Metadata Management Success Requires Effective Active Metadata

With data today in constant motion, automated data management strategies are critical to meet operational objectives and build a competitive advantage. In this two-part series, I explain how active metadata and data governance are transforming how organizations manage and leverage data.

Success in any organization today depends on understanding, harnessing, and deploying data resources to support enterprise departments. Effectively leveraging metadata is essential to these efforts. It represents a powerful tool to help organizations classify, manage, and organize massive amounts of data and select the right data for advanced analytics to drive actionable insights.

However, as data is ingested, created, and transformed across an organization’s data supply chain, metadata is also changing. If metadata is kept current and updated, both data value and data consumer understanding decline. If users don’t understand available data assets, they’re less likely to choose the right data for the right task. Consequently, insights are missed, opportunities are lost, and data is devalued. 

Incorporating Active Metadata Into a Metadata Management Strategy

Metadata management delivers vital information on assets in data repositories to both business and technical users, including the location of data, when and how it was created, and more. Still, the details on data’s lineage, transformations, quality, and relationships to other data can change at any time. 

By refreshing metadata regularly and applying machine learning algorithms, organizations can reduce the need for manual tasks and ensure metadata descriptions remain complete, correct, and dependable across the data supply chain. Not only does active metadata ensure accurate representation of the lineage of information, but it also helps to proactively manage risk related to changes in data quality and data use.

The Significance of Active Metadata and Automation

Active metadata works by leveraging details about the information that is collected through automation and stored with other critical physical, logical, and contextual metadata. Machine learning aids in data management, maintenance, and asset curation by automating tasks such as cataloging and tagging data, identifying similar data sets, and connecting associated business terms.

Beyond automatically updating metadata for critical governance functions, active metadata also guards against data quality risk. It will crawl descriptors within metadata and identify similar data sets and apply the same quality rules to each of them. This significantly reduces the time and resources required to manage data quality, eliminating the need to manually construct quality rules for every data asset.

Beyond automating metadata updates and managing quality risk, it also provides crucial context around diverse data sets. In addition to asset availability, it provides deeper insights into suitability for a specific purpose. Data’s value is contingent on its proper use, and active metadata helps guide business users to the exact information they need for a given project or purpose. This increases the accuracy and quality of analytics results and drives business outcomes. 

Businesses can also leverage active metadata to build recommendation engines and enable easy data discovery. For example, when a user is searching for data, recommendation engines can guide them to similar data sets that may also be relevant. Without these recommendations, pertinent and potentially valuable data sets may go undiscovered. The same active metadata descriptors can also inform businesses about data usage and outdated or redundant information, so old, irrelevant, and duplicate data can be eliminated.

In order to create active metadata and enable this automation for updating data and identifying relationships, businesses need to implement the right tools, technologies, and processes. 

Selecting an Integrated Tool to Enable Active Metadata

Active metadata can elevate any organization’s data management strategy to boost data governance, data quality, and analytics use cases. Ideally, businesses will have a comprehensive solution that integrates all three of these data management domains.

The tool must feature both pre-built and customizable connectors to facilitate quick and easy harvesting of descriptive metadata from varied sources. This allows automated active metadata to provide a complete view of the organization’s data landscape, including data inventory, location, assigned owners/stewards, and data lineage and relationships. Real-time visibility into available data assets and attributes increases users’ data fluency, with readily accessible data catalogs and business glossaries that provide definitions, synonyms, and related business terms. Together, these governance tools allow users to easily define, track, and manage data and disseminate information appropriately for reporting and analytics.

The tool should also incorporate data quality capabilities that establish quality rules and data integrity checks for completeness, conformance, and validity. The active metadata then ensures data is transformed correctly, as it flows through multiple systems and maintains data accuracy and reliability. Machine learning analytics capabilities should also be used for ongoing monitoring and quality improvement.

Finally, automatic data discovery capabilities within the tool should allow for the constant capture and monitoring of changes to metadata. Changes in metadata are then automatically discovered and applied across the data supply chain to deliver meaningful insights.

Active metadata is the key to metadata management success. The many use cases for active metadata —including compliance, target marketing, risk mitigation and more — increase efficiencies and data reliability, eliminate manual interventions, and help businesses rise above the competition.