Factors determining the success of enterprise knowledge graphs

Factors determining the success of enterprise knowledge graphs

June 20, 2018

Enterprises build large enterprise knowledge graphs for competitive advantage, data democratization, and data future proofing. However, to be successful, Partha Sarathi Bhattacharjee, senior solutions engineer at Massachusetts, USA based organization Cambridge Semantics says, enterprises have to factor in variables such as people, data and product.

To deploy an enterprise knowledge graph successfully, a bold and realistic vision is required. That is why building and implementing enterprise knowledge graphs need visionaries who understand knowledge graphs are game changers. In addition, they need to be business savvy to articulate the value of enterprise-wide adoption in tangible terms. Further, the value and scale of adoption of an enterprise knowledge graph are directly proportional to the diversity of data encompassed by it. This is an important consideration because unlike relational databases, scaling enterprise graphs to store and analyze big data is something very few enterprises have done successfully.

Furthermore, enterprise knowledge graphs have to be an integral part of the information system. It has to be well-governed, secure, easily connectable to upstream and downstream systems, analyzable at scale and cloud-friendly. Additionally, the product used for creating an enterprise knowledge graph should be optimized for automation, support a wide array of input systems, and offer a standard based output.

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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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