Implement a Connected Inventory of enterprise data assets, based on a knowledge graph, to get business insights about the current status and trends, risk and opportunities, based on a holistic interrelated view of all enterprise assets.
Improve engagement, discoverability and personalized recommendations for Financial and Business Media, Market Intelligence and Investment Information Agencies,Science, Technology and Medicine Publishers, etc.
Knowledge Graph Implementation: Costs and Obstacles to Consider
This White Paper is part of the Information Literacy series for Executives.
What is the Premise?
The research for this paper began with the objective of defining the cost side of the equation – to make a reasonable business case to executive stakeholders – on the logic of adopting a knowledge graph. The focus is on companies where quality, traceability and data flexibility are essential ingredients – because not every company is an initial candidate.
What Does This Paper Include?
After interviewing a variety of experts and practitioners, findings are organized into three parts:
organizational issues, including positioning and dealing with bureaucratic roadblocks
the costs of operational discovery and technology to deliver the initial use cases
the importance of practitioner capability for the people needed to manage the data pipeline and engineer the content
About the author: Michael Atkin has been an analyst and advocate for data management since 1985. His experience spans from the foundations of the information industry to the adoption of semantic technology. He has served as an advisor to financial institutions, global regulators, publishers, consulting firms and technology companies.
By summarizing the key benefits, approach, barriers and needs for implementing a knowledge graph in your organization, you can make a strong case for action and set yourself up for success.
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