How Knowledge Graphs Help Overcome Data Management Challenges -


A majority of the data-driven initiatives fail because machines, unlike humans, do not have contextual intelligence. Therefore, to successfully automate, machines need to know what we know. Enterprise Knowledge Graphs (EKGs) have arisen to bridge this data management gap by bringing business meaning to machines.

Today, knowledge graphs play a critical role in solving a wide range of enterprise data management challenges. Knowledge graph’s flexible data model connects related terms while allowing data owners to maintain control of the source data. By allowing users to encode and centrally store all business logic efficiently, EKGs enable powerful recommendation engines like those of Amazon and Pinterest.

Besides by readily accepting new data, EKGs help enterprises quickly adapt to changing user and market expectations. A knowledge graph, additionally, offers the flexibility to update the knowledge base without requiring rework continually. For the Internet of Things (IoT) applications, a knowledge graph provides edge control and collaboration between proprietary systems and third-party systems/devices. The flexible data model offers quick connectivity of IoT devices for faster commissioning and changes.

Furthermore, a knowledge graph can unify SQL, NoSQL, and unstructured data so that enterprises can capture real-world context from all relevant sources. EKGs can help enterprises deliver new products by discovering hidden facts and relationships through inferences and identifying the nuanced meaning that different business units may have for the same entity.

Knowledge graphs can create a data foundation, resilient to change, that keeps pace with continued shifts in the market. In short, by capturing the business meaning in a machine-understandable format, knowledge graphs enable enterprises to adapt to whatever comes next.

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Brought to you by Scope e-Knowledge Center, an SPi Global Company, a trusted global partner for Digital Content Transformation Solutions, Knowledge Modeling (Taxonomies, Thesauri and Ontologies), Abstracting & Indexing (A&I), Metadata Enrichment and Entity Extraction.

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