Where can life science companies go operationally to maintain or create a competitive advantage? This question has been a constant in the industry since its inception, but recent advances in older technologies like Laboratory Information Management Systems (LIMS) and the appearance of promising newer technologies like the Internet of Things (IoT) have changed the nature of the answer. Which new technologies that are theoretically worthwhile have matured enough to be gracefully implemented operationally?
In the next few years, successful life sciences and biopharmaceutical companies will be those who can gracefully walk the line between new technology implementation and overextending on technologies that don’t add enough value for their clunkiness—as some early adopters are discovering with the first IoT lab solutions.1 Wasting effort by trying to force the use of technologies which aren’t mature enough or don’t offer a large enough departure from current practices is a real risk.
But which other new technologies are the most promising to implement, which should companies have already implemented, and which are a gamble? These questions are answerable, though the answers may be surprising. Of course, the specific needs of individual companies will vary, but the core features of research and development practices moving forward will be defined by their integration with machine learning, data-driven insights, and data management. What’s more, the R&D pipeline’s new software-initiated landscape will mesh with business development and allow for more transparent corporate governance at every level.
Combining Information Technology and Discovery
Data-driven discovery is the buzzword of the past few years, but smaller and mid-sized companies aren’t certain how they should approach implementation.2 Many research groups are throwing institutional operability concerns to the wind and implementing small-scale efforts to address needs on a case-by-case basis. These efforts have shown some success, but it’s clear that the life sciences industry as a whole would benefit from more unified informatics platforms, and a better conceptual background for data-driven discovery, if they intend to use it to generate insights and ultimately produce products.
Given that certain standard practices are still not fully deployed throughout the life sciences industry (like ELNs), individual companies would do well to make the best organizational platforms operational now before attempting to add on any bells and whistles to their process. Once mainstream organizational technologies are in play, approaching the current generation of data processing and machine assisted research will be much more feasible. Build a framework, then deploy the latest techniques to guide research. For life sciences research in particular, biological modeling, drug target prediction and genetic analysis are the present standard, meaning that the competitive advantage of the near future lies in using machine learning to coordinate these methods more gracefully than a team of people planning a drug pipeline could.
Some Combination Realignment Is Expected
Implementing the latest technologies does not mean deploying the most recently conceived proofs-of-concept within the discovery space, however. Stepping too far ahead of the current competitive practices by reaching into unproven space like blockchain applications for drug discovery is likely to lead to failure and disappointment at present.3 Rather than stumbling forward, operationalizing buzzwords that have yet to prove their value, companies in the life science sector would do well to implement solutions for problems that they currently have rather than buy in to solutions without specific problems in mind.
Embedding R&D and Business Development
The current standard of information technology is on the cusp of enabling more robust collaboration between R&D and business development via analytics platforms and automated business intelligence dashboards. These solutions increase competitiveness by processing the results of research and passing off the insights to those in business development, who can then make company-steering decisions with an appropriately detailed picture. This extra level of information extends outside of individual companies, too. Biopharmaceutical companies have never before been as able to learn about what their competitors are doing via open data sets and automated processing of clinical trial application and investigational new drug application metadata which is freely available online.
R&D and business development units will only continue to grow closer in the future. Software like machine learning and electronic lab notebooks can link into industrial production software as well as clinical research software. The picture of a corporation’s informatics landscape in the near future is that of a mesh of data systems which communicate with each other and help guide the physical work by humans, who are still responsible for providing its inputs.
Unifying the Entire Business and Research Pipeline for Predictability and Controllability
Everyone knows about how revolutionary big data and machine learning are, but the specific application of big data within the life sciences industry introduces knock-on consequences. Machine learning using big data sets will massively enable life sciences corporations to develop business intelligence suites to guide their efforts relative to their competition, which will require a shift in the way that business development personnel view their jobs in relation to the research wing.4 5
Therefore, to get the most mileage out of the recent technology advancements, keep the following in mind:
- Over-economizing on mature technologies like LIMS will leave you at a competitive disadvantage relative to those who go all-in.
- Being an early adopter of a new technology can be a double-edged sword. Have a target problem that will be specifically solved without introducing unnecessary work before diving in.
- The more pieces of your company that new solutions touch, the more chances there are of introducing friction instead of reducing friction as intended. Consider the holistic impact of new technology adoption.
- Not every technology or information paradigm is right for every company or every part of every company. Consider trial programs rather than policy impositions.
Gartner recently updated their ongoing evaluation of the priority matrix for emerging and maturing technologies in the life science R&D industry.6 They identified a number of technologies which we believe are poised to make large impacts as industrial adoption grows, including mobile lab apps, enterprise lab informatics systems, electronic document management systems and cloud-based drug discovery platforms. Dassault Systèmes is identified in many of these areas as a key vendor, with a wide breadth of solutions which we believe can help life science companies implement new digital strategies. To read the research report, please visit this page.
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
- “Internet Of Things Laboratory Test Bed.” October 2015, https://link.springer.com/chapter/10.1007/978-81-322-2580-5_44 ↩
- “Data Driven Discovery In Science: The Fourth Paradigm.” February 2012, https://www.nitrd.gov/nitrdsymposium/speakers/documents/szalay.pdf ↩
- “Blockchain Technology: Applications In Health Care.” September 2017, http://circoutcomes.ahajournals.org/content/10/9/e003800 ↩
- “Comparing Business Intelligence And Big Data Skills.” October 2014, https://link.springer.com/article/10.1007/s12599-014-0344-2 ↩
- “Machine Learning And Visual Analytics For Consulting Business Decision Support.” September 2015, http://ieeexplore.ieee.org/abstract/document/7314299/?reload=true ↩
- Hype Cycle for Life Science Research and Development, 2017, Michael Shanler, Jeff Smith, 17 July 2017. https://www.gartner.com/doc/3761963/hype-cycle-life-science-research ↩