As labs collect more data from more instruments than ever before, manual data handling quickly becomes a bottleneck. Automating data processing not only improves efficiency; it lays the foundation for advanced tools like machine learning and AI. But getting started takes more than plugging in a new tool. Here are five practical tips to help lab managers implement data automation effectively and sustainably.
1. Have well-defined processes
Data streams can only be automated if the existing manual processes are consistent and can be “described systematically,” says Nathan Clark, founder and CEO of Ganymede. Ultimately, computer programs are abstract representations of business processes. But unlike humans, who can make dynamic judgment calls and remain productive even in the absence of formalized processes, computer programs are static. Hence, processes must be standardized and documented if they are to be represented in code. With well-defined processes and consistent entries into your LIMS and ELN, “you’re already 80 percent of the way there to what a general, open-ended AI implementation project would look like,” Clark says. Process standardization is foundational to an effective data strategy.
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2. Identify and engage key stakeholders
Implementing automated data processing is an endeavor that will expand beyond your lab. As mentioned in previous Lab Manager coverage, you may need to justify the decision to leadership. They will want to understand why it’s worth the time and cost. You may also need to involve IT for successful implementation and resource provisions. Without buy-in from involved stakeholders, the project likely won’t succeed.
3. Find data friction and automate from there
Bottlenecks are often a good place to look for opportunities for automation. Automating routine and time-consuming tasks frees up your staff to focus on more meaningful work while reducing errors—tangible dividends that both lab staff and leadership will appreciate. These benefits can go a long way in securing approval to build on your data automation work toward AI readiness and other opportunities that arise with digital transformation.
4. Prioritize data contextualization
Clark advises against building a “data swamp,” which he describes as an unstructured pool of experiment results, instrument data, and other records pulled from various silos across your lab. “People put data in [swamps] without sufficient context,” he remarks. Beyond raw data, you also need metadata—that is, the meaning and relationships between all the data. Meaningful metadata may include:
- Assay name or ID
- Instrument configuration parameters
- Sample and process ID
5. Work bottom-up
As mentioned, automation can be a steppingstone for future endeavors—AI readiness being a prime example, according to Clark. Having contextualized, structured data from every source in your lab, all stored in a robust infrastructure, will be the lifeblood of future AI solutions. And even if you don’t use AI down the road, it’s still a worthwhile endeavor to improve quality while saving time.
Success depends on starting with well-defined processes, engaging stakeholders across departments, and focusing on both structure and context in your data strategy.
This article was adapted from a presentation by Nathan Clark, founder and CEO of Ganymede, delivered at the 2024 Advances in Lab Software & AI Digital Summit, hosted by Lab Manager. His insights offer a pragmatic path forward for any lab looking to turn fragmented data into a reliable foundation for automation and beyond.
