Driven by continuous research and development, the life sciences industry is in a constant state of evolution. But in recent years, several major shifts have converged to drive significant transformation throughout the life sciences landscape. With costs and competitive pressure rising, many organizations are pouring resources into accelerating research processes to reduce lead times and seize opportunities faster, while keeping costs to a minimum.

As AI and machine learning evolve, in silico research is creating opportunities to carry out more research digitally before moving to in vitro tests. But to do that effectively, organizations’ data must be easily accessible and of high quality to meet the standards demanded by life sciences research. This is creating a need for greater collaboration between teams in different research stages. In silico, in vitro, and clinical research teams must work together effectively to deliver the best outcomes and keep research streamlined and efficient. To make the challenge even tougher, teams must also maintain continuous compliance with a growing range of data regulations. With valuable data to safeguard, teams must tread a fine line between securing and managing it responsibly and making it available for use by diverse teams.

In this guide, we’ll be exploring why data meshes are so well aligned with the goals and challenges faced by life sciences organizations and walk you through what it takes to implement your own Data Mesh.