Be More Da Vinci: Why Data Scientists Should Be Working with Designers, Futurists and Business Executives

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In this special guest feature, Michael Kanazawa, EY Global Innovation Realized Leader and EY Americas Advisory Growth Strategy Leader, discusses why data scientists and engineers should be working with designers, futurists, and business executives. With over 20 years of strategy and innovation experience, Michael is seasoned in developing corporate strategy and transformation as well as in building businesses – both within global corporations and as an entrepreneur. He earned an MBA from the University of Southern California, and BA in Economics and Mathematics from the University of California at Santa Barbara.

Mathematician or artist? Right now, we’ve built a way of living, educating and working that says you’re either one or the other. Yet, in fact, this way of viewing the world is both myopic and inaccurate. After all, look at Leonardo Da Vinci, not only among history’s greatest artists but one of its foremost scientific thinkers and inventors too.

As a business, viewing your workforce in these black and white terms is also increasingly dangerous. Sure, not many of us are a Da Vinci but we all have skills that straddle both art and science – individually and, perhaps more importantly, collectively. For organizations, maximizing these diverse capabilities by combining human-centered designers, data scientists, futurists, and business executives is the way to lead the future. Failing to do so risks being consigned to the past.

Deterministic thinking

Before we consider why, let’s remind ourselves of two of big data’s most common misconceptions. First, that it’s a vast sea, with information disparately spread out just below the surface. It’s not, it’s a well. A deep pool, the further down which we go, the richer the patterns, connections and insights we uncover. Second, the more data we have, the more insightful we’ll be. Not necessarily. Often, having a vast amount of information can be overwhelming, making it harder, not easier to find the answers we’re looking for.

Yet there’s a third, more systemic issue at play here too. Currently, organizations tend to forecast using a deterministic mathematical model developed by ancient Greek philosophers. This essentially involves making precise predictions based on historical information and known relationships before searching the data for evidence that confirms them.

The problem is, this leaves no room for random variation or finding unanticipated futures. No opportunity to uncover deeper shifts in underlying patterns within the surface patterns we can see or identify connections that weren’t previously apparent.  As a result, a whole heap of possible insights go to waste.

See what you’re not seeing

The real state-of-the-art, therefore, comes in employing tools such as AI, neural networks and deep learning to go further, faster. To show us what we’re not seeing, rather than confirm what we’re expecting to see. To take information from inside and outside our organization. And do it all in real time.

By doing so, businesses can get to know consumers as individuals – then use that knowledge to generate new value and better outcomes for customers. Imagine, for example, an online retailer combining what it already knows about a customer’s buying patterns – perhaps they’ve previously paid a premium for more sustainable products – with external data that tells it that same customer is about to give birth to their first child. Armed with this previously unknown connection, the retailer can target them with relevant marketing around sustainable baby wipes and diapers. The offer and products are introduced just at the right time, in the right way, to the exact right customer.

Beyond technology

But where’s the employee and talent story for companies here? After all, the potential of these technologies in helping them make better, more data-driven decisions, has long been clear. Why does it mean business leaders should be teaming up data scientists, designers and futurists?

Answer: Da Vinci.

As the scientific part of your team’s collective brain, data science engineers can find the quickest route into the data. Meanwhile, the designers are the artists; the creators of new products and services that deliver what customers want. Yet Da Vinci’s success was not just down to his brilliance as a polymath or skill with a paintbrush; it hinged on his ability to view problems differently. To follow paths that no-one had thought to follow before. That’s where futurists come in.

They have the ability to look at things in new ways and, as result, find new things. Rather than simply consider an historical dataset’s picture of the past, they will consider what human behavior, life and business is going to be like further down the line  – then take cues from the data to see patterns unfolding that don’t exist yet and build a more accurate profiles of individuals’ habits in the future. This diversity of thought and problem solving that was within Da Vinci’s personal experience is what we need to build as individual leaders and across blended teams in this new world.

Personalized and profitable

The value of this is considerable. It enables firms to take their understanding of customers to the next level and create products and services that are personalized to their future needs, not just their current ones.

Already, we’re seeing a new breed of businesses who understand this, pairing up their data scientists, designers and business decision-makers with the free-thinking approach of futurists. This, in turn, is letting them get ahead of competitors in forecasting – and meeting – the evolving requirements of customers and, ultimately, boost profitability.

COVID-19 has simply served to accelerate our progress along this curve, showing us how quickly things can change and providing a real existential threat to firms who continue to rely on more traditional operating models. Having the right data and technology is no longer enough; you must also have a team diverse enough to interpret and act upon it. It’s time to be more Da Vinci.

The views reflected in this article are the views of the author and do not necessarily reflect the views of the global EY organization or its member firms.

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  1. Thanks for the great tips! I’ve always thought of myself as a creative person, but since going into business for myself I’ve discovered my analytical side and have been surprised at how easy (and beneficial) it is to combine the creative with the analytical.

  2. I appreciate the useful advice. Small company owners produce content about whatever they want without any type of plan in place, and then they wonder why it isn’t producing the desired results. I fully agree that data science should be a component of any content strategy, but there are proper and improper ways to go about it.