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Design Thinking:  Is it a Floor Wax or a Dessert Topping?

Do you old schoolers remember the Shimmer Floor Waxcommercial from the early (and best) days of the TV show “Saturday Night Live”?  In case folks don’t remember (because we are old) or never saw it (because you’re too uncool), the Shimmer Floor Wax commercial asked the question: “Is Shimmer a floor wax or a dessert topping?”

Yes, Shimmer Floor Wax did everything.  And that summarizes my frustration with Design Thinkers!

Design Thinkers have bastardized Design Thinking into a concept that presupposes to solve every problem. The over-prescription of Design Thinking as a cure-all for all that ails you is a farce; a lie, a façade, an empty shell. Design Thinking has become both a floor wax and a dessert topping.  So, go ahead and kick me out of the Design Thinking “Kool Kids Club.”

My issue with Design Thinkers is that they are not taking the time to understand where and how Design Thinking fits into the overall scheme of things; what role Design Thinking can play in driving towards specific business and organizational outcomes. The fault lies with Design Thinkers who forget that “Design Thinking is not an end in of itself; Design Thinking is a means to an end.”

Design Thinking is a powerful additive that can supercharge outcomes, but it’s critical to understand you use Design Thinking in the context of the outcomes you’re trying to achieve.

Design Thinking Helps Deliver More Relevant Analytic Outcomes

I find that Design Thinking – in the context of identifying, validating, valuing, and prioritizing the business use cases – supercharges our data science envisioning process. Design Thinking not only helps in improving the completeness, effectiveness and relevance of our analytic models, but equally important is the resulting organizational alignment, adoption and buy-in around the analytic results.

Let me share some specifics as to where and how we use Design Thinking in context of our Data Science envisioning process.

Technique #1:  Brainstorming Data Science Use Cases

One of my favorite Design Thinking tools is the Customer Journey Map.  A Customer Journey Map captures the tasks, actions or decisions that target customers or stakeholders needed to make in the accomplishment of a specific activity (buying a house, fixing a wind turbine, going on vacation, improving on-time deliveries).

We add an economics perspective to the Customer Journey Map to identify the high-value actions or decisions (green arrows) as well as the impediments or hindrances (red arrows) to a successful customer experience (see Figure 1).

Figure 1: Enhanced Customer Journey Map

Adding the economic dimension (green arrows and red arrows) to the Customer Journey Map enhances the context in which we use the journey map to identify the specific sources of customer, product and operational value creation. Check out the blog "Digital Transformation Law #6: It’s About Monetizing the Pain" for more details.

Technique #2:  Prioritizing Use Cases

The Prioritization Matrix facilitates the discussion between key stakeholders in identifying the “right” use cases around which to focus the data science initiative; that is, identifying those use cases with meaningful financial, customer, and operational value as well as a reasonable feasibility of successful implementation (see Figure 2).

Figure 2: Prioritization Matrix

While the Prioritization Matrix is not a traditional Design Thinking tool, it should be because in many cases, it is the secret sauce for ensuring organizational alignment, adoption and buy-in of the high-value use cases.  See the blog "Prioritization Matrix:  Aligning Business and IT On The Big Data" for more details on the workings of the Prioritization Matrix.

Technique #3:  Uncovering and Scaling Heuristics

Data Science discovers and codifies the trends, patterns, and relationships – the criteria for analytics success – buried in the data.  I call these unknown unknowns.

Design Thinking, on the other hand, uncovers heuristics or rules of thumb (e.g., change the oil in your car every 3,000 miles, see the dentist every 6 months) used to support key decisions.  Design Thinking uses design tools such as personas, journey maps, and storyboards to tease out these heuristics--the unknown knowns (see Figure 3).

Figure 3: Design Thinking Uncovers Unknown Knowns (Source: Roger Martin)

It is this combination of what’s in the data coupled with what’s in subject matter experts’ brains that lead to delivering more robust analytics. See the blog "Using Design to Drive Business Outcomes, or Uncovering What You" for more details on how to use Design Thinking to uncover Unknown Knowns.

Making Design Thinking Work Summary

So hopefully my opening harsh statements have been balanced with more pragmatic advice about where and how to leverage Design Thinking in context of what you are trying to achieve. 

In summary:

  • Design Thinking has become the industry’s new “most abused” concept.
  • Design Thinking is a means to an end; Design Thinking is not an end.
  • Design Thinking accelerates two important outcomes: 1) fuels innovative thinking around identifying, validating, testing, refining and valuing ideas and 2) drives organizational alignment around those ideas.
  • Don’t just learn Design Thinking; learn Design Thinking in context of the outcomes you are trying to achieve.
  • Finally, Design Thinking is a powerful additive that can supercharge outcomes, but you first must understand towards what outcomes you are driving.

So maybe you’ll allow me back into the Kool Kids Club.  Hey, I’ll bring my own Shimmer Floor Wax!

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Tags: #AI, #BigData, #DataAnalytics, #DataMonetization, #DataScience, #DeepLearning, #DesignThinking, #DigitalTransformation, #DigitalTwins, #Economics, More…#IIoT, #InternetOfThings, #IoT, #MachineLearning, #NeuralNetworks, #Smart, #SmartCity, #SmartSpaces

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