Metadata management tools are considered an increasingly important part of a data-driven organization’s overall software collection. These tools help businesses make better decisions by providing useful context and insights about their stored data. Metadata management tools can also be used to improve the customer experience and keep customers longer. Additionally, they allow non-technical users to do research.
Active metadata management is needed for modern Data Governance programs.
For a business to become data-driven, it must have access to metadata management tools – and use them. These tools allow a business to access the resources needed for making analytics-based decisions. As a consequence, profits and revenue are greater than for businesses that make their decisions using “gut feelings” and speculation.
An additional reason for increasing the use of metadata management tools is that, per legal changes around data privacy, the collection of third-party data has been dwindling, and will be coming to an end, soon – think of it as the protection of both your and your customers’ privacy. The steady elimination of third-party cookies (data collected for purposes of resale) is forcing businesses to rely on first-party data (data collected directly from your customers).
The use of metadata management tools makes it much easier to find and work with first-party data.
However, a business that cannot quickly locate useful data will suffer the frustration of its customers and give its competitors an advantage. Additionally, with the loss of third-party data and the ability to track a customer’s overall data use, businesses are rapidly developing their own strategies for using metadata to manage their first-party data.
Direct first-party data collection from the customers can be seen as an opportunity to learn more about the customer’s preferences, characteristics, and interests, and to develop a “friendly” relationship.
What Is Metadata?
Metadata is essentially a complex labeling system that describes data files and assets and helps in locating and understanding the source, structure, context, and nature of the data. It allows staff to easily organize, categorize, and retrieve data/information.
Both humans and computers can read metadata to identify the data file, and can communicate useful information for the purpose of analytics, such as formats, file sizes, permission details, etc.
With the return to relying on first-party data collection, metadata management becomes more critical. The most basic metadata for text documents are:
- Author
- Date created
- Date modified
- File size
The metadata for an audio track’s metadata should contain:
- Singer
- Album name
- Track duration
- Copyright
A photograph or image’s metadata should include:
- Type of image
- Color space
- Resolution
- Dimensions
Passive Metadata vs. Active Metadata
Passive metadata describes metadata that has been created manually and is managed and processed by humans. This metadata process has been the historic norm until automated/machine learning services were applied to metadata. Now, passive metadata is used to add “uniqueness” to the labeling system, making it easier for humans to locate, understand, and make use of the data.
Passive metadata remains static unless it is altered manually.
Active metadata management has become crucial for the effective use of a modern Data Governance program. Without it, a Data Governance program can become both inefficient and ineffective. This can lead to poor decision-making and increased risk.
More and more, active metadata is using machine learning to automate a variety of metadata tools and platforms. It supports much more updated information than passive metadata and can be used to make more intelligent decisions. Metadata management tools and platforms can apply machine learning to provide insights about the data.
Active metadata can improve both analytics and decision-making by supplying additional insights into the data.
Active metadata provides a comprehensive view of the data, including data lineage and context. It can help managers understand the source and history of their data. Active metadata can also ensure that the data is in compliance with various regulatory – GDPR, CCPA, LGPD – requirements and is being used appropriately.
Tools for Managing Metadata
There are a number of tools available for supporting metadata management. Listed below are some basic tools that can be accessed individually, rather than as part of a metadata management platform.
- Data catalog: Software that creates an organized inventory of an organization’s data assets. Data catalogs use metadata to manage the data. They also enrich metadata to help with data discovery. Collibra, DataGalaxy, and Atlan offer data catalogs.
- Data lineage: This is a step-by-step mapping of the data’s journey as it moves through the system, and records any changes to the data. Data lineage can be especially useful in showing how a customer’s personal privacy is being protected. Collibra and DataGalaxy also offer data lineage tools.
- Business glossary: A list of the organization’s data assets, a business glossary often contains technical metadata. DataGalaxy and Atlan offer business glossaries.
- Metadata tagging: The process of linking the appropriate terms and descriptors with your digital assets. Metadata tagging is used in photo-sharing applications, social media (in the form of geotagging, user tagging, and hashtags), and more. With so much content stored and consumed in digital format today, metadata tagging is a crucial component of modern information management. TagSpaces offers open-source metadata tagging software. Refinitive offers an intelligent tagging service.
Metadata Management Platforms
There are several metadata management platforms available. Each of these platforms should contain the basic tools listed above, plus they often contain additional tools. The selection of a platform should be based on the needs of the business, and the tools that are useful to meet those needs. Listed below are some of the more popular metadata management platforms.
Informatica Metadata Management: This platform is designed to help businesses access and use the value of all their data with active metadata. Informatica’s metadata management platform is designed to scan the metadata used by the business’s data systems, including file systems and databases and integration tools. It will discover, classify, and document the key data elements and provide detailed metadata and the data’s lineage.
Dataedo: A metadata management platform that enables businesses to catalog their data in a central metadata repository quickly. It will annotate each data asset, build a business glossary while mapping it to the data dictionary, and classify sensitive data. Additionally, Dataedo offers both an online cloud version and can be downloaded and used on-premises.
Oracle Enterprise Metadata Management: This metadata management platform is considered comprehensive. It can harvest and catalog the metadata from most metadata providers, including relational, Hadoop, ETL, etc. Oracle’s platform also provides interactive searching of the metadata and offers data lineage.
Alation: The platform provides a powerful and intelligent data platform that supports several metadata management applications using such features as search and discovery, Data Governance programs, and digital transformation. Alation uses machine learning to analyze how the data is being used and to identify patterns within the data’s use.
Unlocking the Hidden Value of Metadata
Metadata management tools are critical for the success of any business wishing to stay competitive in a data-driven world. Organizations investing in metadata management tools or platforms can see significant benefits. These benefits include improved Data Quality, an improved Data Governance program, better decision-making, and increased efficiency. The potential for improvement makes it clear metadata management is a necessary investment for any business that wants to be successful in our modern economy.
Effective metadata management tools help businesses to optimize their data processes while reducing overall costs and driving innovation. They allow organizations to improve their understanding of their data, ensure its accuracy and consistency, and use it to accomplish business goals.
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