Is innovation an artisanal feat? Can innovation be weighed, measured on a scale, and optimized like a production operation? Can the use of data analytics disrupt creativity as it has done in other business functions? As an innovation consultant specialized in data science, I see the power of data and creativity going hand-in-hand every day. To understand how this happens, we will have to start with the evolution of innovation analytics.
Early life of innovation analytics
Innovation has been a hot topic in business for years. Out there in the market, one can easily find consultancies that offer “innovation toolkits” (usually a variation of Design Thinking tools) to help businesses turnaround their value propositions, business units, or the whole company. Sometimes the tools work, sometimes not. This innovation paradigm is highly artisanal and uncertain.
As the use of Big Data becomes mainstream, we are starting to see data analytics being applied to business innovation to enhance the efficacy. The case of the successful House of Cards is a good example. A series designed with the help of data analytics to make it a blockbuster. Another example is Nielsen Innovation Analytics which claims to boost the success rate of innovations from 10% to 75%.
From the "what" to the "how"
While Nielsen's tool utilizes data analytics to answer "what to innovate", there is already an increasing use of data for "how to innovate". That is, using data analytics to enhance the efficiency of the innovation process and seeking the optimal innovation recipe.
One example is Creative Difference, an innovation analytics tool developed by IDEO. The tool uses IDEO's mastery of the innovation key factors to diagnose a company's creative profile. Based on the diagnostic, generic recommendations are given to improve the innovation result. But at no point the company is asked about its innovation performance. If innovation results are left outside the equation, it is impossible to move from the current descriptive analytics to predictive and prescriptive ones.
Creativity Audit, an innovation analytics tool developed by Ferran Adrià and ESADE, is another example. Since 2015, it has been applied to companies, such as HP, through the C4Bi challenge. The tool takes an ambitious and powerful comprehensive approach, including innovations key factors, innovation results, and industry conditioning factors. The tool requires from quantitative but also qualitative information which challenges its standardization and scalability.
Near future of innovation analytics
From what we can see in the field of innovation analytics, from now on the market will continue evolving and more paradigms will emerge. Only experiment minded companies will be early adopters. By 2022 we will have some innovation analytics softwares in the market. The adoption of these softwares to innovate better will be massive, and in turn, create even more precious data. The softwares will compete based on their predictive and prescriptive power. By 2026 the market will consolidate. There will be an oligopoly with the capacity to advise on optimal innovation ecosystems.
In the coming years, we are expected to see innovation analytics becoming a new battlefield for the tech companies.