Enterprises must be able to turn their data into business assets in order to stay competitive. But since data comes in large volumes, varied formats and types, enterprises struggle to ensure that it can be used, shared and analyzed. This makes Metadata Management strategic for companies interested in extracting value from their data.
Metadata Management is an organization-wide agreement on how to describe information assets. That is what metadata is: a set of data that describes and gives information about other data. For example, a document will have metadata describing its file type and size, date of document creation, author(s), the dates of any changes as well as some other more descriptive metadata such as title, tags, and comments.
However, there is more than one type of metadata. Semantic metadata, for example, describes the “meaning” of data. It describes the context in which data is embedded by explaining how it relates to other data. In this sense, semantic metadata is not just a list of categories, but goes down to detail describing the “aboutness” of data. Metadata about food products, for example, could describe it in detail, including nutritional facts, country of origin, etc.
There is also active metadata, which is stored and organised in a way that enables operational use, manual or automated, for one or more processes. Active metadata makes it easier and more efficient for users to build, deploy, and operate data management applications for analytics, data science, governance, or almost any other purpose. Active metadata also makes the wider data management processes intelligent and dynamic because it is overlaid with machine learning, augmented with human knowledge and integrated. Therefore, active metadata can not only highlight missing, incorrect, or anomalous data, but also help improve the quality of analytics by automatically correcting and enriching data to improve decision-making and avoid costly mistakes.
With modern Metadata Management practices that put semantic and active metadata in place, organizations are able to link, use and discover their data. This makes them more transparent and better prepared to evaluate the value and risks associated with data and its usage.
Use cases for Metadata Management vary from company to company. However, according to Gartner, four main use cases are predominant in the search for metadata management solutions.
Data governance is the most demanded use case for metadata management. Enterprises rely on Metadata Management to build their Data Governance structure. This way, they are able to improve the quality of their data and understand its workflow. Besides complying with regulatory and business requirements, Metadata Management helps assess the impact of a change within a data source. It also supports accountability for the terms and definitions of a business glossary to lead organizations towards the development of a standardized data model.
Data risk and compliance
Since data has become one of the most valuable assets to enterprises today, the risks to data security are diverse. In addition, organizations must comply with legal regulations as well as internal policies and procedures. Metadata Management helps security and risk professionals be ahead of these two scenarios by classifying data according to risk and security needs. It facilitates data lineage, impact analysis and data management to reinforce privacy requirements.
Enterprises know the value of quality analytics. Monitoring performance and making valuable decisions based on data should be at the forefront of any enterprise. Metadata Management supports building a data catalog for the analytical uses of data across an organization. As a result, organizations avoid confusion and misinterpretation of information and reinforce analytics effectiveness.
Enterprises need to have the right infrastructure in order to be able to benefit from their data. With Metadata Management in place, they are able to identify and relate their data (and analytics) to real or desired business outcomes. By doing that, organizations can better assess what data is going to drive the most significant return on investment.
The Role of Knowledge Graphs in Metadata Management
The digital transformation requires businesses to understand data and use it in innovative and more efficient ways. Metadata Management gives businesses vital information, but as data is constantly ingested, created and transformed, its metadata also changes. If metadata is not updated, users are unable to identify the right data for the right task. As a consequence, the value of data decreases as insights and opportunities are missed.
The Knowledge Graph is a model of a knowledge domain created by subject-matter experts with the help of intelligent machine learning algorithms. The Knowledge Graph provides a structure and common interface for companies’ data and enables the creation of smart multilateral relations throughout databases. It also supports metadata management processes by providing additional context information and rules to describe the meaning of metadata in a consistent and machine-readable way.
Enterprise Knowledge Graphs, when combined with Machine Learning, advanced text mining algorithms as well as Natural Language Processing (NLP), can be used to automatically extract, link and reason about relevant metadata from documents and structured data sources. This combination of different technologies is shifting the paradigm in how organizations deal with Metadata Management. The technologies can be used to transform descriptive metadata into active metadata, making it interoperable and actionable across systems.
Knowledge graph technologies combined with machine learning algorithms effortlessly identify complex relations within content and data elements. This is possible because Knowledge Graphs organizes data like the human brain – through context and relations. By connecting data, humans and machines are able to contextualize, ultimately being able to make better and more informed decisions.
Therefore, companies can use Knowledge Graphs to augment the value of their metadata, making it reusable across data silos, capable of being injected into different systems and more processable, actionable, machine readable.
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