By Michelle Grainger
CIMS Executive Director Paul Mugge is among the co-authors of a chapter in a new book on using strategic alliances for more effective open innovation. The chapter focuses on one of our key strengths–Big Data analytics for business intelligence—and was written with two Poole College of Management colleagues, Steve Barr and Richard (Dick) Kouri.
The lead author is Mariann “Sam” Jelinek, Ph.D., who is Professor Emerita at the College of William and Mary and also a CIMS Academic Fellow. The team’s chapter—“The Big Data Lever for Strategic Alliances”—appears in Open Innovation for Strategic Alliances: Approaches for Product, Technology and Business Model Creation, edited by Refik Culpan.
I recently chatted with Sam to learn more about Big Data in general and how companies can employ Big Data to discover and manage opportunities to expand their capabilities through open innovation. We also touched on some of the ways CIMS is helping its member firms address other issues related to their innovation efforts. Here are some highlights of our conversation:
Big Data is misunderstood
Companies should focus on the application of the search results that Big Data makes possible rather than on the data technology, Sam says. “One of the reasons that Big Data analytics has often been such a hard sell is that executives don’t understand what it does,” she says. “They think it’s Google on steroids.” But instead of Google’s scattershot results, it produces a highly focused, curated database to answer a range of questions within a particular domain.
While data scientists are invaluable for running Big Data Web searches, subject-matter experts and business analysts really run the show. She refers to the process as Directed Big Data Analytics (DBDA) for a reason—subject-matter experts, both internal and external, need to know the right questions before they start looking for answers.
As Sam and her co-authors write: “The risks and uncertainties of open innovation strategic alliances have much to do with information: where to find partners or possibilities; how to contact them; how they might be assessed against one another, in light of strategic interests. Directed BDA bolsters decision quality by providing more and better data that is relevant to the specific decision domain of interest. The automated search process uses the SME-generated search term dictionaries and URLs, plus the links discovered in search, to execute an objective, inclusive, domain-relevant search with prima facie legitimacy. DBDA’s curated database is available for deeper analysis by SMEs, via a host of supporting analytics.”
Another misconception is that Big Data is about the numbers (quantitative data) when it’s really more about words and sentiment. As employed by CIMS, Big Data analytics relies on Natural Language Processing software to go beyond the usual structured databases to scour social media, newspapers, magazines, websites and other text-based web documents for unstructured data—the kind that can’t be understood by standard program logic– to glean important news, evolving trends and discoveries.
It takes a team
Companies are better served when the people directing these data searches bring different perspectives and expertise to the table. Sam notes that CIMS recommends forming Joint Project Teams (JPTs) consisting of firm executives with decision responsibility, data scientists to manage the software, and relevant outside experts to ensure appropriate depth and breadth of knowledge for the discussion. For example, in its work with the Clinton Health Access Initiative (CHAI), CIMS experts created a joint project team consisting of CHAI experts, outside public health and other experts and some CIMS researchers to help identify an initial research question: “What is the market potential for new TB diagnostics and therapeutics in specific CHAI countries?” Because the experts crossed organizational and disciplinary boundaries (e.g., marketing, public health, tuberculosis, information science, strategy), the approach “reduced the likelihood of overly-constrained thinking,” says Sam.
Minimizing human limitations
“It’s important for executives to recognize that for difficult questions, there is no easy button. But there is a ‘button’ that extends the reach of subject-matter experts,” Sam notes, referring to Big Data analytics.
It’s also true that some executives resist embracing open innovation because they’re afraid to give up control. But given the increasing complexity of technology and commerce, those who let emotions prevent them from engaging in technology sharing or joint partnerships might lose their competitive edge. “Giving away your deep secrets is a realistic concern, but that can be managed. Today, if you somehow thought you could do it all on your own, you can’t anymore.”
Big Data analytics can help assuage these fears. That’s because, when teams ask the right questions, the information they receive from directed searches will almost certainly be more relevant, valid and credible than the results researchers could uncover using conventional methods, Sam notes. Better information from BDA searches can point the way toward promising partnerships, licensing agreements, or new ventures. Big Data, in general, is also good at preventing the unrecognized biases inherent in human decision processes, the ones we can’t help but inject into our research and analyses because we are unaware of them.
CIMS is at the forefront
For now, because of the supercomputing power needed to carry out Big Data analytics, most companies can’t do Big Data Analytics on their own. That’s where CIMS comes in. “CIMS members have the opportunity to be on the cutting edge of a really important new strategic tool,” Sam says. “The chapter we wrote highlights that potential.”
Have a question for Dr. Jelinek or care to comment on Big Data for open innovation? Leave a comment or call 919-513-0166!