What is Metadata Management?

Turning Your Data into a Strategic Asset
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Enterprises must be able to turn their data into a business asset in order to stay competitive. But since data comes in large volumes, varied formats and types, enterprises continuously struggle to ensure that they can use, share and analyze their data. That is why metadata management is increasingly becoming a strategic initiative for enterprises.

Metadata Management is an organization-wide agreement on how to describe multiple technical and business facets of enterprise information assets. These valuable descriptions are known as 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.

Organizations need metadata to link, use as well as discover their data. Even more, metadata management helps organizations to provide transparency in the use of data as well as helps to evaluate the value and risks associated with data and its usage.

The growing need for data governance, risk and compliance, data analysis and data value still drives strategic requirements in metadata management and the growth of its solutions.

Gartner (2018): “Magic Quadrant for Metadata Management Solutions

Why Metadata Management?

Companies are currently using metadata management either for inventory or to leverage data assets. 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

Data governance is the most demanded use case for metadata management. Enterprises rely on Metadata Management in order to build upon 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, it also helps to 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 to this, organizations must comply with legal regulations as well as internal policies and procedures. Metadata Management is helping 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.

Analytics

Enterprises know the value of quality analytics. Knowing how well a company performs and making valuable decisions based on your 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.

Data Value

Enterprises need to have the right infrastructure in order to be able to benefit from their data. With Metadata Management in place, organizations are able to identify and relate their data (and analytics) to real or desired business outcomes. By that, organizations can get a better assessment of what data is going to drive the most significant return on investment.

Metadata Management Capabilities using Graph Technologies and Semantic AI

As mentioned above, metadata management is an organization-wide agreement on how to describe multiple technical and business facets of enterprise information assets.

Semantic AI supports metadata management processes by adding a semantic layer on top of existing metadata systems. This provides additional context information and rules to describe the meaning of metadata in a consistent and machine-readable way. Semantics helps to harmonize metadata to make it interoperable and actionable across systems.

We will explain the main aspects of the semantic layer in the upcoming lines.

W3C Standards-based Semantics – Unifying Language Across the Organization

Semantic AI is based on W3C standards, ensuring that industries adhere to a specific way of publishing their data. Data standards also help to work in a more agile and DataOps manner, helping departments to improve interoperability and to further break down data silos after generations of building them up.

The Rising Role of Context – Identifying Relationships between Data Elements

Knowledge graph technologies combined with machine learning algorithms (shortly known as Semantic AI) effortlessly identify complex relations within your content and data elements. This is possible because Semantic AI organizes data like our brains – through context and relations. By connecting your data, you (and also machines) are able to gain context within your knowledge; helping you to make informed decisions based on all of the information you already have.

Defining Business and Data Rules for Compliance

When implementing Semantic AI, enterprises rely on knowledge domain experts to establish business and data rules together with a unified vocabulary. This results in increased quality and traceability of the information exchanged as well as advanced reasoning capabilities, helping to reduce the cost of compliance. What is also important to note is that Semantic AI is also able to rule out impossibilities from set rules and limitations, helping to avoid false positives.

PoolParty Semantic Suite: Solutions for Metadata Management

PoolParty Semantic Suite is an innovative and enterprise-ready technology platform which helps enterprises to link data across silos to turn their data into business assets. It uses Enterprise Knowledge Graphs, machine learning, advanced text mining algorithms as well as Natural Language Processing (NLP) to automatically extract, link and reason about relevant metadata from documents and structured data sources. Powered by Semantic AI technology, PoolParty Semantic Suite is able to add facts about the extracted metadata, helping to drive in-depth text analytics.

PoolParty Semantic Suite fully supports the following Metadata Management processes:

  • Identify compliance and exceptions based on predefined rules
  • Support business glossaries, taxonomies and ontologies to address semantic variations
  • Establish a standards-based and machine-readable metadata management framework
  • Enable subject matter experts to create and manage semantic metadata based on user-friendly interfaces
  • Help enterprises gain transparency in the management of their data to effectively execute information governance and regulatory compliance
  • Support unified views of enterprises’ structured and unstructured data assets without data migration
  • Integration with SharePoint, Office 365, Drupal and Confluence for consistent and automatic tagging.

The Future of Metadata Management

Most enterprises are aware that developing a metadata management strategy is key to staying competitive – especially in a fast-moving market. However, many aren’t sure how or where to begin. Here at the Semantic Web Company (SWC), we believe that making your data intelligent should be a priority for any digital business.

While many large enterprises have teams of data and analytics experts helping them ensure that they are getting intelligent data, SWC’s PoolParty Semantic Suite provides a comprehensive platform with all of the tools and methodologies required to leverage the value of your metadata by adding a semantic layer on top: starting from building up business glossaries, taxonomies and ontologies, to large-scale Enterprise Knowledge Graphs that are generated based on a fused approach exploiting knowledge engineering, text mining and machine learning.

PoolParty’s approach will provide you with four main advantages:

  • Benefit from consistent metadata across your organization including semantic variations and multilingualism
  • Start tagging your data automatically by matching the relevance of terms and context found in text and documents against your standards-based Enterprise Knowledge Graph
  • Discover the knowledge that your enterprise possesses by finding patterns in your data that hide previously undetected pieces of knowledge
  • Gain in-depth analytics from your structured and unstructured data to find patterns and topics of interest that will help you to stay compliant with all relevant regulatory frameworks or also to make well-informed decisions.

Any Metadata Management strategy should be built around the insight that the value of metadata is highly dependent on its automatic interpretability, which should work independently from the applications which make use of the metadata. PoolParty can help by using a standards-based approach in order to better organize metadata and data and content in general in a highly scalable way.

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