Advanced analytics can drive innovation to solve seemingly insurmountable issues — but these solutions cannot be derived from thin air, according to Lynn Wu, PhD, assistant professor at the Philadelphia-based Wharton School of the University of Pennsylvania.
In an interview with business analysis journal Knowledge@Wharton, Dr. Wu described how using data analytics for innovation differs from traditional innovation: Rather than relying on brainstorming sessions to develop entirely new ideas, analytics are most effective when tasked with reimagining existing solutions.
"We didn't have any conclusive evidence that it impedes, but we definitely find that analytics does not help with building or creating de novo innovation that is foundational and can act like a future building block for future combinations," Dr. Wu said. "If you think about it, if something's so new, it probably didn't exist in data yet. So, there's not much you can do with data analytics to help you find that pattern."
Instead, she said, analytics can drive "recombinations" of technologies that have already been developed. "Each individual technology already exists, but how do we recombine them in some ways to create a new innovation? Or reuse something that we know solved one problem, but apply it to a different domain?" she said. "Analytics is really great at finding these linkages or hidden patterns we may not easily observe by mining through a ton of data."
Read the full interview here.
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