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What Is Metadata Management and Why Is It Important?

Jay Selig - 3 May 2022

Data is king in modern business. But it is one thing to simply have a Big Data architecture and another to know how to manage and optimize it over time. A major hurdle of Big Data is the presence of distributed architectures that create data silos, making it difficult for cross-organizational cooperation and for users to leverage enterprise data.

As companies accumulate more, increasingly diverse data, the need to categorize and organize it is critical. Business content like technical documentation, whitepapers, articles, and more are all more discoverable when the enterprise effectively manages metadata. As such, metadata management has become one of the most important practices for a successful digital initiative strategy and the cornerstone of every data-driven organization.

What Is Metadata?

To understand how to manage metadata, you must first understand what it really is. While many define metadata as “data about data,” that does not get the core message across. More specifically, metadata is the information that creates context for other data elements. This makes it easier to organize and locate data more easily to support a number of business functions.

There are several types of metadata that businesses regularly track and use.

  • Technical metadata provides information on the format and structure of data, like data models, data lineage or access permissions.
  • Business metadata defines business terms like business rules, data sharing rules, and data quality rules.
  • Content metadata may describe keywords, content habitat, and more.

All forms of metadata are important to the enterprise. However, it is important to understand that metadata doesn’t just describe data; it helps you use it appropriately.

 

What Is Metadata Management?

Metadata management includes everything from data analysis to data labeling to classification. This process enables organizations to gain more control over their data so they (and their business users) can discover it more quickly and leverage it for any number of processes.

The common challenge with metadata management is that every aspect of it is tedious and time-consuming when done manually. First is the actual reading and processing of informational assets. From your analysis, you can label each informational asset with the metadata you deem most important to your business. You can then classify this data catalog to your needs and establish taxonomies that help you organize the entirety of your data.

 

Why Is Metadata Management Important?

Your metadata directly impacts the quality of your data. And the cost of bad data is an astonishing 15% to 25% of revenue for most companies. This underscores the importance of metadata management. The better you manage your metadata, the better you are able to do the following with your data.

Usability

Many organizations struggle to find the data they need to deliver business insight and support key business decisions. This is often due to enterprise information architectures that are overwhelmed with poor-quality data that is completely disorganized.

Unless data is labeled and categorized properly, organizations will be unable to use it to its fullest extent — plain and simple. Effective metadata management ensures that all of your data is consistent, accurate and of the highest quality. As a result, your organization can leverage its data for any number of business processes.

Adaptability

As the enterprise grows and consumes more data, metadata management enables adaptability for different contexts. For instance, IT, sales, and compliance teams all use the word “customer” in different ways. How you define “customer” also changes over time as you may categorize them by customer status (e.g., former, current, repeat, etc.).

Effective metadata management enables each team to accurately identify information about customers within the organization’s data ecosystem. This helps them to understand and govern their systems, data and relationships with people outside of the organization.

Discovery

Content discovery applies to both internal and external users. From an internal standpoint, properly managed metadata provides structure to your enterprise data. This enables you to locate specific documents when you need them to carry out business processes and make timely, informed decisions.

For customers and end users, the ability to quickly locate the content that matters to them is critical. If they search your database and cannot find exactly what they need, they will find somewhere else to search. This ultimately reflects upon the customer experience which makes it all the more important for companies in every industry to get right.

Governance

Metadata management is also important due to an increasing need for data governance. Regulatory and compliance requirements around the world put the onus on organizations to keep track of and responsibly use their data. If you are unable to quickly locate certain data assets, you leave your organization in an unnecessary bind.

 

Implementing Metadata Management

Metadata management varies in complexity depending on the volume and diversity of data sources, use cases, users and their roles. This must be considered as you implement a metadata management solution so you can align your system with data governance compliance requirements and data analysis strategies.

Data governance is especially important as you must ensure that access to trusted data is correctly understood throughout the data lifecycle and used in the right context to avoid compliance violations. Plus, automation and self-service systems only work when high-quality data is available with a shared understanding of metadata.

Elements of Successful Metadata Management

Metadata management has three core elements, and you must create linkage between all three:

  • Terms for common business language and definitions. Sources may include industry standards, policy manuals, contracts, reference guides, and handbooks.
  • Business resource-specific attributes like systems or reports. Sources may include data dictionaries, technical documentation, or data models.
  • Data resource-specific elements like database tables or reports. Sources may include database catalogs, spreadsheets, and data models.

An effective metadata management system uses metadata that is captured, stored and governed consistently at all three levels, with clear linkage between them.

Core Capabilities of a Metadata Management Solution

When implementing a metadata management solution, you must have the following critical capabilities:

  • Data inventory with which you can automatically identify similar attributes, resolve ambiguities, and detect relationships with other data.
  • Data lineage for identifying data provenance, and impact analysis to understand what happens if any metadata element is changed.
  • Automation to actively support broader data management efforts.
  • Intuitive user experience and collaborative workflows to support a wide range of users and use-cases.
  • Semantic language understanding to support variations in terminology and establish transparent business rules with the ability to identify exceptions to data rules.

A metadata management tool can help streamline implementation while retaining compliance and security, while also automatically governing data in an accessible, organized way.

Simplified Labeling and Annotation

While labeling and annotating metadata is not complicated, it is an extremely tedious and time-consuming task. Artificial intelligence can help automate this process via entity extraction of details (such as topic, business, or location) contained within your internal data infrastructure and various content sources. However, it requires the right AI approach and technology.

Standard machine learning-based natural language processing techniques like keyword recognition lack the knowledge to discern terms with multiple meanings or synonyms that should be grouped together. So while companies can use this approach to automate annotation, the lack of accuracy is likely to result in subpar data quality.

A symbolic approach to entity extraction brings a higher level of knowledge and common sense to your model that can identify and capture keywords (and phrases) and their synonyms and establish semantic relationships between them. This automation capability streamlines metadata enrichment while also creating more accurate, high-quality metadata.


Metadata management is crucial to the enterprise effectively capturing, accessing and using data to generate business insight. However, it can be an expensive manual chore. Artificial intelligence platforms like expert.ai can streamline metadata management by automating everything from data labeling to data classification in a highly accurate manner that meets both compliance and security standards.

Learn more about how to effectively implement metadata management in our white paper, “Elevate Your Information Services Business with AI”.

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