Reduce data silos and duplication by leveraging standardized naming conventions to describe both business and IT concepts. Semantics provide a “universal language” for describing disparate systems with active metadata.

Use semantics to present business concepts with the language and vocabulary of your business users. A knowledge model enables similarities and differences in vocabulary to be recognized and leveraged in your business context.

Leverage metadata standards, harmonize metadata definitions and create a rich metadata layer across all data sources—structured, unstructured or semi-structured. With this approach, you can correctly categorize, label and define within your specific domain to achieve better quality.

Create metadata that is completely auditable and transparent to support information governance and regulatory compliance initiatives.

Metadata is information about data that helps users derive value and meaning from the data. Common metadata items might describe the format, the origin or the validity period.Semantic metadata describes what the data means to your organization by adding meaning and context. It optimizes the reuse and simplifies the retrieval and tracking of content to enhance the efficiency of various business functions.

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Metadata management is the practice of handling data that describes and explains other data. It adds additional meaning to and describes your organizational assets with details like source, type, owner, etc., leading to better data usability and findability.

A fundamental metadata management framework integrates metadata creation, discovery, governance, storage and distribution.

• Metadata creation – Describe one or more aspects of data by tagging and enriching data with metadata.
• Metadata identification – Locate metadata that accurately represents the data and follows the established governance framework by using vocabulary-based classification and entity extraction.
• Metadata governance – Create a collaborative approach for metadata management and creation that makes data usable and meaningful to specific business contexts.
• Metadata storage – Store metadata for business needs in a centralized repository.
• Metadata distribution – Implement a formal framework for metadata distribution to downstream systems.

There is huge business value locked away in enterprise information. Insights based on metadata will become a fundamental requirement for organizations aiming to successfully implement transformations and enhance collaboration and business operations. These insights will also help organizations fulfill the increasing requirements for data governance, compliance and analytics. To harness this value, businesses must understand their data, the information and knowledge it contains, how it can be applied in their business context and how it enhances the insights from unstructured data.

When data leaders build a data strategy around metadata and keep their active data, active metadata and active meaning consistently connected, data agility can be realized.

By defining and leveraging information about the data itself (descriptions, definitions, relationships, etc.) you can achieve better data quality, governance, unity, reliability and security—among other benefits. Making thoughtful use of data enables organizations to continuously find new and innovative applications for business data that will power their people, processes and technologies.

Metadata management capabilities collectively enhance an organization’s ability to manage their metadata efficiently, supporting data quality, compliance and use for informed decision-making and operational success. InfoTech Research Group outlines the most important vendor capabilities in metadata management and lists vendors that provide robust sets of tools for creating and managing metadata.

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Best practices for metadata management include establishing a clear governance framework that defines roles, responsibilities and processes for metadata creation, maintenance and usage. Integrating metadata management practices in the overall data lifecycle helps capture and maintain the metadata throughout its lifecycle, fostering accountability and transparency.

Furthermore, by fostering a collaborative environment, subject matter experts can competently handle metadata within the bounds of the structured governance framework, offering their specialized expertise and perspectives to enhance the metadata's quality. It is essential that subject matter experts track and audit the changes made to the metadata, as well as communicate and share feedback with other stakeholders. Leveraging automated tools for metadata management can significantly enhance the efficiency and accuracy, while minimizing the risk of human error.

Metadata management can become difficult when dealing with a growing amount of data produced in different formats. The bigger the datasets, the harder it is to label and manage metadata. Metadata can be even harder to manage if users input metadata manually or it’s system-generated, prone to errors and there is no applied context. Additionally, different teams might use diverse terminologies, definitions or standards for identical data, leading to misunderstandings and ambiguity. This can prevent the effective sharing and reuse of data, leading to silos and inefficiencies. These limitations can severely hamper an organization’s ability to quickly and easily make changes to how information is interpreted and acted upon. Therefore, it is important to harmonize the terminology and metadata across the organization, using common vocabularies, taxonomies and ontologies. This can facilitate data integration, interoperability and collaboration, as well as improve the quality and usability of the data.

Effective metadata management is crucial for successful AI implementations, helping to turn high-quality data into accurate and reliable generative AI results. Metadata acts as a guide, reducing AI hallucinations and enhancing the trustworthiness, value and contextual relevance of AI outputs. By providing a structured framework, metadata enables AI systems to understand data's origin, context and relevance, leading to dependable insights and more informed decisions.

Adopting a semantic approach further amplifies these benefits by creating a more interconnected and meaningful data environment. This approach facilitates deeper insights and more intuitive AI interactions, transforming raw data into actionable intelligence. To explore these advantages in detail,watch our on-demand webinar and learn how robust metadata management can revolutionize your AI initiatives, driving trustworthy, valuable and contextually rich outcomes.

The development of a metadata management strategy should be aligned with your data and analytics strategy. Progress Semaphore supports enterprise-wide use of metadata. To be more successful and deliver business value, start your metadata management journey with a well-defined use case in one problem domain and expand outwards into the rest of the organization when business value has been demonstrated.

Progress Semaphore takes a holistic approach to metadata management, leveraging semantic knowledge models, rules-based classification and sophisticated semantic processes to yield precise, complete and consistent metadata that embeds meaning across the data ecosystem and drives intelligence. This method offers a faster and more cost-effective route to much-improved data quality. With Semaphore, organizations can:

• Eliminate the effect of data silos across business functions​
• Harmonize the diversity of data sources and types​
• Unify data from systems spread across physical operating environments
• Meet the growing demands for real-time and event-driven data for decision making​
• Support operational analytics and business-led data modeling activities

Metadata management is no longer about collecting metadata from various sources and putting it into a repository for analysis. It’s about harmonizing metadata from disparate sources, creating additional metadata to enrich the data at the source and extracting critical facts from the data for processing and analysis. Semantic technologies uniquely provide a common language for describing all aspects of an information ecosystem. Semantics transform decision making through higher-value metadata that drives new insights.

• Metadata creation, harmonization and management are intrinsic to the business process
• Information assets are enriched with precise, complete and consistent metadata
• Contextual understanding is enhanced by relationships in the semantic model
• Vocabulary equivalences are established in the model and used to harmonize metadata from diverse systems
• Metadata is contextualized using internal and external sources to make the data fully self-describing

With a semantic approach to metadata management, organizations can solve previously intractable problems, drive time to value and accommodate differences in vocabularies.