For years life sciences companies have been working to develop the so-called “lab of the future.” But what is that exactly? A modernized lab would eliminate the issues that continue to hamper advanced research and slow new insights and business opportunities such as siloed data and integration of disparate datasets. Further, it would seamlessly automate necessary, but time consuming, tasks including microplate loading, liquid handling, imaging and screening for cell- and genomic-based applications all aimed at supercharging your high-throughput lab.

The good news is lab managers no longer need to wait. The lab of the future is now and can be created in a pragmatic, action-oriented modernization that creates a unified platform to integrate lab systems and data silos, allowing for a structured, standardized, maintainable research ecosystem with the highest levels of data security. Further, this lab is better equipped to help manage the increasing complexity of research by shifting from an on-premises model to a cloud-based Software as a Service (SaaS) model that allows for easy scalability, while also maximizing discoveries aided by the development of machine learning (ML) and artificial intelligence (AI) models.
 

Leading with Automation

One of the many benefits of the lab of the future is freeing scientists from the time-consuming, day-to-day manual tasks related to running their assays to create more quality time away from the bench for them to be creative, to think beyond their current work for what is next or how to better answer their scientific questions. Automation, in all its forms, whether it is automated liquid and plate handling to speed imaging and cell counting, collecting and unifying data from different datasets and instruments without the need for coding, and feeding these data downstream to leverage AI and ML tools for more streamlined analysis and intuitive data visualization, all speed new discoveries and insights.

But automation is not limited to helping run assays and collecting, unifying, and analyzing data. It also plays an important role during the “make” and “test” portions of the experimental cycle. In today’s scientific R&D landscape, it is common for lab teams to be scattered across the globe while still working on the same project. But recreating the same experimental protocols is a challenge. The lab of the future eliminates the manual task of recording all the details of experimental procedures and instead captures them to allow them to be easily reproduced in the lab across the hall or across the country. Eliminating the variability from lab to lab also ensures data integrity while aggregating it in the cloud for easy and secure access across the enterprise.

Automation even extends to the most basic tasks of managing a lab—ensuring consistent scheduled maintenance and calibration of tools to operate within specifications. A SaaS solution in the lab of the future is an agile and critical component, scaling with the needs of the lab and streamlining everyday processes such as inventory - tracking the use of reagents and other consumables required for each assay, so everything needed to run an experiment is always at hand.
 

Data Integration and Harmonization

Accurate, actionable data is the lifeblood of scientific research operations, but for too long the data generated for R&D projects has remained siloed and not easily accessible for other research initiatives and research partners. Unlocking these data is the key to unlocking the inherent value of the enterprise. The lab of the future leverages modern technology to build unified platforms that are able to automate the transformation of unstructured reports and disparate data sets into unified formats that can be used downstream for faster and more comprehensive analysis.

Yet it is not enough to simply unify data, the lab of future both improves data accessibility, and enhances this integration with streamlined workflows that reduce the time spent preparing and querying data. These productivity gains can be significant. According to a 2021 report by IDC, for companies looking to unlock the value of their data via AI, more than 50% of the time in these projects is spent on data preparation and deployment. Reducing this time via improved data accessibility promises to foster innovation, provide for faster decision making and ease the adoption of AI and ML to deliver insights from large datasets.
 

Data Security

Protecting data against a cyber-attack for an industry that is driven by data is paramount. According to IBM, the average cost of a security breach globally in 2023 was $4.45 million. Robust data security not only protects a company’s intellectual property from falling into the wrong hands, it also protects a company’s bottom line.

A benefit of adopting a SaaS model for the lab of the future is the service provider’s expertise in building complex, multilayered approaches to data protection. An important security feature for any SaaS is the use of a “Zero Trust” security model. Zero Trust requires that all users on both sides of every data transaction are authenticated. Vendors following the Zero Trust model also conduct vulnerability scans each week to look for—and patch—potential vulnerabilities. Patching can be synchronized with frequent system updates to minimize the vulnerability windows.

Good data security also recognizes that not all data is created equal. Data classification categorizes data based on its sensitivity. This allows companies to determine who in the organization can have access to which data and what protection protocols to apply when storing or moving data.

Should there be a breach, a SaaS provider typically provides frequent, automated backups of data to ensure comprehensive data recovery and data resilience.
 

Legacy Systems and Developing a New Mindset

The best lab of the future is created when there are well-defined business goals and understanding how the lab’s structure and capabilities will help support achieving those goals. Rather than modernizing all the tools and instruments during an automation and SaaS deployment, the company’s goals can help pinpoint which instruments need replacing and those older instruments that perform well enough to support business objectives.

Creating this new laboratory ecosystem also requires a move away from legacy systems. The use of old methods of storing data—the PDFs, the Excel spreadsheets, files, and folders—can hinder the overall functionality of the platform, especially in the age of advanced analytics and AI and ML models that require vast amounts of unified data to unlock new insights
 

An Automation and SaaS solution

At Revvity Signals we understand the tremendous opportunities that accompany creating the lab of the future. The Revvity Signals SaaS solutions helps businesses leverage modern technologies including AI and ML to derive additional value. Standardized systems allow for better collaboration via secure data sharing and data aggregation, while SaaS solutions are scalable allowing growth in step with your business without incurring significant additional investment.

To see how Revvity Signals can benefit your R&D efforts contact us or request a free trial.
 

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