We all strive to make good business decisions, but with the continuous improvements in technology and data storage it can often be difficult to sift through all of the available data. Much of it may be good data—in other words, correct data that is timely, useful and relevant and has been collected using valid, reliable methods. But, being able to identify “good” data is not definitive for confirming and the right data to use for decision-making. That is why it is important to know what constitutes meaningful data—or, the data that is needed to make better decisions.
Metadata, which is simply data about data, allows us to understand the identity and characteristics of data (such as, name, origin, date created, date modified, definition, data type, source, valid values, security classification, security policies, lifecycle, retention policies). Metadata is essential to understanding the meaning of data within a specific context of use, by providing answers to key questions such as those shown in the following figure:
Equally important to being able to identify important, meaningful data, is the ability to communicate about it in a common language that supports understanding its meaning across departments and systems. To this end, semantic standards provide a common language that makes communication of meaningful information and its relationships possible:
And, as a result of 15 years of effort by the World Wide Web Consortium (W3C), we have a collection of semantic standards (shown in the figure below) that allow us to:
- Say things (using RDF, the Resource Description Framework)
- Use a common vocabulary (using RDF schema RDFS)
- Make some inferences about what we are saying (using OWL, the Web Ontology Language for formally defining meaning)
- Ask questions about what has been said (using SPARQL, the query language for RDF)
- Verify all of this using rules (using SHACL, the Shapes Constraint Language)
The semantics standards were developed to represent two key types of metadata: descriptive metadata, which states or explains something about the data, and relationship metadata, which connects or points to other objects that participate together in a process or analysis or job, thus making a model of the actual uses, flow and impact of data in the enterprise.
Both types must be captured to have an effective, metadata-driven solution that that makes your data meaningful. Semantic metadata management focuses on describing the things and relationships that assemble and “glue” together the business, technical and operational aspects of informational assets into a single “data landscape.”
At TopQuadrant, we are committed to Semantic Information Management and developed TopBraid Enterprise Data Governance (EDG) to provide Semantic Metadata Management in an extensible and flexible system that captures the meaning of all types of data. TopBraid EDG maps your organization’s diverse information assets to a rich, standardized data model, which can be easily extended or customized, as needed. With TopBraid EDG, organizations are now finally able to connect their silos of data and metadata, and unlock the true meaning behind their business data.
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For more information or to schedule a demo of TopBraid EDG tailored to the interests of your organization, contact us at: email@example.com