Late last year, I had the honor of being invited to share my perspective on the future of innovation as a speaker at a number of impactful events including the Forbes Healthcare Summit and Milken Future of Health. The sessions focused on recent healthcare breakthroughs and how we can gain additional leverage to further accelerate
Late last year, I had the honor of being invited to share my perspective on the future of innovation as a speaker at a number of impactful events including the Forbes Healthcare Summit and Milken Future of Health. The sessions focused on recent healthcare breakthroughs and how we can gain additional leverage to further accelerate the pace of discovery.
As a specialist in scientific information solutions, CAS has a unique perspective at the crossroads of published scientific data and advanced technology to observe and measure global innovation. Our team of hundreds of scientists meticulously curate the world’s largest collection of published scientific information. By applying advanced analytics to track trends in this collection, we can see shifts in both the pace and the direction of innovation from their genesis.
By any measure, I would argue that we are living in the most innovative time in human history. More than 14 million new chemical substances were disclosed and added to the CAS REGISTRYSM last year alone. The total volume of scientific patent and journal publications disclosing new discoveries is increasing by ~10% annually, but really that is just the beginning.
I firmly believe that we are on the verge of a sea change in the pace of discovery across scientific fields that will be fueled by effectively applying the vast potential of technologies such as AI and machine learning to probe the unexplored white space faster and more efficiently. Scientific discovery, which has historically been an effort of educated trial and error, has the opportunity to become a far more systematized, reliable, data-driven process. To truly capture that potential, however, new strategic approaches and investments will be required across R&D organizations and the surrounding ecosystem. A few opportunities stand out that I believe will be critical to maximizing the pace of scientific innovation over the next decade.
Embrace the strategic importance of data
The next decade of innovation will be fueled by data. In this digital age, organizations must view their unique data collection as a competitive asset. High-quality data, and the tools to effectively access it, are critical assets for all innovation-driven organizations, yet the level of investment in data and information solutions has historically been less than 1% of overall R&D budgets. That is simply not enough going forward, given the growth of data and the available opportunity.
When we examine some of the high-profile failures and setbacks of R&D applications of AI technology, the common thread is data challenges. Thus, one key focus of investment must be building, curating and connecting internal data sets so that they can be fully leveraged, in tandem with public and licensed data, to drive unique insights and efficiency. Investments in underlying data will likely have better payback than custom technology, because the value of the data will endure even as technology evolves and becomes obsolete.
The other key investment is tools to effectively mine externally published data and keep up to date on the latest advances. Given the global nature of innovation and the complexity of scientific disclosures, specialized resources are necessary to efficiently and comprehensively search and maintain awareness of the landscape. A recent survey showed that scientists spend nearly 20% of their time searching for needed information. Thus, the payback on investing in the right information solutions to make that process more efficient becomes positive very quickly.
Recognize the value of human capital
The hype surrounding artificial intelligence, machine learning and other emerging technologies can make it easy to forget the importance of human intellect at the core of the discovery process. Though technology and data are important, and algorithms can do great things, human curiosity is what truly drives and guides innovation.
Human expertise is still a key component for success, especially in the sciences where data goes beyond just text and numbers. Humans are uniquely capable of analyzing and connecting data, putting it in context and bridging the gaps where judgement is needed. One of the most dangerous mistakes we can make is allowing AI-driven conclusions to steer our path, without careful vetting and understanding of the underlying data by our teams.
Communicating this point is urgently important. It is critical to reinforce the idea that technology is intended to augment, not replace, human intellect. Fear of being replaced by technology can create roadblocks to digital transformation. If teams don’t share their data, adapt to new processes and buy in to the transformation, progress will be derailed. Employees must be aligned with the vision and the why to ensure they are confident in their roles, understand their unique value and feel comfortable questioning technology-driven conclusions.
Focus on core competencies
Despite well-considered strategies and significant investments, the reality is that many organizations are struggling to demonstrate measurable value from initial digitalization investments. This is not surprising. Activities such as information management and related technology development have not traditionally been core competencies of scientific research organizations. Quickly ramping up these capabilities is difficult and resource-intensive.
As data and technology are becoming fundamental components of innovation strategy, it is easy to conclude that deep internal expertise and heavy capital investment in those areas is required. However, this may not be the best course for many research-focused organizations. The nature and extent of any such investments should be carefully considered to maintain agility and maximize return.
Strategic partnerships are one alternative meriting consideration. Partnerships are an effective way for research organizations to reap the benefits of these strategies more quickly, while maintaining flexibility to pivot as technology and the landscape evolve. Identifying partners with deep expertise, data or technology assets to complement researchers’ core capabilities is proving highly effective for organizations seeking to quickly catalyze digital transformation in their innovation processes. I believe that, just as contract research organizations have changed the pharmaceutical landscape, digital transformation will give birth to new types of partnerships that fully embed data and technology organizations into the fabric of the ecosystem.
Adapting for the future
As we enter this new decade, I have great hope for the future. I am confident that the synergy of human intellect and technology will deliver unimagined solutions to address our world’s most challenging problems and improve our everyday lives. How quickly we can achieve that reality will be governed not by data or by technology alone, however, but by our collective ability to adapt, evolve our approaches and embrace the opportunity before us.
At CAS, we are actively investing to make this future a reality. We are growing our content collection and building cutting-edge search and analytics solutions such as SciFindern to efficiently query the data. We have also added a custom services team to empower organizations to leverage our assets and expertise more broadly for applications such as machine learning, knowledge management, and workflow integration. I am confident that together we are prepared to capture the opportunity.
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