Is Analytics-driven Innovation the Ultimate Oxymoron?

Sometimes it just takes a simple, provocative statement to kick-off the innovation process – to remove an everyday given like driving a car or possessing a landline phone or centralizing all of your data in the cloud – to fuel the innovation process. Henrik Christensen, director of University San Diego’s Contractual Robotics Institute, issued such a provocative statement:

“My own prediction is that kids born today will never get to drive a car.”

I have recently been promoted to Chief Innovation Officer at Hitachi Vantara. I am very excited about the opportunity to build upon my work to interweave data science, design thinking, value engineering and economics to create a “Pathway to Analytics-driven Innovation” map that helps organizations derive and drive new sources of customer, product and operational value.  Think of the “Pathway to Analytics-driven Innovation” as a maturity model that measures how effective organizations are at leveraging analytics to deliver innovative products and services to the market.

Analytics-driven Innovation…isn’t that some sort of oxymoron like jumbo shrimp or definitely maybe? I mean, isn’t analytics about doing what the data tells you to do, while innovation is doing something that has never been done before? Not necessarily.

Here is how I define innovation:

The “Pathway to Analytics-driven Innovation” map is a process for integrating customer journey-centricity (Design Thinking) with advanced analytics (Data Science) to translate an idea into a product or service that creates distinct, differentiated value (Economics).

From a 2008 Booz & Company’s Global Innovation 1000 report, we get this important perspective on innovation:

“Rather than simply throwing more money at R&D, it’s time to address the fact that the real problem hasn’t been identified – innovation can be systematic. Growth can happen by using proven methodologies and tools.”

“Innovation can be systematic”. Like the Booz report, there’s a good body of work out there on the theme of systematic innovation. And if innovation can be systematic, then analytics can play a role in enabling, driving and maybe even optimizing systematic innovation.

Fortunately, there is lots of work already being done in the area of innovation. However, for my purposes, I needed a simple innovation model so I can more easily contemplate where and how analytics impacts each phase of the innovation process. So, I have simplified the Innovation Framework to three stages as depicted in Figure 1.

So, let’s leverage our old friends – data science and design thinking – to help us make the framework in Figure 1 more relevant and actionable.

Figure 2 is a great starting point for creating a “Pathway to Analytics-driven Innovation” map. It integrates Design Thinking, which can uncover new ideas through customer empathy, with Data Science, which can validate whether those ideas can deliver economic value at scale. Perfect partners-in-crime…like Batman and Robin, or Mermaid Man and Barnacle Boy. 

Let’s see how Design Thinking and Data Science enable the Analytics-driven Innovation Framework.

Curiosity is the strong desire to know or learn something; fostering an inquisitive demeanor or behavior fueled by a provocative statement or question; an eagerness to “take things apart” to see how they work.