive strategy.
  3.  Acquiring Different Types of Metadata: My last metadata blog discussed why businesses must collect physical, logical and conceptual metadata. Since physical metadata deals with the location of the data and logical metadata deals with the flow of data through an enterprise, collection of these two types of metadata requires an analyst with technical skills. Once information is collected, the metadata is automatically refreshed. Conceptual metadata, however, is about the meaning and purpose of data from a business standpoint, and so it must be harvested manually from business users.

 Once an organization has built a data governance foundation and established a metadata management strategy, business and technical users can work together to document relevant metadata – the metadata that identifies and locates data, verifies what it means, determines where it came from, and if it changed along the way. They can also determine if all the data is there and they can discover associated data sets, reports or other outputs. As a result, business users have the ingredients to develop analytical insights that improve business processes and augment growth while technical users can quickly troubleshoot issues.

Are you looking for additional information about building a metadata management strategy with data governance? Check out this eBook, Why Metadata Management is Meeting the Growing Demands of Data Governance.


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