As a representation of an organization's knowledge, an enterprise knowledge graph allows for the aggregation of a breadth of information across systems and departments and provides valuable business outcomes. However, a few key considerations and approaches need to be kept in mind to ensure that the investment in an enterprise knowledge graph remains a long-term success.
Successful implementation of enterprise knowledge graph involves having a clear definition of need, an appetite to start small, and a few iterations. The first consideration would be to understand and design the architecture properly. It is one of the fundamental aspects of making sure knowledge graph does not become stale or irrelevant.
The second consideration is to determine the frequency and volume of data. This would ensure that the knowledge graph is relevant, up to date, accurate, and has scalable coverage of all information sources without having to rebuild or restructure the entire graph.
Thirdly, to build a reliable enterprise knowledge graph, it is necessary to design and implement a repeatable data extraction and application model guided by the source systems' governance. It is also vital to put validation checks and analytics processes in place to identify conflicting information within the knowledge graph.
Lastly, a governance plan and an operating model is indispensable. For an enterprise knowledge graph to be effective, the governance model addresses the common set of standards and processes to handle changes and requests to the knowledge graph and peripheral systems at all levels.
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