CSols Summit Recap: Exploring the Future of Lab Data Across Your Organization

CSols Summit Speakers

When technology changes, you must change with it. If not, your business could fall behind (just ask the founders of Netscape, who brought the Internet to all of us). Organizations like yours can be easily swayed by new technology buzzwords such as digital transformation and AI/ML for the laboratory, without really understanding what will be required to enact that technology. As a consultancy to a wide range of companies, it is our business to stay at the forefront of laboratory innovation, learn all that we can about what the future may be, and share those learnings with our audience. This year, we decided to try something new—we brought the experts to you!

In the first-ever CSols Summit, we developed a two-day program of eight sessions in various formats. The diverse sessions shared the topic of “Exploring the Future of Lab Data Across Your Organization.” A panel discussion moderated by Lisa Richard, CSols Director of Data and Analytics, serendipitously became a linchpin of the entire event, through a conversation that extracted themes that were revisited across the other sessions. Those themes are elaborated on in what follows.

CSols Summit Video Recordings

Data Literacy and Awareness

The first important theme to come out of the Summit was something that should be the foundation of any sort of digital transformation. Data literacy and awareness must be brought to a certain shared level. It’s important to be thoughtful about where you’re going with your lab data while still considering the legacy information. Thinking about how your data supports your processes should be the foundation of a digital transformation, according to Chris Dehen.

Standardization is seen as an important way to elevate an organization’s data literacy and awareness. As Kyle McDuffie pointed out in his Keynote address, driving lab data standardization can be difficult if your organization is spread across multiple sites and using multiple systems—which is often the case if there have been mergers and acquisitions in the past. 
A complication in data literacy and standardization is in the standardization process itself. There are two competing approaches to lab data standardization. One approach is based on ontologies; the other approach is based on machine language. At the heart of both is the metadata; organizations should develop literacy in, and awareness of, the core structure of their underpinning data.

Understanding Cloud Hosting

Cloud hosting is another one of the buzzwords that organizations tend to misuse. A distinction must be made between software as a service (SaaS) and the cloud. All SaaS is cloud-based, but cloud environments encompass much more. Think of the cloud as the background. SaaS alone is not the cloud. According to Dudley Snyder, when choosing the hosting environment that’s right for your organization, you must consider the number of instances, the internal resources available, and your tolerance for risk.

Cloud hosting involves storing your data on someone else’s hardware, so cybersecurity is an issue that you cannot ignore when thinking about remote access to your lab data. The decision about what, and how much, of your lab data to trust to the cloud comes down to your organization’s tolerance for risk, as mentioned by Kevin Cronin. It’s important to strike a balance between security and accessibility, according to Giovanni Nisato of the Pistoia Alliance. Bringing the Internet of Things (IoT) into labs to improve productivity will add another wrinkle to the issue of cybersecurity. Large-scale clinical trials of wearable medical devices are an area where these issues are being felt today.

▶ Related Content: On-Premises or Cloud Hosting: Which Model is Right for Your Lab

What AI and ML Can (and Cannot) Do for Your Lab Data

Understanding the Foundational Needs to Support AI/ML

The second edition of the Good Automated Manufacturing Process (GAMP) 5 Guide has an Appendix on artificial intelligence (AI) and machine learning (ML). Codifying its uses and applications which may encourage faster adoption by the (traditionally slow to innovate) pharmaceutical industry, as Bob McDowall mentioned in the Fireside Chat. Increasing numbers of organizations are curious about how to develop effective data science infrastructure.

If AI isn’t on the front end of your lab applications, it’s certainly there on the backend. Voice-activated digital lab assistants use AI-powered models to improve their speech recognition abilities. Any manipulations of big data sets, like the genetic screenings that Clyde Jones of Helix spoke about, need AI and ML to be successful.

Digital Transformation Across Your Organization

One thing that is clear about the future is that there will always be an app for that. Investing in the sort of backbone infrastructure that a LIMS represents means that you should look carefully at your lab’s processes before making changes. It is preferable to tweak the lab processes to fit the out-of-the-box capabilities of the LIMS as much as possible and keep customization to a minimum. For processes that can’t be made to fit the chosen LIMS, third-party applications are available, or in development.

A type of third-party application that is becoming more common in laboratories is digital assistants. These have a wide variety of forms, from a robust voice-to-text app for your phone to virtual reality or augmented reality wearable devices. “Digital lab assistants are the solution to simply align scientists’ needs with data goals,” according to Jeroen de Haas of LabTwin.

The pandemic has opened our eyes to the necessity of alternative ways of working; some of which are, by default, electronic. The availability of rapid results notification that the pandemic brought to everyone’s phones raised the awareness that this capability can be part of any lab’s processes.

Lab of the Future

It’s clear that the next 20 years will bring revolutionary changes (and challenges) to labs and to the broader scientific and technological organizations in which they sit. Organizations that position themselves to take advantage of those changes will have an edge over those that wait to see how the changes play out. CSols has resources to help your organization embrace change and meet challenges head on, whether it’s a digital transformation of your lab using LIMS or ELN, or evaluating your data and analytics to create cross-domain data streams

For those of you who might have missed the Summit, we recorded it and you can access the content at your leisure (minus the awkward “Is this thing on?” moments).


What steps are your labs or organizations taking to embrace the future of lab data? Share them below.

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