Let us start with a rather simple question: Can Big Data help Disruptive Innovation?
Let us start with a rather simple question: Can Big Data help Disruptive Innovation?
At the outset, we must begin by clarifying that most expert analyses have identified Big Data itself as a disruptive technology with tremendous potential. For instance, McKinsey identifies a dozen potentially disruptive technologies that could deliver as much as $33 trillion in economic value and says that Big Data lies at the heart of most of them. IDC says that worldwide spending on Big Data related software, hardware and services will grow to $125 billion. So, by its very nature, Big Data seems to be disruptive, as measured by the impact it is predicted to have.
But, the question here is: can Big Data help an organization to innovate disruptively?
The GE Global Innovation Barometer 2014 found that two-thirds of executive surveyed believed that businesses have to encourage creative behaviors and must disrupt their internal processes in order to cause or create disruptive innovation. Yet, many experienced 'innovation vertigo' – they were unsure of how to move forward with disruptive ideas, products and services.
This is easy to understand. This survey was among growing and mature businesses where executives rise through the hoi polloi by differentiating themselves on the grounds of predictability and conformance. This is far removed from the total uncertainty and risk associated with how disruptive innovation germinates.
Back to GE’s barometer findings. The participants all agreed they need to first become disruption-ready and that it hinges on three factors:
1. Understanding customer and anticipating market evolutions (84% of the responses)
2. Attracting and retaining the most talented and skilled employees (79%)
3. Quickly adopting emerging technologies (67%)
In all these three areas, Big Data can and is playing an important role:
1. Understanding customer and anticipating market evolutions (84%):
Among the companies that have adopted Big Data and are running Analytics, which part of their business are they first applying it to? Turns out that 48% are applying it to Customer Analytics which helps them acquire more customers, reduce churn, increase revenue per customer and improve existing products. In comparison, only 21% are using Big Data for Operational Analytics (Supply Chain efficiency, IT operations, etc). This fits with the rise of ginormous unstructured data from the demand-side chain of business.
2. Attracting and retaining the most talented and skilled employees (79%):
At Google, Lazlo Bock led a pioneering effort to apply Analytics in HR, which has paid off rich dividends. While the intention behind such an effort is not to reduce each employee to a statistic, supporters of using Analytics are being careful about the limitations of such an approach. It takes time and a lot of investment in capabilities to realize the advantages of HR analytics. Deloitte's Bersin estimates that, on average it takes an organization between five to eight years to put the necessary people, processes and infrastructure in place to become a data-driven culture.
3. Quickly adopting emerging technologies (67%):
Although there are not too many examples of this, the most well known one is probably how GE used reverse innovation and turned glocalization on its head, to fund a $3 billion dollar transformation that unleashed over a 100 health-care innovations in a six-year period to disrupt itself and the market. It has turned to Big Data to embed sensors in the 250,000 ‘intelligent machines’ manufactured by GE and transform them from products to services. For instance, it now sells jet engines as a service with over 5000 data points analyzed per second.
So, it appears that Big Data can help organizations become disruption-ready. Will it help them take the next step?
This is where we need help from Clay Christensen's thoughts. He points out that in case of sustaining innovations, the incumbents almost always win. When it comes to disruptive innovation, entrants nearly always win.
Another differentiation is necessary and important. The likes of Google, Facebook and Netflix are neither entrants nor do they fit the traditional incumbent labels. They pioneered the use of Big Data and continue to be at the forefront of applying insights from Big Data to disrupt themselves and markets.
All this, you may agree, is merely scratching the surface of this topic. We thus need to explore further how Big Data is inherently disruptive by itself, how it is fueling innovation (both sustaining and disruptive) among traditional incumbents, the Analytics pioneers (Google, Facebook, Netflix, LinkedIn) and startups.
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