Pharma research labs are becoming increasingly complex, as the science relies more heavily on advanced instrumentation and data collection moves from paper-based to digital. This complexity comes with its own set of challenges, and it can be difficult for R&D leaders to keep up.
So labs need tools that allow them to leverage the latest technology and keep up with these increasing demands. This article explores how the right data management tools can help labs address four major challenges:
- Flattening the learning curve
- Coordinating processes across apps
- Avoiding data silos
- Balancing consistency and flexibility
The changing landscape of research data management
R&D labs are becoming more complex because the science behind drug discovery is becoming more complex. New types of instruments and assays reveal a more detailed picture of biological mechanisms and outcomes. But they also generate a lot more data. Today’s scientists are tasked with not only analyzing that data, but organizing it and integrating it with multi-modal datasets to turn multiple detailed but incomplete pieces into a single complete picture.
Transitioning to new tools from existing systems
There are many tools available to help scientists manage and analyze all this new data. But no matter how well they’re designed, it’s often difficult for users to learn to use them. Small changes in a user interface can make otherwise familiar processes look very different. And more substantive changes can be even harder to adapt to.
One of the most common failure modes when organizations adopt new software is that users just don’t use it. Even with a strong mandate from leadership, teams often take much longer than planned to migrate from the old system to the new one. Or they may fall back on workarounds and manual processes to avoid using a system that doesn’t make sense, no matter how much time they put into learning it.
For scientists to start using a new piece of software as quickly as possible, it needs an intuitive interface that closely matches the user’s existing processes and how they think about their work. It should be easy for both users and administrators to configure workflows that make sense to each member of the team.
In addition, some research lab management software has AI integrated into the platform to help users understand the new workflows and capabilities. For example, Sapio’s ELaiN allows scientists to use simple natural language phrases to “ask” their ELN or LIMS to conduct various tasks. An AI assistant like this can be a critical component of a successful rollout.
Coordinating processes across the data management platform
One of the most common obstacles to introducing new tools into existing systems is that most users don’t want to have to switch between different pieces of software. And they have good reason to feel that way.
When processes are split across multiple, unrelated interfaces, tasks can become very frustrating. Users may need to enter the same information in multiple places without an automated way to send information from one tool to the next. They will need to learn each new interface separately, and then switch contexts when they move from one to the other.
To minimize these issues, successful data management systems provide ways to integrate with other tools, minimizing the amount of duplicate work needed to sync between them. And systems that offer a broad range of functionality within the same platform minimize the need to rely on separate, specialized tools any more than absolutely necessary to support the lab’s work.
Creating a data hub and avoiding data silos
To support data science and achieve the kinds of results expected of modern research labs, organizations must be able to integrate data from across all their teams and projects. Users should be able to access data from a single source of truth through a centralized data hub. But instead, organizations often create silos around individual teams and projects at the point where data is collected.
Contrary to most industry wisdom, data silos aren’t caused by storing data in different systems. Transferring data between systems is the easy part. Instead, data silos come from having raw data in different formats and using different conventions that require tedious work from domain experts to clean and integrate.
Most data analysis and data management software leaves a fair amount of flexibility for how users organize and format their data. That includes things like naming conventions, folder structures, column names, and even the units for common measurements. This can work well enough when teams function completely independently. But this is exactly what creates silos and causes difficulties for teams that need to collaborate.
The best way to avoid silos, and ensure that all teams can access data across the organization, is to encourage teams to adopt conventions that are consistent across as many teams as possible. While organizational politics and change management may play a central role, the software these teams use can support this consistency by creating a path of least resistance to follow the conventions and supporting teams in learning the necessary processes and habits.
Balancing consistency and flexibility
The biggest factor in ensuring data quality in a complex lab environment is maintaining consistent data collection practices, whether it’s across teams or within one team over time. But this consistency comes with a price, particularly for early discovery labs: The fundamental nature of R&D experiments requires flexibility, so any tools or services that don’t support that flexibility are doomed to fail.
Experiment designs get updated to account for new insights or to work around logistical problems. Protocols are adapted to the idiosyncrasies of a new sample. Plates are reorganized to optimize for a different factor or account for the other changes.
Each of these changes leads to a fundamental, unavoidable change in how the data about the experiment, or the protocol, or the plate can be collected and organized. If the lab’s data platform can’t adapt to these changes, it doesn’t matter how intuitive the interface is or how well trained the team is. They’re not going to use it.
So the key is to find a balance: Configure a system that is flexible enough to handle the kind of work that the team is doing, while enforcing as much consistency as possible within those bounds. And the easiest way to do this is with software designed for the modern, AI-enabled R&D lab.
Conclusion
Modern R&D labs face multiple hurdles when it comes to managing data in an increasingly complex environment. But while it may seem overwhelming to try and deal with them, the best way to start is to adopt research lab management software that was designed for a modern R&D lab, with an intuitive user interface, a broad range of integrated functionality, and configurability that provides the right balance of flexibility and consistency.