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Metadata Management Tools Summarized

metadata, metadata management, MDM, data governance, data management


Gone are the days when locating a file meant digging it up from deep in the storage and brushing the dust off. With the digital era at its peak, almost every organization has a digital catalog of its files and documents. Searching for files is now easier than ever before. Typing in a keyword to the search bar, one can retrieve every single document that uses that word.

So, how is this ‘look and find’ possible?

The answer to this puzzle lies in metadata. Metadata, very simply put, is the information regarding data assets of an organization that helps user to identify data with ease. It includes details such as title, author, time of creation, time of editing, storage location, topical tags, and accessibility rights. This ‘metadata’ can be attached to every data asset manually or be automated with the help of metadata management tools. Metadata management refers to the organization and maintenance of metadata.

Metadata Management (MDM) Tools are software solutions that automate the various aspects of metadata management. They include embedded assessment and classification functionalities that allow repository, data lineage, attribute and data framework creation. They are capable of data discovery, profiling, cataloguing and glossary creation and help improve the accessibility and understandability of an organization’s data assets.

Features of Metadata Management Tools

Structured and Unstructured Data Management

Metadata management tools can create tags for data irrespective of their structural organization. Structured and unstructured data can be categorized with the addition of metadata.


Provision of sample data helps train the software build a better attribute creation system. With the passage of time, as more data is processed, the software becomes better at understanding data and creating more detailed metadata.

Data Lineage Creation

Metadata tracks the journey of a data asset from its origin and all usage information. Authorship, date of creation, dates of edits and details regarding who accesses data contributes to the creation of lineage of different kinds of data assets. This kind of tracking helps organizations monitor the usage of data and strengthens data governance.

Repository Creation and Control

Metadata repositories are data warehouses specifically made to store metadata. These repositories do not hold the actual data asset. They store only the abstract layer of metadata that is attached to the data asset. The repositories are highly structured, easy to navigate and can be controlled by users with accessibility rights.

Business Glossary Creation

Similar to data governance tools, metadata management software has the ability to create a glossary of terms that can function as a guide for employees. Glossaries not only help users identify key search attributes but provide a list of keywords that users can add to data in the future to make the data architecture better.

Impact Analysis

With access to all data assets as well as data lineage, some metadata management tools also have embedded impact analysis functionality. Impact analysis is the study of business structure, data usage, resource sharing, communication channels and interdependencies of an organization.

Semantic Framework Creation

Sophisticated advancements in natural language processing allow the software to classify data based on semantic understanding. This makes the entire data architecture more sound and accurate.

Data Discovery, Data Profiling and Classification

Based on relationships between the metadata, data assets are stored together to make locating data easier.

Data Cataloging

Data is stored in a central catalog that can be used to search for data assets.

While some features like data discovery and data classification can also be found in other data management tools, other features like repository control and semantic understanding are usually only found in metadata management tools.
Metadata acts as a reference point, and provides historical usage related context, improving the usability and relevance of data over extended periods of time. It is a vital part of governing data, garnering insight, and strengthening business intelligence. A well maintained, detailed metadata system enables the formation of an excellent data management system. When an enterprise can release the full potential of the data collected, its data governance, customer experience management, client relationships, employee morale, advertising / marketing strategies and productivity can all be improved drastically.