Pittcon’s technical program officially kicked off yesterday, with the exposition floor opening for manufacturers, scientists and lab friends just this morning. For 75 years, Pittcon has been drawing analytical chemists and other scientists from every conceivable industry into a web of collaboration, expertise, experience, education and more.
While we wait for news to come out of the largest laboratory equipment tradeshow in the U.S., Labcompare sat down with Alan Marcus, Chief Growth Officer at LabVantage to get an idea of what he expects to see this year in San Deigo. Labvantage is a technology leader with an advanced informatics platform that focuses on data integrity, cybersecurity and digital transformation.
Labcompare: Last year, there was an overwhelming focus on batteries, AI and PFAS technology at Pittcon. What do you think will be some of the most talked about topics this year?
Alan Marcus: We're going to see a lot of people talk about generative AI. From an investment standpoint, over the last couple of years, there’s been a big dry spell in IPOs. It looks like we're starting to come out of that a bit and we'll start to see new levels of investments—and they’re going to AI. They're not going to the old technology, even though there's still a lot of growth left. They're moving away from enterprise level software and equipment into more AI-related technology. So how does the lab fit into that?
We've got to be able to answer that. The Biden Administration has now mandated there has to be an AI expert or officer to make sure that citizens’ rights are respected and that inclusion is recognized. As we move forward, we're going to see how all of that builds up into the scientific space.
For the scientific space, when I think of the digital revolution that started, in my opinion, back in the 80s and through the 90s, the laboratories have been left behind. As we move forward, that that gap as the laggards is going to close. No longer are we going to look at the lab as a unique situation, it's going to become more of a strategic valuable asset in the enterprise—and AI integration is going to be key.
Labcompare: Can you briefly describe the new ecosystem you will be launching at Pittcon?
Marcus: What we've heard from our customers again and again is that people are looking for a more consolidated platform of software tools to help them in leveraging their scientific data much more significantly. It's something that we've been watching and trying to respond correctly to our customers. We brought in SDMS and ELNs and things like that into the LIMS environment.
A lot of customers talk say ‘we're capturing lots of data, but I don't always have access to it’ or ‘it's loaded in lots of different places,’ or ‘they're incomparable datasets.’ It's very confusing. Analytics don't seem to work, so it drove us to think much more on how we build in ingestion capabilities to make that data more useful down the path, but also make it more accessible what data exists.
So, the two areas we put a lot of investment in around is the notion analytics and using AI and some machine learning capabilities to improve upon that, and more semantic search. And that’s really about helping scientists think about what other questions they might need to be asking as part of discovery.
For example, if the same platform where a lot of your data has been being captured already, if it could query you on, ‘are you also asking for?’ and ‘did you did you also think about this?’ As we look at the various levels of research, that's a big part of what this starts to do. Within the platform, I'm able to build a single source of truth of data. I've got all the stuff coming in from my electronic lab notebook and it's captured and cataloged, I'm able to apply semantic search capabilities and query capabilities on that data.
Researchers have production data coming out of manufacturing, research data coming out of other institutions, instrument data coming in all kinds of forms. This single platform helps them formulate a very different level of discovery, which can reduce experimentation time and link everything back from R&D all the way through production.
Labcompare: What would you say makes this product different from others in the same kind of category?
Marcus: Recognizing that this where we're moving. We're moving away from workflow management to more data flow management, and there's a couple of key reasons for this.
On the production side, compared with 25-30 years ago, there’s a lot less people on the manufacturing floor and involved in that production. Increasingly, automation takes over and automation is not a workflow.
Workflows are about people and how people work, but as we start to look at how to improve quality and increase yields—it becomes about data.
The second goes back to the R&D side and the flow of the scientific data. We built these huge data lakes, but there's still so much data and we're only scratching the surface by measuring and managing workflows because it's all only humans and their interaction. But when data starts to interact with data and we start to build value and create discovery as far as starting to look at in silico—where you're doing experiments in the digital twin—then the its data flows you have to manage and not workflows.
Because of this, we’re really starting to shift to a data flow model. We'll start to see how that manifests itself as some of the larger enterprises move to do the same, but I think that's a significant difference.
Labcompare: Can you speak a little about your Professional Services Organization and how it's grown over the last 3 years? What does this mean for your customers?
Marcus: A couple years ago, there was still a large portion of our software wins with a backlog in our ability to deliver, right. As our technology improved, as our product improved, as our sales and marketing improved—there were more customers. More customers means more service, more delivery and the delivery organization as it was couldn't keep up.
What we started to see over the last couple years is that the complexity of the product—particularly when you start to get into the AI space and the semantic space—started to create some challenges for some of the implementors out there, and for Labvantage too for that matter.
But there's a difference when a vendor owns that capability and our ability to react quickly with development hand-in-hand—it's a better response to our customers. Thus, we agreed to build out that team, we hired a bunch of people all over the world, and we also acquired additional service capabilities through acquisitions. We're about 80% larger as a footprint now than we were. We're probably larger than any of those partners that we used to work with in the past. Now, our ability to react quickly and help our customers is better than ever.
Add that in with our ability to align better with development in any problems or in speeding up delivery and our ability to leverage some of the AI we're developing in master data management.