Laboratories have many moving parts: experiments, inventory management, and data entry, for example. Time spent wrestling with processes and correcting errors detracts from core research goals.
Sticking with manual systems isn't just frustrating; it's inefficient and prone to error. With digital solutions, you automate workflows, save time, and reduce mistakes. Effective lab digitalization requires a thoughtful, collaborative plan aligned with your organization’s priorities.
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Defining lab digitalization
Broadly, lab digitalization is the use of digital technologies—such as LIMS and ELNs—to automate tasks, streamline data management, enhance collaboration, and improve decision-making. In many labs, these platforms exist in silos. Digitalization joins them, allowing interconnectivity, smoother communication, and opportunities for automation.
But digitalization is more complex than “just dropping files into Google Drive,” notes Namrata Patil, head of client experience at Ganymede. “[Many people] are not aware of how much thought must go into digitalization,” she says. Rather, lab digitalization is about implementing a robust technical infrastructure that consolidates every stream of data to heighten efficiency and quality—a goal that demands a lot of thought and planning to achieve.
There is no universal template for lab digitalization. It will look different in every lab depending on the lab’s unique workflows, instrumentation, regulatory requirements, and priorities.
Why lab digitalization?
Key benefits include:
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- Automating processes: Patil says that removing the “human in the loop” of repetitive processes is top of mind for many labs. Automating the flow of data between instruments, databases, and reports is precisely how those labs can achieve significant time savings while reducing errors.
- Enhancing collaboration: Digital platforms connect teams across locations, facilitating data sharing and real-time collaboration.
- Preparing for future tech: Digitalization lays the groundwork for emerging technology like AI. For instance, Patil notes that some labs have been able to feed the maintenance logs from their equipment into AI models that can summarize the logs, allowing lab managers to easily spot trends and make informed asset management decisions.
- Supporting compliance: Regulatory standards demand detailed, auditable data trails, easily provided by digital platforms.
- Improving agility: High-quality, abundant data and automated processes help labs pivot to new opportunities or respond to external pressures quickly.
- Holistic decision-making: “With an Excel sheet, you can only draw insight from that one [domain] of the lab,” Patil says. But by consolidating all available data, you can see the whole picture of the lab. “Imagine having insights across your whole lab—that’s a huge thing a lab manager could take advantage of when it comes to both operational things and [longer-term] goals.”
Planning strategies to consider
Successful digitalization requires convincing senior leadership for approval and securing staff support, as well as identifying long-term needs and selecting a suitable platform.
Finding a data czar
“In order to be successful [with digitalization], you need to have a data czar,” says Patil. This person develops a vision for leveraging lab-generated data through digitalization to impact the organization’s bigger picture.
As a lab manager, finding time for digital transformation can be challenging. Appointing someone experienced in informatics and invested in the lab’s growth will aid the process. This role can also offer career growth for a bench scientist interested in larger projects.
Imagine having insights across your whole lab—that’s a huge thing a lab manager could take advantage of. . .
Building a business case for leadership
Return on investment (ROI) is the lynchpin of your business case. Senior leaders are data-driven; your argument should emphasize financial health, growth opportunities, and the risks of not adopting technology. Patil notes that lab digitalization is a long-term investment, so it will be important to manage expectations, making it clear that digitalization will pay dividends in the future, but not necessarily now.
Additionally, ROI isn’t the only component of a successful business case. An effective business case can be divided into four sections as defined by Scott D. Hanton, PhD, Lab Manager’s editorial director and a longtime lab manager:
- Why: Explain the necessity and strategic alignment, such as automating tasks, improving quality, and readiness for AI.
- How: Outline the proposed solution, feasibility, required resources, costs, and timelines.
- Benefits: Quantify digitalization’s impact through metrics—time and cost savings, reduced errors—and highlight qualitative improvements.
- Risks: Identify potential risks like data privacy issues or operational disruptions, and propose mitigation strategies.
Gaining staff buy-in
Successfully leveraging digital workflows depends on staff buy-in. Changes, particularly automation, can threaten perceived job security.
To gain support, first gather detailed insights into the staff’s needs (daily pain points) and wants (additional technology or equipment features). Designing strategies based on these insights shows staff their concerns matter, fostering greater support during rollout.
Future-proofing and scalability
A key ingredient of a successful digital transformation is future-proofing—in other words, predicting your lab’s future growth and technology needs to make sure digital strategies taken today will still be useful in the coming years. It’s important that the digital solutions evolve alongside your lab and are chosen with long-term commitment in mind.
Future-proofing is largely a question of infrastructure. Let’s explore how this may look when implementing a LIMS:
One of the first decisions you’ll make when implementing a LIMS is opting for a cloud-based (SaaS) platform or an on-premises (colloquially, “on-prem”) system. Both options offer advantages and potential pitfalls with respect to scalability.
In order to be successful [with digitalization], you need to have a data czar.
Cloud-based LIMS platforms scale easily. As your lab’s data storage needs or user count grows, the vendor simply provides more resources from their data centers—no need for your team to purchase, install, and maintain new servers. Cloud LIMS are also ideal for labs anticipating rapid growth, collaboration across multiple sites, or fluctuating resource needs. The vendor’s handling of system administration (such as backups and security patches) can further reduce strain on your internal IT resources, helping ensure that infrastructure doesn’t become a bottleneck to growth.
However, cloud LIMS add a recurring cost to your operating budget and may limit how much you can customize the system to your lab’s specific workflows. Because cloud solutions are often built to serve a broad range of customers, they tend to offer fewer opportunities for deep, lab-specific customization compared to on-prem systems. This tradeoff must be considered when evaluating the long-term flexibility and suitability of cloud solutions.
On-prem LIMS, by contrast, involve a large upfront investment in server hardware, networking equipment, and IT personnel. Scaling an on-prem system typically requires more deliberate planning, as adding capacity will likely involve buying and installing additional infrastructure. This may limit the lab’s flexibility to scale quickly.
For some labs, on-prem platforms may be the best fit. Patil notes that some organizations want to run software on-prem for cybersecurity and data privacy reasons. For instance, many older analytical instruments are paired with computers running legacy operating systems like Windows XP. Those computers must stay isolated from the internet. While workarounds exist to relay that data to cloud platforms securely, some organizations may still prefer to err on the side of caution and keep all data isolated to their own intranet.
Key takeaways
Lab digitalization is essential for modern lab efficiency and innovation. Automating processes, preparing for future tech, enhancing collaboration, ensuring compliance, and improving agility are significant benefits.
Successful transformation hinges on strategic planning: appointing a data czar, securing leadership and staff buy-in, and selecting scalable platforms.
