To Our Customers:
Six months ago I articulated our vision for the future based on the clear trends we’re seeing in R&D for life sciences:
A growing emphasis on data science in addition to bench science.
Software buying decisions must demonstrate how that solution moves the scientist towards a more predictive and generative AI future.
Customers are implementing company-wide initiatives to break down data silos and improve interoperability between software providers.
And increasingly, companies are adapting to a multimodal approach to drug discovery.
These trends are only accelerating today, and the biggest driver is that data in life sciences is exploding. Historically, astronomy has been the bellwether of data volumes, velocity, diversity and complexity, however by 2025, life sciences is projected to surpass astronomy. That is a massive shift that will only continue.
We know that our customers are all progressing toward an AI-enabled future. The transition to AI and machine learning was well underway long before ChatGPT burst onto the marketplace, however organizations were, and still are, constrained by data challenges. Unlike many other industries, our customers face a couple unique challenges that threaten to impede their ability to progress along what, for other industries, has become a predictable transformation path.
First, although there have been some attempts, life sciences lacks agreed upon industry standards that have realized mass adoption. Instrument companies and software vendors aren’t incentivized to improve interoperability, because interoperability leads to commoditization. Second, the ecosystem is highly fragmented. We’ve seen in other industries that typically once a single company has enough control of the data in the market, they're able to influence interoperability. Yet, in life sciences no single company controls enough of the data to force alignment. As a result, change and progress is particularly arduous.
Overcoming the hurdles towards digital transformation
Nonetheless, we are all collectively investing in the promise of an AI-enabled, multimodal future. And the reality is that whoever gets the best insights from the most comprehensive data fastest will thrive. Even just a few years ago if we asked our customers why they bought R&D software, the usual answer was about efficiency and productivity i.e. how do you spend less money trying to discover new therapies? Today that answer has shifted, some. Efficiency remains critical, but equally important is the ability to drive toward AI.
Whether your domain is pharmaceutical therapies, materials, or agriculture, we want to support our customers who are focused on reversing the downward trend of ROI in scientific research. This is a big part of what we’ll be addressing in June at our Dotmatics Mini-Summit: From Insights to Impact (more on that later).
We think about that digital transformation journey toward achieving predictive and generative insights on a spectrum in three primary phases: Foundational, Transformational, and Aspirational. Many companies sit in one of the first two phases. Most are “foundational,” they do the basics brilliantly such as simplifying the application landscape and workflows, and doing digital data capture. That means simply using software, moving off a pen and paper or spreadsheet and using software in its place. While the data might not be structured correctly, you have an understanding that software is necessary to derive efficiency. That's where change begins.
Some companies are in the “transformational” phase; you’ve recently implemented or are in the process of implementing a platform to harmonize all their data, and perhaps are even starting to achieve analytical insights as a result. But it’s here in this early transformational phase where most companies get stuck.
The challenge is, without a harmonized data platform, you cannot get insights at scale from that proprietary data. Much of the industry struggles with complex, massive, yet isolated data points from applications and instruments, and bringing all of these data points together between applications into a workflow will likely consume much of the next few years. And that’s a big problem. Because, beyond your people, we know our customers' number one asset is your proprietary data. These data come from years of experience and knowledge gained in the discovery process, and it’s what separates a company from their competition.
Ultimately everyone is moving towards the “aspirational” phase—first achieving lab automation, then starting to utilize AI to gain new insights in research, and ultimately, performing in silico methods, and simulations. It’s this phase that we so commonly hear our customers say that they need help from the right technology partner to get there, to address those data silos, interoperability, and workflow problems.
The Dotmatics advantage
That’s why last fall we introduced Dotmatics Luma. Luma is a revolutionary new scientific data platform that helps scientists and administrators unify and analyze large volumes of data for better decision-making. Luma is an out-of-the-box, low-code SaaS platform that flexibly aggregates all relevant data into intelligent data structures enabling clean, reliable data analysis, which paves the way for meta-analysis and AI & ML-based algorithms.
A huge part of why the time was right for Luma now is that we know our customers are increasingly moving from single modes of discovery toward a multimodal approach to research and discovery for new potential targets and therapies. You deserve software that empowers your scientists as they choose the best therapy type or combination of therapies to address a particular target. Unfortunately the software industry hasn't followed suit, almost entirely focusing on addressing a single mode of discovery. That’s because there is enormous complexity involved in building purposeful software to support various approaches and disciplines, and most tech companies don’t have that diversity of expertise required.
That's what makes Dotmatics so different. Dotmatics is comprised of an 800-person team with specializations across all modes of drug discovery: small molecules, biologics, mass spectrometry, biostatistics, and so much more. We believe the only way to understand our customers’ problems is to share the same knowledge and domain experience, to truly be able to empathize with your goals and pain points. Today Dotmatics has 10,000 customers and over 2 million scientists who use our software. We connect data from best-of-breed applications and instruments to enable collaboration, automation, and analysis that power a multimodal AI future. Nobody else can say this.
Most recently, we launched Luma Lab Connect, which squarely addresses the headache of connecting the myriad lab instruments and other data sources into one central location with an automated ingestion engine that works across labs and experiments. It’s just the beginning of leveraging our Luma platform to extend our scientific applications (Geneious, GraphPad Prism, Omiq, Protein Metrics, SnapGene, FCS Express, M-Star, SoftGenetics, nQuery, and LabArchives) to provide seamless and integrated workflows/dataflows that support the ever-growing multimodal discovery needs of customers.
I’m incredibly bullish about the future of our business with Luma, Luma Lab Connect, and all our tremendously valuable products and services. And if you haven’t already signed up, please take a moment to register for the Dotmatics Mini-Summit in June. This is a 90-minute digital event where our executive leaders will guide you through our product roadmap, and you can hear industry experts explore how Dotmatics is optimizing and accelerating discovery with an end-to-end R&D research solution.
We look forward to being your digital transformation partner!
Thomas Swalla
CEO, Dotmatics
