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Pharmaceutical Executive was able to connect with Mike McKee, President of Dotmatics to chat about the necessary advancement of data science.
Dotmatics is a leader in research and development (R&D) scientific software connecting science, data, and decision-making. Its combination of an open enterprise R&D platform together with scientists’ favorite applications drive efficiency and accelerate innovation. More than 2 million scientists and 10,000 customers trust Dotmatics to help them create a healthier, cleaner, safer world.
Pharm Exec: What’s the difference between science data and data science?
McKee: Researchers in pharmaceutical labs today are being pushed to innovate rapidly, consistently deliver, improve ROI on the capital investments made, implement adaptable processes, collaborate more broadly, and convert data into insights. Yet, they are short on time and funding and they lack the ability to share, access, and collaborate using data in open ways. They often don’t have sufficient tools to visualize the results of their experimentation. In other words, the technology has lagged behind the needs of scientists.
Far too often, they’ve found that the IT part of processing data and discovering insights, “data science,” has not matured to the extent of its older more experienced sibling, “science data” which grows as rapidly as researchers experiment. Only recently have researchers in labs started to realize how much value there is in having data science software and processing power applied to the science data that is being extracted in labs. We think that data science has finally reached a level playing field with all of the data that scientists are uncovering in the make, test, decide innovation cycle. And now that it has, we believe there will be extraordinary improvements in the time and costs associated with handling research data.
How has data science progressed to be equally available to science data?
Scientific knowledge and growth in pharmaceutical drug discovery and chemicals and materials innovation is increasing at a faster pace than ever before. Advances in computational power are unlike anything in human history, creating avenues for AI, machine learning, and quantum computing. Today, more than ever before, data science and science data are on an equal playing field and the beneficiary will be humanity. Too commonly however, data is trapped in silos and researchers are trapped in silos.
In the United States alone, the Congressional Budget Office reported in 2021 that the annual spend on pharmaceutical research and development was $83 billion–more than 10 times the annual spending in the 1980s even when adjusted for inflation. Meanwhile, productivity in the research labs has decreased as workflows have become more complex and data sets more fragmented.
Complicating this is the reality of the time and money required for scientists to bring discoveries to market. For example, in pharmaceuticals, It takes on average 10-20 years to develop and bring a drug to market in the United States. Only 5% of compounds from the discovery stage launch in the market. And according to Accenture, the average cost to develop a new drug is between $2.6 and $6.7 billion.
What does this mean for research and development (R&D) in health and science industries?
At its core, science is about discovering hidden truths. Some of those truths are so microscopic or macroscopic that only technology can aid in their discovery. Nearly every major breakthrough in science that has advanced human well-being started with a question, and was uncovered through an often tedious and arduous experimentation process. Eureka moments are the exception in science, not the rule. And innovation is born out of experimentation. But the experimentation process in R&D has grown in complexity, complicated not by a lack of ingenuity, but by the failure of technology to support the brilliant ideas of our scientific community.
Why, when so many technological advances have been made, and so many scientific enterprises now spending such great sums of money on those technologies and softwares do these problems still exist?
The reality is that in practice, most data science “platforms” in the market for scientific R&D limit access to the data, to the ability to visualize it, to understand its context, even to trust it, and in general focus more on keeping the technology and the data within it proprietary to the technology platform itself. The result is missed insights, time wasted devising workarounds, and increased costs. For too long, the market has been without a digitally transformed lab, one that offers unified solutions for connecting the full lifecycle of science, data, and decision-making, and doing it in an open way, sharing insights across applications and data management platforms.
Again though, I believe today we are collectively reaching this exciting moment where we’re on the cusp of major change, with scientific R&D and innovation undergoing a pivotal transformation.
As more data science becomes available how will it be managed and used in the future of research and marketing of global health advances?
COVID-19 only underscored the need for rapid acceleration of scientific research and collaboration to lead to faster, more successful breakthroughs. The industry has been missing an integrated R&D platform built upon the capabilities of scientists’ favorite software applications supporting the entire innovation cycle of make, test, decide. Scientists need such a platform that offers depth and breadth in its capabilities to enable the powerful scientific workflows, and to weave all the data together from the research instruments to the software itself.
The goal here is to improve scientists’ efficiency to allow them to focus on what matters – conducting meaningful research. In this paradigm data is freed from silos and researchers are freed from silos. With Dotmatics’ solutions for example, scientists are saving up to 70% of time spent on searching for data, analyzing it, and integrating it as the data from instruments is automatically captured and linked to the cloud. That means a 70% increase in time for conducting additional experiments. Dotmatics customers also see a reduction of up to 50% in time spent on documentation by automatically recording documentation from experiments and processes. That means a 50% increase in time spent collaborating.
The long-term vision here is to turn scientists’ comprehensive data into actionable insights and enable decisions at the speed of discovery. And as importantly, it means that companies in the industry can move from spending billions to millions and bring their innovations to market in a more timely manner.
Mike McKee is the President of Dotmatics