In this special guest feature, Robert Lowe, CEO of Wellspring Worldwide, looks into how data strength needs to be the key focal area as the government begins to act on the CHIPS Act and future innovation efforts. Rob was previously the Director of Enterprise Creation at Carnegie Mellon University, a position created to provide management advice and assistance to the university’s many start-ups’ companies. He was also a professor of entrepreneurship and corporate strategy at Carnegie Mellon University and has authored over a dozen academic papers on technology commercialization and start-ups.
The CHIPS and Science Act of 2022, signals a new dawn for American innovation as nothing comes close to its $280 billion of new investment in new science and technology. Nonetheless, this generational investment may be squandered as America’s innovation engine is not ready.
The status quo has persisted for decades. Granting agencies prefer to fund “proven” research stars. Corporations partner only with top-tier universities. Short-term, tactical metrics drive decision-making and resource allocation at every level. Even within the startup community, the relentless push to hit valuation milestones means that game-changing breakthroughs are often out of reach.
In the ongoing innovation race, America has an opportunity to lead the way. But it won’t happen unless we use data-driven insights to make every phase of the innovation lifecycle increasingly more efficient, productive, and scalable.
Visibility is Power
Large grant-funding organizations may want to invest in new tech, but with so many options to consider, no one knows which tech will pan out. This “tyranny of choice” forces funding organizations to either a) pick one area and focus on it exclusively; or b) spray bets widely and hope for the best. Neither approach is optimized for success. The first is likely to end up as a dead-end, and thus horribly wasteful. The latter may not offer enough oomph in any one area to reach escape velocity.
What’s lacking is twofold. First, funding institutions lack the visibility to employ agile strategies that are adaptable based on iterative feedback from the external tech environment. Second, it’s hard to have accountability – and thus proceed with confidence – when you have imperfect information about what happens downstream. The result is a mixed bag of investments based on finger-to-the-wind guesses that remain uncertain and disconnected over time.
To be fair, most granting agencies do keep tabs on the ecosystem, as best they can. They check in with PIs, attend conferences, and conduct their own trends research. But without large-scale data analytics, they cannot carry out any of these activities as systematically as they should. The result is that America, as an innovation system, misses a wide range of opportunities to get the most out of our country’s talent. There are dozens of professors running innovative labs waiting their turn, innovation opportunities stuck off-the-radar, and complementary technology pairings that remain unexplored.
Collaboration is a Force-multiplier
Beyond the up-front question of where to invest, corporations also struggle with when and how much to invest. Most companies see the need for more innovation, but it’s typically only based on seeing the rough contours of future threats and opportunities. It’s hard to justify aggressive action in any particular direction, and so they operate tactically – chronically underinvesting until the path becomes clear.
For two decades, companies have embraced the practice of Open Innovation to try and get past these difficulties. The goal is to find the best innovators in the world, and then work with them regardless of whether they are employed at your company. In theory , this is the best way to accelerate innovation toward real-world impact – a parallel-track approach that can be a force multiplier. Conveniently, it also keeps large up-front research investments off the balance sheet. Too often, however, these practices end up watering down the overall program, which limits the corporate sector’s effectiveness to drive innovations into the market. It’s simply too difficult and time-consuming to keep tabs on all the possible R&D partners out there.
What’s needed is a scalable way of keeping tabs on up-and-coming innovation opportunities around the world, without a massive manual effort or unnecessary throttling. Unfortunately, most companies today don’t have the digital tools they need for this effort, as most vendors compete in niche specialties that don’t have a comprehensive view. Innovation teams tend to have one tool for researching patent activity, another to hunt for startups, and another tool to facilitate academic partnerships. Faced with the complexity of so many data silos, many teams throw up their hands, trim their ambitions, and resort to Google and word of mouth – so everyone loses.
The Moment Is Now
If America intends to win the 21st-century innovation race, government largesse is necessary but not sufficient. We cannot afford to run through the mud; instead, we must lace up with the best track shoes. This means empowering innovators with data-driven insights at every turn – early-stage funding, basic research, applied technology development, business model incubation, commercialization, and scale-up. We must use data to systematically find every nugget of opportunity, amplify the best of them, combine those opportunities into something greater, and in the process, maximize our collective potential.
With the emergence of modern AI/ML and big data techniques we can uncover deep insights about innovation trends and potential collaborators that would previously be out of reach. The newest commercially-available tools compile comprehensive data sets from across the innovation landscape – grants, publications, patents, startups, partnerships, public announcements, corporate filings, news articles, etc. These systems can analyze information of dizzying depth and breadth, significantly boosting America’s ability to drive next-level innovation productivity and innovation success.
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