As the number of data sources and the amount of data in enterprises continue to grow, automating metadata management, data management and data governance tasks has become a necessity. No longer is it just a way to stay ahead of the competition.
An effective tool to support data governance strategies at scale and with a high degree of automation is metadata enrichment, which significantly increases the precision of searches in your document database or website.
For instance, metadata enrichment can assign relevant tags and eliminate the guesswork to tell whether a document is responsive in the context of a legal matter, add business context such as which department it deals with, as well as determine whether specific regulations apply.
Data-intensive organizations struggle to understand and effectively operationalize metadata, but they also face more regulatory pressure in regards to data privacy.
As more enterprises realize the extent to which both consumer expectations and expanding data privacy regulations will impact how they govern their data, deriving insight from metadata can become key to data-driven business strategies.
Studies have consistently shown that data-driven organizations outperform their non-data-driven counterparts, with data-fueled programs improving everything from customer relations to operations to forecasting. Leaders at many organizations still struggle with making their data programs efficient and effective.
Strong metadata management is essential to both good data governance and an effective data program, but it isn’t always easy. The volume of work involved with metadata management requires automation — and ultimately intelligence — to yield the greatest benefits.
Augmented and automated metadata management can cut down on time spent on data tagging, cataloging and linking, resulting in intelligence to help uncover insights and connections that would be difficult to identify otherwise.
A company’s semantic model is an ever-evolving reflection of their business assets and relationships to the industry and the world. Creating a taxonomy and thesaurus is the foundation, but expanding that into a robust semantic model takes intelligence. Data Harmony’s semantic tools allow for concept identification and recommendations.
Finding concepts takes AI to delve through your document collection to identify and classify concepts and allows you to expand your semantic model to create meaning and relationships. The Data Harmony suite expands the semantic model to build and deliver the ontology you need for semantic search. A better semantic search will increase the likelihood of finding what you need, when you need it, and creating the transaction or outcome you desire.
Melody K. Smith
Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.
