Innovation in the Age of Big Data

Stephen DeAngelis

March 9, 2018

We live in an age characterized by the generation of enormous amounts data. It’s no surprise cognitive technologies (i.e., artificial intelligence (AI) systems) have matured simultaneously in order to mine insights from all that data. It’s also no surprise that companies that understand the power of connectivity, data generation, and advanced analytics have done exceedingly well. Mark van Rijmenam (@VanRijmenam), the founder dscvr.it and Datafloq, writes, “For years, these companies have understood that data is a goldmine. Since their beginning, these companies have rigorously been collecting and storing data. … As a result, these companies have become exceptionally influential.”[1] Along those same lines, Viktor Mayer-Schönberger (@Viktor_MS), a professor at Oxford, and Thomas Ramge (@thomasramge), an author and journalist, ask, “Are the most innovative companies just the ones with the most data?”[2] Van Rijmenam hopes that isn’t the case because he rues the fact large Digital Age companies often dampen the innovative efforts of others. He believes we need to broaden the creative base and spark an Imagination Age. He explains, “The Imagination Age was first coined by designer and writer Charlie Magee in 1993, and it is a theoretical period after the Information age when creativity and imagination become the primary drivers of economic value.”

 

Big Data, Cognitive Computing, and Innovation

 

Mayer-Schönberger and Ramge note, “The cases of startups with superior ideas dethroning well-established incumbents are legion. This is the beauty of ‘creative destruction’ — the term coined by innovation prophet Joseph Schumpeter almost a century ago. Incumbents have to keep innovating, lest they be overtaken by a new, more creative competitor. Arguably, at least in sectors shaped by technical change, entrepreneurial innovation has kept markets competitive far better than antitrust legislation ever could. For decades, creative destruction ensured competitive markets and a constant stream of new innovation.” Like van Rijmenam, they now wonder, “What if that is no longer the case?” The reason they ask the question is because, “The source of innovation is shifting — from human ingenuity to data-driven machine-learning.” They note humans do have a role in this data-driven innovative process; but, it’s a secondary role. “More often than not,” they write, “the data that fuels innovation is being generated by users interacting with an existing digital service.”

 

Mayer-Schönberger and Ramge agree with van Rijmenam that the concentration of data in the largest companies can be a barrier to widespread innovation. They explain, “If innovation is founded on data rather than human ideas, the firms that benefit are the ones that have access to the most data. Therefore, in many instances, innovation will no longer be a countervailing force to market concentration and scale. Instead, innovation will be a force that furthers them. … The specter of companies with access to data becoming data-driven innovation leaders, leaving smaller competitors and startups behind in the dust, should concern policymakers intent on ensuring that markets stay dynamic and competitive. Their challenge is less to realize the problem than to devise a solution that keeps markets competitive without stifling data-driven innovation on the whole.”

 

Some pundits insist cognitive technologies will increase innovation rather than stifle it. For example, Debbie Landers (@debbie_landers), Vice-president of Cognitive Solutions at IBM Canada, writes, “Leading organizations recognize that innovations once unimaginable are now within sight and are redesigning their enterprises in the hunt for ideas not yet conceived, paths not yet explored and opportunities not yet uncovered. Their executives are less inclined to see competition coming from outside their industry and to instead search for innovation externally among partners.”[3] Innovation guru Greg Satell (@Digitaltonto) agrees innovation is about ideas not yet conceived and paths not yet explored. “An idea can never be validated backwards,” he writes, “only forwards, so we have no real way of knowing what’s viable until we actually give it a shot. That’s why the ones that succeed learn how to test new ideas, see what happens and learn until they find something that really works. Innovation is never about what you know, but what you don’t.”[4] Cognitive technologies can augment human creativity in the search for innovative ideas. Mayer-Schönberger and Ramge conclude data-driven innovation isn’t going away and to survive companies of all sizes must adapt. They explain:

“Most business leaders … face a very different challenge in this world of data-driven innovation. To compete against digital champions, they will have to overcome not just scale and network effects but especially these new data-driven feedback effects. For many innovative companies, the next few years will be a time of reckoning: as the power of data-driven innovation increases, these more conventional innovators will have to find access to data to continue to innovate. That necessitates at least two huge adjustments. First, they need to reposition themselves in the data value chain to gain and secure data access. That’s difficult if, for instance, all the data is captured upstream in the data value chain. Just ask suppliers in car manufacturing, or book publishers. Second, as innovation moves from human insight to data-driven machine learning, firms need to reorganize their internal innovation culture, emphasizing machine learning opportunities and putting in place data exploitation processes. This is hard because it often runs counter to an engineering culture that has long championed human ingenuity.”

The very fact Mayer-Schönberger and Ramge bring culture into the discussion means they understand humans will still play an important creative role even in a future characterized by data-driven innovation. So how do you get humans and machines to collaborate? Victor Lipman (@VictorLipman1), a former business executive, recommends telling the human/machine team what needs to be done then getting out of its way.[5] He explains, “Management should provide sound strategic direction, … but don’t proscribe solutions for creative people. Let them sort through the options and the pros and cons. Don’t try to solve the problem for them. They’ll find a better way.” Once humans begin to appreciate what cognitive technologies bring to the table, they will find collaboration much easier than they ever imagined. Van Rijmenam notes Ray Kurzweil states in The Singularity is Near, “When we live in a world where anything can be imagined, imagination only becomes more important.”

 

Summary

 

Cognitive technologies can democratize innovation if companies gain access to the right data. Even though large digital-age companies have an advantage in this area, almost every company can obtain the data it needs to innovate. I agree with van Rijmenam that the world needs an Imagination Age and I believe cognitive technologies can augment human creativity to spark such an age.

 

Footnotes
[1] Mark van Rijmenam, “Why We Should Use Blockchain and Artificial Intelligence to Enable the Imagination Age,” Datafloq, 23 November 2017.
[2] Viktor Mayer-Schönberger and Thomas Ramge, “Are the Most Innovative Companies Just the Ones With the Most Data?Harvard Business Review, 7 February 2018.
[3] Debbie Landers, “Cognitive solutions for innovation: Are you a Reinventor, a Tactician, an Aspirational or an Observer?Financial Post, 1 February 2018.
[4] Greg Satell, “Innovation Isn’t About What You Know, But What You Don’t,” Inc., 10 February 2018.
[5] Victor Lipman, “Everyone Wants Innovation — Why Is It So Persistently Hard To Find?Forbes, 12 February 2018.