For Success in Lab Automation, Bridge the Gap Between Science and IT
Contributed Commentary by David Dambman, biosero
April 7, 2023 | When implementing laboratory automation, one of the biggest challenges can be group dynamics: getting scientific and IT teams on the same page. Both groups are integral to the success of an automated setup, but often they have very different perspectives and might even be resistant to working together. It’s important to understand why so that we can overcome this issue.
Some challenges result from the bias of interpreting something through a narrow lens. For example, scientists typically picture robots—liquid handlers, instruments, or multipurpose work cells—when they think of automation. On the other hand, IT teams are more likely to think of software tools. If these teams have completely different things in mind, they’ll need help to bridge the gap between them.
We should also acknowledge that not all scientists emphatically embrace participation from the IT department. The IT umbrella covers everything from cybersecurity management—with the dreaded two-factor authentication—to WiFi troubleshooting and beyond. Yet, too often, we associate the IT group with slowing down our projects when they can be some of science’s greatest enablers.
To be clear, these are generalizations. The idea that IT doesn’t understand the lab and the lab doesn’t understand IT is certainly not true in all organizations. But unfortunately, it is true often enough to be a stereotype. For the success of automation in laboratories, we must overcome the lingering view that IT hinders science and help everyone involved see how valuable it can be when science and IT come together to accelerate research.
When IT teams and scientists join forces, we often see tremendous progress. And as more laboratory systems get automated—with the installation of hardware and software from different vendors—now is the time to encourage all organizations to address this issue head-on.
For the most successful laboratory automation installations, there are some common themes about how researchers and IT folks contribute and what they gain from each other.
Scientists tend to focus on hardware and science, which is good. Those responsible for running and managing experiments and mining resulting data to plan their subsequent experiments are in the best position to understand their scientific goals and instrumentation needs. Whether it’s a liquid handler or a next-generation sequencer, the scientists need to make the final decisions about instrumentation, devices, and overall process workflow that will be implemented in the lab.
The IT team should understand how these components work together and the software needed to connect the setup. Consider the drug discovery process as an example. It involves countless scientific tasks and workflows, and each step might tie into dozens of software systems that all need to work seamlessly together to capture, mine, and interpret data as it’s produced. More and more, the success of a pharmaceutical or biotech business hinges on how well these complex products can be integrated to enable more profound insights into the experimental data.
The IT team can function as the glue that holds everything together. For example, their decisions to help a biology lab may also benefit the chemistry lab down the hall. The biologists and chemists might not interact much, but the cross-functionality of the IT team can ensure that solutions for one group get translated to others in the organization.
The scientific and IT teams have much to offer in automation planning. Together, their perspectives meld into a more holistic view that enables thoughtful system design for the best possible outcomes.
Build or Buy?
The different views of scientific and IT teams often lead to disagreement over a key step in the automation process: build it yourself or buy from vendors.
Scientists focused on hardware—especially industry-developed tools that have been validated in the literature—tend to favor the “buy” option. But IT people focused on meeting diverse software challenges often prefer custom-built solutions. Unfortunately, these custom projects usually take longer than expected to develop and—even once deployed—can be a significant support burden.
Buying has its own challenges. For example, a singular focus on an instrument's scientific capabilities only sometimes considers important characteristics such as reliability, maintainability, and the ability to integrate successfully. Therefore, it’s essential to choose the most useful automation components for delivering great scientific results and the system's long-term success.
The optimal approach may be to choose an automation design consulting partner to help navigate the hardware or software choices. This partner would work closely with scientists and IT experts to plan a setup that meets everyone’s needs, fits the technical complexity requirements, and incorporates the best hardware and software options in a vendor-agnostic approach.
Realistically, the success of laboratory automation is precarious at any point. There is great motivation to automate, but not all labs are successful in these efforts. However, labs are far more likely to achieve their goals by encouraging close collaboration among scientists and IT teams and relying on automation partners to streamline the process.
David Dambman, an automation veteran with experience in a number of industries, is the chief technology officer at Biosero. He can be reached at firstname.lastname@example.org.