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 of California San Diego’s Contextual 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.

Innovation Starts with Changing the Frame

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.

Figure : Pathway to Analytics-driven Innovation

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.

Design Thinking + Data Science (Analytics) Driving Innovation

In the blog “Design Thinking: Future-proof Yourself from AI”, I introduced the integrated Design Thinking – Data Science engagement framework (thanks John Morley!).

Figure : Design Thinking Humanizes Data Science

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.

Stage 1: Curiosity (Embracing Customer Empathy to Ideate)

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.

The Curiosity stage is a great opportunity for organizations to embrace the “Without Exercise”; that is, pose a provocative question using the format “What if [this condition] no longer exists?” That is, remove an everyday given such as driving a car or possessing a landline phone or centralizing all of your data in the cloud to see what creative thoughts the statement might fuel.

Customer empathy is a key component of the Curiosity stage. The more you understand what your customers are trying to accomplish (the customer challenge or opportunity), the jobs to be done, and the associated gains and pains, the better positioned one is to leverage curiosity to drive ideation. Your design thinking research will dictate use some great tools to gain customer empathy including:

• Ethnographic (Observation), Participatory (Engaging) and Qualitative Research
• Creation of Challenge Statements
• Concept Mapping
• Problem Framing
• Stakeholder Mapping
• Persona Development
• Customer Journey Mapping

From a Data Science perspective, we want to decompose the persona’s challenge into the enabling decisions, analytics and data sources. We can make use of the following methodologies and tools to define the analytics necessary to support the Curiosity stage:

Figure : Interweaving Design Thinking and Data Science to Unleash Economic Value of Data

At this point in the “Pathway to Analytics-driven Innovation” map, you should have multiple perspectives on the customer challenge, with a broad understanding of where and how analytics “might” be able to help.

Stage 2: Creativity (Create)

Creativity, which the use of the imagination, ideation and ingenuity to create (remember, create is the foundational word in creativity) is something that uniquely and more effectively solves a customer problem. There are several marvelous and fun Design Thinking tools that help fuel the creativity process including:

• Storyboarding
• Mockups
• Wireframing
• Prototyping
• Usability Testing

From a Data Science perspective, we will blend different data sources, test different combinations and ensembles of Machine Learning and Deep Learning algorithms in order to quantify the patterns, trends and relationships that are better predictors of performance and behaviors. The tools and techniques that we will use in the Creativity stage of the “Path to Analytics-driven Innovation” map includes:

• Analytics Proof of Value
• Data Transformation and Enrichment
• Feature Engineering
• Ensemble Modeling

Figure : DEPPA Data Science Development Process

At this point in the “Path to Analytics-driven Innovation” map, we have tested the user viability of the new innovation (using Design Thinking) and analytics value and scalability (using Data Science).

Stage 3: Innovation

The final stage, the Innovation stage, is driven by integrating AI (Machine Learning, Deep Learning, Reinforcement Learning, etc.) with the customer journey to create a new “intelligent” product and/or service that can continuously learn and evolve through the usage of that product and/or service, with the ultimate goal of learning and evolving the product and/or service without human intervention (autonomous).

The Design Thinking tools that will help us in the Innovation phase include:

• Cover Story Mock-ups
• Scenario Modelling
• Customer Trial and Testing
• Experimentation
• Validate & Scale (Lean Start-Up: Build, Learn, Measure)

The Data Science tools and techniques that will accelerate continuous learning and adapting (and the creation of intelligence) include:

• Federated Learning

Summary: Analytics-driven Innovation Framework

As the Chief Innovation Officer, I am seeking to 1) blend data science, design thinking, value engineering and economics within a 2) constantly transforming business and technology environment in order to 3) continuously learn, adapt and create new sources of customer, product and operational value.

The “Pathway to Analytics-driven Innovation” will evolve to bring together the different disciplines of data science, design thinking, value engineering and economics to create, capture and deliver new sources of value to our customers, our company and our ecosystem.

Figure : Pathway to Analytics-driven Innovation

Heck, if one can’t have an aspirational vision, one might as well retire. So, welcome to the continuation of my Most Excellent Adventure!