Data maturity is a measurement of the extent to which an organization is utilizing their data. In the life sciences, there is a wide spectrum of how companies engage and use data. On the one end, organizations are focused on figuring out how to collect data. Meanwhile, other companies are making important business decisions based on their data.

The trend toward a heavy reliance on data and the move toward the use of artificial intelligence (AI) and machine learning (ML) is currently being driven by synthetic biology companies that do not have a legacy infrastructure and can start from scratch with a data-first approach, Iyengar said. These companies use AI and ML as the foundation on which they build their science.

How do companies manage data?

Iyengar likened the digital transformation that is currently underway in life science laboratories to a similar evolution that happened in the software field. There, coders and developers are the creators and are supported by system administrators who build the necessary infrastructure.

Over the past decade, Iyengar noted, systems administrators have transformed into development operations engineers who have more critical roles in building systems that handle increasingly complex and higher volumes of data. Likewise, in the research space, scientists are the creators and lab managers support them. Iyengar said that lab managers will naturally evolve into lab operations engineers down the road.

What's needed for data-first approaches in the life sciences?

The main challenge for the industry is that there are no real industry standards for data communication, Iyengar said. This is partly because "the nature of science is so heterogenous that I think we are not going to have one universal standard, but what we will have is interoperability," Iyengar commented.

Because of the diverse requirements of researchers, there has been an emergence of many individual vendors (equipment and software) that serve specialized functions but that each have their own proprietary standards. There is important work to be done in getting stakeholders on the same page and getting them to agree on how these different systems will talk to each other.

Moving forward, the industry should focus on showing a concrete return on investment (ROI) by demonstrating how data-driven decisions can be effective for steering business success. Examples of this can be seen with adoption of data standards in facilities management, compliance, and purchasing, where showing ROI garners executive management support very quickly, Iyengar commented.

For digital transformation in research laboratories to happen, stakeholders need to associate ROI to the lab operations side of how science is done, he concluded.

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