Navigating the Biotech Landscape: A Deep Dive into Knowledge Graphs for Market Insight

Harnessing Knowledge Graphs to Revolutionize Market Intelligence: Join us as we explore the transformative role that knowledge graphs can play in market intelligence in the life sciences industry to revolutionize your research, drive innovation, and establish a competitive edge.

Staying ahead in the biotech and pharma industries requires a deep understanding of the market landscape. Whether researching small biotechs who are working in a particular indication, or staying abreast of the latest notes from a competitor’s FDA approval meeting, market intelligence is an ever-important task. The vast amount of available data can be overwhelming, but harnessing its potential can unlock tremendous opportunities.

Knowledge graphs have been around for many years, but are currently having a renaissance due to their potential in pipelines built around Large Language Models (LLMs). Although there are different implementations of knowledge graphs (Neo4j is a popular one), a key defining feature is that they represent information about how “things” connect to one another, providing a way to integrate diverse data sources into a single unified “web” of knowledge. We have written another post about several use cases and best practices for knowledge graphs in life science industries. Here, we will explore some of the ways they can revolutionize the approach to market intelligence specifically.

Competitive Analysis and Identifying Unmet Needs

To make the most impactful decisions in life science businesses, it is crucial to be aware of the competitive landscape so that resources can be focused on the most underserved and critical problems. By analyzing competitors' strengths and weaknesses, businesses can differentiate their pipelines, identify partnering, licensing, and acquisition opportunities, and position their products strategically for success.

The power of knowledge graphs can be leveraged here. Bringing together complex datasets into a unified framework can help uncover intricate patterns and relationships. These insights can reveal potential connections between targets, diseases, drugs, and companies, to identify novel therapeutic targets or opportunities to repurpose existing treatments. An example use case for this could be evaluation of the relative potential of a new drug candidate in a given indication. To survey the drug’s market landscape, you might query a knowledge graph to see what other products are already available to treat the same indication. If most other products for that indication have the same mechanism of action or the same target as the new candidate, it might be less interesting than a candidate drug with a completely novel approach.

Another example use case for this is in identifying unmet needs in the market. Viewing the web of connections between companies, the drugs they are marketing, and the indications those drugs are approved to treat provides an intuitive picture of where there are gaps. Layering in epidemiological information, funding data, or knowledge of the clinical trial landscape in other markets globally can give even more nuance to this. By evaluating which gaps in treatment have the most supporting knowledge and the greatest affected population, the focus of research can be shifted using data-driven decision-making.

Enabling Collaboration and Knowledge Sharing

A knowledge graph can also be used to store and track an organization’s accumulated knowledge, for example: PowerPoint slide decks, important research publications, KOL (key opinion leader) interview notes, etc. Representing this information in a knowledge graph facilitates collaboration and knowledge sharing in several key ways. It helps to combat the siloing of information with specific individuals or departments. Recording work in a knowledge graph means that it is easily accessible to anyone within the organization who can access the graph. This also encourages a level of standardization across the organization, which makes it easier to understand and utilize work produced by other teams.

Knowledge graphs help different members of an organization know what information is available. It is common for specific departments to work on projects or use resources that others within the organization are not aware of. Ingesting data from frequently used external sources and tracking internal projects gives everyone in an organization better oversight into the available data, whether it was internally generated or is from a trusted resource. By tracking information within an organization this way, knowledge graphs also enable provenance tracking. This can provide additional confidence when making key business decisions.


Over the years, knowledge graphs have come into and out of vogue; the hype around generative AI is beginning to bring more discussion to them again. Historically, knowledge graphs have been conceptually intuitive but difficult to work with in practice, requiring specialist knowledge of query languages or graph theoretical algorithms. Now, however, we are seeing LLMs beginning to democratize use of these frameworks, providing opportunities to apply more sophisticated graph data science techniques for things like relationship prediction, identifying related communities within a network, and more.

Though there are a number of ways knowledge graphs can be employed in the life sciences, we hope that we have given you some insight into how to leverage them for market intelligence. The specifics of how each individual organization uses market intelligence can be quite varied, in terms of different diligence needs, relevant data sources, or market focuses. There is no one-size-fits-all solution. However, if you are seeking to leverage market intelligence and knowledge graphs to accelerate your projects, we invite you to reach out to our data science consultancy team. Our experts specialize in delivering tailored solutions that empower life sciences organizations to make data-driven decisions, deploy scalable solutions, and advance their mission.

Written by:
Daniel Konecki
Senior Data Scientist
Sam Regenbogen
VP of Generative AI
Published On:
May 1, 2024