The problem with living in a rapidly changing world is that things are always changing. But as Yogi Berra (famous Hall of Fame baseball catcher for the New York Yankees and occasional American philosopher) said: “You can observe a lot by just watching.”

Well, more than watching, we are living in this fast changing world. And nothing impacts our expectations more than our own personal experiences. Whether you are using Amazon for your shopping needs…

…or Netflix for your viewing pleasure…

…or eHarmony for whatever you use eHarmony for…

…everyone seems to have a recommendation as to what you should buy, view, listen, vacation, drive, date, sit, turn and eat next! These firsthand personal experiences are shaping the world of Big Data, data science and application development by changing the expectations of our business stakeholder and customer communities. Your business stakeholders don’t want reports and charts, even impressive and swirling dynamic ones. They want highly relevant business recommendations that help them do their jobs better. And your customers want highly-personalized, highly-relevant recommendations that first and foremost look out for what’s important to them. That means that successful data science teams need to not only know the latest data science techniques, but also need to become more fluent and literate in the art of design thinking so that organizations can deliver on these changing expectations.

Role of Design Thinking For a Data Scientist

I have written previously about how design thinking can unleash an organization’s creative thinking capabilities (see “Can Design Thinking Unleash Organizational Innovation?”). Building predictive and prescriptive analytic models is not sufficient in a world where our users personal experiences have dramatically changed their engagement expectations.

As a refresher, Design Thinking is “building a deep empathy with the people you’re designing for; generating tons of ideas; building a bunch of prototypes; sharing what you’ve made with the people you’re designing for; and eventually putting your innovative new solution out in the world[1].”

Figure 1 shows the Design Thinking process as taught by Stanford’s Design School (d.school).

Figure 1: Stanford d.school Design Thinking

My design thinking cohort in crime, John Morley, is doing some innovative thinking in this space (see his recent blog “A Blueprint for Better Program Design”). John, Leon Zhou from Google and I have been discussing the linkage between design thinking and machine learning. I love how the chart below, a bit of an eye test, brings these two seemingly disparate disciplines into alignment (see Figure 2).

Figure 2: Mapping Design Thinking to Machine Learning

Let’s drill into the graphic a bit more, and try to understand the explicit, actionable integration points between design thinking and machine learning (see Table 1).

Table 1:  Integrating Design Thinking and Machine Learning

Linking Design Thinking and Machine Learning is really not that hard, if we think about these two disciplines from the perspective of trying to optimize stakeholder and customer decisions. The business stakeholder and customer decisions drive alignment between the 5 Design Thinking and Machine Learning stages:

• Emphasize/Understand Stage: Define the decisions that support the use case and understand the challenges and impediments to those decisions from the perspective of the stakeholders and customers.
• Define/Synthesize Stage: Define the hypotheses for the decisions and understand the environment (e.g., decision latency, quality, granularity) in which the decisions will be rendered in a way that is both actionable and measurable.
• Ideate/Ideate: Brainstorm the variables and metrics that might be better predictors of decision performance.
• Prototype/Tuning: Test and fine-tune the analytic models across the wide range of potential variables, transformations, enrichments and analytic algorithms.
• Test/Validate: Measure model goodness of fit and ultimately, the effectiveness of the decisions; use the decision results to update and fine-tune the analytic models, and stakeholder and customer engagement environment.

Changing Personal Experiences Create Business Model Opportunities

Changing customer expectations open up new business model opportunities. Think how Uber and AirBnB have changed our personal experiences with respect to car transportation and house sharing, or how Amazon has changed our personal experiences with respect to product selection and delivery, or how Netflix has changed our personal experiences with respect to on-demand entertainment. These changing personal experiences are the source of business model disruption and customer disintermediation.

And these changing personal experiences come back to one common denominator – how we make decisions. Decisions about what to buy, or how to get to our next destination, or what to watch.

If your data science and design thinking capabilities aren’t in total alignment around identifying, understanding, validating, prioritizing, optimizing and rendering recommendations to these decisions, then you just might miss being the next Uber or Netflix.

[1] http://www.designkit.org/human-centered-design

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