IDC estimated that approximately 270 gigabytes of healthcare and life sciences data was created for every person in the world in 2020. The high velocity at which data is being generated, along with its multidimensionality and lack of standardization, not only add to costs and cybersecurity risks but also frustrate end users who are spending more time threading it together instead of developing insights from it.
Knowledge graphs can provide an integrated view of structured and unstructured data, linking it with key business and scientific concepts. Customized therapies deliver better clinical outcomes and minimize adverse effects, but there is an urgent need to be able to map the data, which is all over the place. Life sciences organizations must connect the dots between a multitude of bio entities sitting out there to derive meaningful insights from them. Connecting these dots becomes critically important when researching disease biology and targeted therapeutics. Written by Dr. Nimita Limaye, research vice president, life sciences R&D strategy and technology at IDC, this paper explores:
- How knowledge graphs can provide a framework for data integration
- Key considerations while adopting knowledge graphs
- Five market challenges you could face when adopting knowledge graphs
“Knowledge graphs will weave a fine web, contextualizing millions of data points and threading together valuable pieces of information to derive powerful insights to scale discovery.”
Dr. Nimita Limaye, research VP, life sciences R&D strategy and technology
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