Data Science and Design – a Match Made in Heaven

“Water, water everywhere but not a drop to drink” goes the evergreen cliché. Thanks to the exponential advancement of information science and technology, the same can be said for data today. We are drowning in an abundance of data—but how much of it is really useful?

By itself, “data” is a bunch of meaningless values. It is only when placed in or applied to a context that it turns into powerful planning, feedback, and steering input. As Riley Newman, ex-Head of Data Science at Airbnb puts it, “Data don’t number, it’s people.” Conventional research often misses the subtleties and nuance of context, culture and affect, and is invariably presented in complicated and abstruse forms. To unlock the potential of Data Science, a combination of “looking in the right places” and “making the insights visible” is necessary—or in simpler terms, “getting the right data, and communicating the data right”. This is where Design Thinking can help.

Design Thinking is a systematic process of creative problem-solving, that paves the way for process or product innovation with humans at the center since even fully automated systems report to and are operated by humans. Design Thinking draws on a combination of ethnographic & ergonomic research methods along with lateral and creative techniques to analyze a given problem space and open up opportunities for innovation.

What does statistical analytics have to do with design and innovation? Turns out, plenty. Just a simple glance at the literature throws up page after page of links from academia, business, and consulting. But perhaps none captures its essence best as Oana Bradulet. She says: “In analytics projects, one starts by exploring the data, slicing and dicing it in various ways to uncover some meaningful insights. The difficulty with this approach in the context of data science stems from rushing to find answers when we don’t even know what questions we are asking.”

One starting point where data science and design thinking naturally come together is anticipating and mitigating “human error”. There is no better example than a modern aircraft cockpit. Flying an aircraft involves collecting and processing vast amounts of real-time data, to inform and empower the pilot to make the right decisions from start to finish. In fact, much of the pilot’s work is now done by computers.

Testifying the efficacy of data science, automation technology, and human-centric design working together to perform a life-critical task repeatedly and without fail. However, despite the incredible advances in this particular area, failures still occur whether due to technical or human causes, and point to the need for improvement. Sometimes revealing big gaps in the model that were missed out on by everyone. The key gap areas remain in getting the right data and communicating the data right.

An instructive example is Netflix, a high customer experience and data-driven business. If you watch “The Fast and The Furious” one night, Netflix will recommend a Mark Wahlberg movie for you the following night – thanks to data science. But did you know that Netflix also uses its data insights to inform the way it buys, licenses, and creates new content?

Two examples of how it leveraged big data to understand its subscribers and cater to their needs are the blockbuster shows “House of Cards” and “Orange Is the New Black”. In a nutshell, matching the data outcome to the market and end-user needs & constraints.

As Vahndi Minah writes, “In the end, the marriage between data science and design thinking is all about understanding our end-user, and in turn, how we can help our clients best serve that user.” Today, many organizations are investing in data and design capabilities but only with tightly woven disciplines of design and data together, it will be possible to unlock their full benefits and reinforce long-term innovation capability.

So how can Design Thinking and Data Science team up to give us better insights and understanding of our data, and unlock new possibilities for innovation and progress? Jon Wettersten and Dean Malmgren of IDEO literally advocate integrating these two teams. They illustrate the parallels between the five-step Design process and the equivalent Data Science stages:

• EMPATHIZE = Frame, define and model the problem
• DEFINE = Measure, map, and model the process/journey
• IDEATE = Inform, enhance, and experiment around the failure points identified
• PROTOTYPE = Simulate, visualize and iterate improvement ideas as prototypes
• TEST = Test, validate and implement (automate) final solution

We can assert that making data science and design thinking work together is a sure-fire way to power-pack the innovation and decision science capability of your organization.

By Prof. Arvind Lodaya, Professor, School of liberal arts and Design Studies, Vidyashilp University