‘Follow the Money’ To Locate Potential Business Partners
Editor’s note: The following article is adapted from the March-April 2016 issue of Innovation Management Report, the newsletter of CIMS. Its authors are Paul Mugge, CIMS executive director, and Richard Kouri, CIMS’ “chief evangelist” and former executive director of the BioSciences Management Initiative at N.C. State.
For the past several years, CIMS has been using Big Data analytics to address the strategic challenges confronting companies. By analyzing publicly available data residing on the Web, we have been able to help businesses answer such strategic questions as:
What major trends are impacting our industry? What market opportunities do these trends present to our company? With whom might we partner to deliver solutions to these customers?
These are big questions, however. By heeding the saying, “money talks,” we’ve been able to narrow the focus of these questions so that answers are more relevant. Following the money—whether it’s sales generated in an industry, investments made by venture capitalists or grants awarded by government agencies—yields the kind of data that can help companies make better decisions.
Following the flow of dollars is particularly important in the pharmaceutical and life sciences industries, where investment can come in the form of private funds and government research grants. A leading pharmaceutical company hoping to develop cancer treatments based on personalized medicine knew they were facing an uphill climb. Success, they realized, would come from good answers to the questions, “Who are the key opinion leaders in personalized medicine? Where are they located?”
Big Data analytics makes it easy to follow the money
The company recognized that personalized medicine, a new approach to health care based on each person’s unique genetic makeup, represents a major breakthrough in the treatment of these difficult cancers. However, they didn’t believe they have the skills in-house to understand and respond to this advance, nor do they have the time to train their own R&D employees. Consequently, the company wanted to form partnerships quickly with the leading thought leaders in oncology personalized medicine (especially in the areas of breast, lung and prostate cancer). So they sought us out for help.
As Henry Chesbrough, the widely accepted father of Open Innovation and a professor at the Haas School of Business, emphasizes, “In today’s information-rich environment, companies can no longer afford to rely entirely on their own ideas to advance their business, nor can they restrict their innovations to a single path to market.”
This could not be truer than for Big Pharma companies caught up in a desperate search for people, and ideas, around which they can build new sustainable business models. The field of personalized medicine holds such promise, but how to identify and locate the “best of the best” of these partners, anywhere in the world, is the challenge.
To tackle the problem, we used IBM Watson Explorer, a content analytics program that can “read” and decipher massive amounts of unstructured data such as web sites, government reports, press releases and the like. We know that the National Cancer Institute (NCI) is a leader in cancer research and makes substantial annual grants to deserving faculty across the world, including those at the top U.S. medical schools. However, before searching NCI.gov for this information, Watson has to be trained to recognize the specific words and phrases that describe terms, like “medical schools,” “issued grants,” “personalized medicine,” and “oncology.”
These words and phrases are captured in dictionaries that often contain hundreds of entries. For example, to build the oncology dictionary we downloaded its definition from the NCI Dictionary of Terms and all of the descriptors (synonyms) for lung, breast and prostate cancers from the NCI Thesaurus.
In order to follow the money, we created a special annotator, called “$money_finder,” which we set to identify those medical centers receiving NCI grants greater than $1.5 million. We were looking for large grants that NCI would likely only issue to prominent researchers at the top medical schools. The answer to this first sub-question was that the NCI had issued SPORE grants (Special Programs of Research Excellence) to 52 medical centers in 21 states.
From there we moved to the second sub-question, “Which of these centers’ grants were focused on breast, lung or prostate cancer?” By using the oncology dictionary to search the S.P.O.R.E grants, we identified 10 medical centers researching the use of personalized medicine methods to treat patients with these specific cancers. The third sub-question asked for the key investigators on these 10 grants. Again, we built another special annotator, called “name finder” to extract the investigators’ names. Watson found 143 investigators working on these grants.
These 143 investigators—all U.S. residents—are some of the most knowledgeable people in the world in the biology of breast, lung and prostate cancers. It stands to reason that these people would likely possess the skills that a company, like the one in our case, needs to help jumpstart its new line of business in personalized medicine in oncology.
The company’s questions to this point could have been solved eventually—albeit with a tremendous amount of hunting and pecking—by using Google and the search engine intrinsic to NCI’s website. But here’s what could not be done using these tools:
Using Watson, CIMS crawled the Web and created a custom dataset containing the entire websites of the top 398 venture capital (VC) firms in North America that focus on developments in the life sciences. By mining this huge corpus of information—estimated to exceed 170 million web pages—we were able to answer the fourth sub-question, “Which of these key investigators are linked with startup activity.
From this analysis, we get a good sense of how the VC community values these people and their technologies. Only 13 of these people are engaged in startup activities and/or sit on the science advisory boards of the VC firms. For our pharma company, which is looking to partner with the “best of the best” of these people, this last test, or screen, yields this critical information. The last sub-question can be equally valuable to the company. By searching the investment portfolios of the 398 VC firms with the dictionaries—personalized medicine + oncology—we are able to identify the six firms making investments in new ventures in this field. Possibly the company in the case would like to co-invest along with the VCs in these new ventures and their emerging treatments for breast, lung and prostate cancers. This represents another way of creating a new line of business while using VCs to help lessen the risk.
Publicly available data such as the ones we used contains a tremendous amount of raw intelligence. The trick is to extract this information using logical arguments the rest of the organization understands. In this case, we used the editors of US News and World Report, the U.S. National Cancer Institute, and almost 400 VC portfolio managers to do the due diligence of locating special people with great expertise in a specific critical area.
For business people faced with making complex strategic questions, we believe “following the money” represents such an approach. Readers can learn more about this technique and how it was developed in our September/October 2014 IMR article: “Why Big Data Is Not All Hype: The Power of Unstructured Text Analytics.”—Paul Mugge and Richard Kouri
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