When 'Big D' meets 'little d': An equation for innovation

This quarter, I’m teaching a class at the Stanford Graduate School of Business with my friend and colleague, Sarah Soule. We’re collaborating with six pioneers in our local foodshed to design disruptive solutions to strategic business challenges they face. Teaching design thinking to a room full of MBA students can be like teaching a clowder of house cats how to swim. But it has created an opportunity for us to think creatively about how we might frame design thinking in ways that are more intuitive to an audience of generally hyper-analytical, solution-oriented people. Now, I have a Stanford MBA myself. So I understand the irony in revealing that I had previously developed an “equation for innovation”. I use it as a construct for understanding how we might help teams more frequently design solutions with greater disruptive potential (see figure 1, below) through work we are doing at the FEED Collaborative, which I co-founded.

innovation-curve-matt-rothe Figure 1: The Innovation Curve

Only until now, having tested its application with some success in our class this quarter, has it occurred to me to share this equation publicly:

matt-rothe-equation

This is an equation where,

In = Innovative capacity of people/teams/organizations

pt = practice (cumulative sum of the team)

et = team empathy (as a function of team dynamics and motivation)

es = self empathy (as a function of self awareness and reflection)

eu = user empathy (as a function of engagement, observation and immersion)

tc = team composition (as a function of diversity in experience and personality)

io = number of original ideas generated

id = number of divergent additions on original ideas

pt(ioŸid) = pt(et+tc)

de = domain expertise

db = domain perceived need/solution bias

dc = domain contribution quotient (de/db)

and assuming the sum of es + et + eu is positively correlated to insightful inference of people’s needs.

Having an equation to work with has allowed us to answer this question: “Given the composition of folks in my classroom, under my mentorship, or in my employ, where might we need to focus our educational or managerial efforts?”  Put otherwise, having an equation of this type provides a diagnostic tool for understanding where teams need support in order to maximize their innovative potential.

In the case of our MBAs, for example, we’ve focused more heavily on their domain contribution quotient, dc, assuming many of them will continue to be leaders, experts, and problem solvers in their respective fields after graduation. This led us to demonstrate, in ways that we never have before as a teaching team, that the relationship between building empathy for users and building context around their problems through the collection of data is two things simultaneously: it is synergistic and a valuable management tool for deriving important insights. These insights can be around anything from products and customers to company culture and the competitive landscape.

We have, in this way, opened the door to an intriguing exploration of what can happen at the intersection of big data and design thinking, or, as we’ve been thinking about it, “when 'Big D' meets 'little d'”.  Having an equation for innovation, it turns out, allows us to not only build the innovative capacity of teams, but our own innovative capacity as teachers and managers as well.

This is the beauty of qualitative math: good answers always lead to better questions.