How Knowledge Graphs Solve a Diverse Set of Business Problems

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institutional-knowledge-CCC

The following is an excerpt from our upcoming white paper: Knowledge Graphs – Connecting Your Data to Solve Real World Problems in R&D Business Intelligence and Strategy.

The use of graph technology is not limited to scientific questions. Knowledge and information managers are curating and processing a diverse range of sources that are useful for multiple functions across an organization, from business intelligence and strategy to legal affairs and financial planning.

So how can these functions be better served by knowledge graphs? The question to ask as a knowledge and information manager is ‘where do the connections between those sources add value?’

Empowering business intelligence

A group working in business intelligence, for example, might be interested in which drugs are being developed to treat a particular disease. This could require linking across scientific and medical literature as well as clinical trials databases. Going further, a full business intelligence landscape might draw from many more sources. Drug pipeline and clinical trials data can tell a team much about what competitors are investing in. Finding links between those pipelines and mergers and acquisitions from SEC filings, industry news, and patent filings can give further clues. It’s not hard to see how a complex graph of disparate information sources with information coming in multiple shapes and sizes can be cross-referenced to gain extraordinary insight.

Reducing costs in clinical trial design

I recently spoke with a director at a company that provides clinical development analysis products. They use data from clinical trials databases, as well as some surprising sources like researcher CVs and financial reports, to improve clinical trial design and investment decisions. Some of the questions that their group were able to answer included how to optimize trial design based on number and locations of sites. They also help triage trials during difficult financial times to maximize return on investment and reduce risks.

As well as saving costs, finding previously missed connections can also improve the financial viability of a project. The person I spoke with talked about a recent project they had worked on in which they were able to expand the indication of the trial drug using knowledge graphs and thereby rescue the at-risk project.

Identifying the key opinion leaders faster

Another use case that was spoke of was identifying key leaders in particular fields of research. This might be of use for R&D when looking for academic collaborators. It might also be of use for HR departments engaged in talent acquisition. Knowledge graphs can add particular value in areas that are emerging or cross-disciplinary, where it’s not always clear who the new and emerging leaders are. Knowledge graphs can spot emergent connections in new areas even before communities recognize them.

COVID-19 is an emergent trans-disciplinary field

A very pertinent and recent example of the need to identify leaders quickly is in the fight against COVID-1911. There has clearly been an explosion of activity in the last few months. In essence, a new trans-disciplinary research field has emerged in a period of months with researchers across the world applying for grants, repurposing effort, and looking for solutions.

With this rate of work, it would be impossible for a researcher to digest all of this content, find the connections, the most likely best approaches, and identify the leaders. Mapping the topic areas and authors within this new discipline can give rapid insight to this new and vital field.

Interested in learning more? Check out:

Using a Knowledge Graph for Peer Review

Six Innovative Examples of Enhanced Data and Visualizations

Phill Jones

Author: Phill Jones

Phill Jones has a decade of experience bringing innovative products to market. Prior to founding DLD, Phill was the CTO at Emerald Publishing. He has had a series of roles at Digital Science (DS), including a senior role in the DS Consultancy. He also led thought leadership efforts in scholarly publishing, and developed patron driven acquisition and article syndication business models. Phill was the first Editorial Director at Journal of Visualized Experiments and is an influential thought leader in the scholarly communications technology sector. Areas of expertise include product and technology strategy, market-led digital innovation, and the changing landscape of academia. Phill is a former cross-disciplinary researcher. He received a PhD in physics from Imperial College, London and held a faculty position in neuroscience at Harvard Medical School.

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