Top 5 metadata management best practices

Metadata management best practices

Organizations must contend with massive data volumes, disparate data sources, complex compliance regulations, growing security threats and stiffer competition. Five metadata management best practices offer a foundation to start building an effective management program using a systemic approach.

1. Make a strategy

IT teams planning to a launch a metadata management effort should start by developing a strategy that clearly identifies the project's objectives and what it will take to achieve them. They should learn everything they can about the data and its metadata, such as where the data is located, how it is formatted and what metadata is being tracked. They should also determine how long it will take to complete each step of the rollout process and what resources they need to carry out those steps, including where they will store the metadata.

IT teams should define goals and KPIs that are specific to the project. The goals should be consistent with the organization's goals and reflect the project's expected outcomes.

Goals should take into account the organization's specific circumstances. For example, the metadata goals for a medical center won't be the same as the goals of a food distributor. The KPIs should align with the goals and provide a measure of the progress toward achieving them.

2. Create a metadata management team

A metadata management strategy cannot be implemented without a qualified team in place dedicated to the project. A dedicated team can refine and implement the strategy, as well as ensure it continues to move forward in an efficient manner after its implementation. To this end, metadata management is not a one-time event. It's an ongoing process that requires a team of professionals to monitor, evaluate and adjust the management process over time. Team members need clearly defined roles and responsibilities and should receive the resources and training they need to execute their duties.

Metadata management should be part of an organization's larger data governance strategy. It's an organization-wide effort, and the team should reflect all business units that interact with the data. Team members should also include individuals whose skills and responsibilities reflect the different ways they use and oversee the data and its metadata. For example, the metadata team might include representatives from IT who manage storage and other infrastructure, stakeholders from the marketing department who consume the data and people in product development who build apps that rely on the data.

3. Adopt metadata management standards

A metadata management strategy must define the standards and policies that specify how to structure, classify, capture, store and use metadata. Concise standards and policies ensure metadata's consistency and facilitate interoperability across the organization and its applications. Without them, it's more difficult for users to find, access and understand the data. Standards provide the structure necessary to manage and interact with the metadata. The standards should have clear documentation and communication to ensure compliance across the organization.

Organizations often adopt a standardized schema to apply to their metadata, such as ISO 19115, which focuses on geographic data, or ISO 27001, which is concerned with security management. Other common metadata standards include Dublin Core, Data Documentation Initiative and Publishing Requirements for Industry Standard Metadata. The metadata team is responsible for implementing mechanisms to monitor and enforce the applicable standards. The team should also regularly review the standards and policies for timeliness and validity.

4. Incorporate AI and ML when managing metadata

Metadata management is generally moving from a passive approach to an active one. Passive management is a highly manual process that relies on static metadata snapshots to describe each file's attributes. Active metadata management is an ongoing, automated process in which the metadata is continuously updated to reflect changes to the data.

The active approach enriches metadata with descriptive information about the data's lineage and how it flows. Active metadata management can benefit from advanced technologies, such as AI and machine learning (ML).

Incorporating AI and ML with metadata management enhances automation and the processes used to capture and classify metadata. The technologies also increase efficiency and reduce the need for manual intervention. The use of AI and ML makes it possible to extract metadata from a wide range of data sources, including databases and data dictionaries. It can also address data quality issues by analyzing patterns and anomalies in the data, tracking data lineage better, and uncovering patterns and associations within the data. AI techniques enhance data integration and transformation, governance and compliance, and privacy and security.

5. Select and implement a metadata management tool

The tools an organizations uses to implement their metadata management strategies can make the difference between success and failure. The right tool properly collects, stores, catalogs and maintains metadata. An active metadata management tool can handle all types of metadata and extract metadata from on-premises and cloud-based data stores. The tool should provide an intelligent platform that incorporates AI technologies and enables the organization to derive the greatest benefit from the metadata.

One of the most important aspects of an intelligent metadata management tool is its automation capabilities. It should automate and streamline the entire metadata management process, from capturing and cataloging to data discovery. The best tools scale as demand grows and integrate seamlessly with data analytics platforms and other systems. Tools should adhere to data security, privacy and governance policies. Effective and efficient metadata management tools are simple to use and flexible enough to meet evolving metadata needs.

Robert Sheldon is a freelance technology writer. He has written numerous books, articles and training materials on a wide range of topics, including big data, generative AI, 5D memory crystals, the dark web and the 11th dimension.