How wine, baseball, and data offer lessons on disruption
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How wine, baseball, and data offer lessons on disruption

Winning the game means changing the way it's played

Tiger Tyagarajan

Former President and CEO, Genpact



How do you disrupt an industry that's been around for thousands of years? What about America's favorite pastime? Today, it's eat or be eaten when it comes to disruption: Half of all companies today will fail because they are unable or unwilling to disrupt their own industries. With that kind of pressure, the idea of disruption can intimidate.

Last month, Genpact hosted a panel on predicting and disrupting the future, in which I shared the stage with innovators like Pam Dillon, co-founder of Wine Ring, and Billy Beane, executive vice president of baseball operations at the Oakland Athletics and the true story behind the film Moneyball. How they're each disrupting industries that have been around for hundreds of years – wine and baseball – struck me.

What particularly interested me about Pam and Billy is that they didn't draw the line at finding faster or cheaper ways of playing the game: They changed the rules. And we can all do the same – here are three lessons I took from the day:

1. To predict the future, build a foundation of data

Enhance judgement with prediction: Technology is making data prediction faster and cheaper, which means it will soon be ubiquitous. Every company will use tools to help them make predictions about their customers, competitors, and even internal operations. This, in turn, will lead to more informed strategy decisions.

Gather rich, diverse data sets: But in this age of ubiquitous data, predictions will only be as potent as the quality of that data. The best predictions are borne from data sets that are colorful and diverse because they pull from several sources and build multidimensional insights. For example, Wine Ring is trying to build datasets about consumer tastes by analyzing restaurant sales data, distributor data, individual store inventory data, online review aggregators, and more.

2. Your best technology team won't be all tech

Build bilingual teams: To get the most out of your artificial intelligence (AI) and data investments, you must build teams that are multidisciplinary – or bilingual – that pull together a wide range of expertise and skill sets. It's not all about science, technology, engineering or math expertise, although having AI and analytics skills is certainly critical.

In addition to technology experts, you need people on your team who speak multiple "languages," including those with extensive knowledge of the industry landscape, and can put data insights into context. In baseball, for example, you need people who can hit, catch, and pitch. Everyone brings their diverse strengths and plays a vital role, but you need a manager with the dugout perspective to make the difficult decisions, like determining which pitcher to send in when it's the ninth inning and the bases are loaded.

Don't be afraid to bring in outsiders: Sometimes you need to introduce an industry outsider to bring diversity of thought and innovation. Billy, for instance, hired an analyst from the financial services space to interrogate baseball data.

3. Ask yourself: How do emotions play into your data game plan?

Don't rely solely on emotions to make decisions: Emotional decision-making is human – but it can perpetuate poor results. For example, Billy Beane realized that emotions – like loyalty, admiration, or nostalgia – caused managers to make poor choices about the team's line up and roster. By collecting and analyzing data – such as player performance stats – with AI and machine learning, you can spot patterns to evaluate a player's strengths and weaknesses without the cloud of emotion or bias. Now you can base decisions on the numbers as well as experience, rather than emotions alone.

Embrace emotions as data: On the other hand, collecting data on emotions can be one of the best ways to make accurate, useful predictions about customer preferences. This is especially true for companies selling products that consumers buy based on personal taste.

But how do you quantify an emotion? You try and find a scientific way to understand why, for example, one wine makes a person happy or nostalgic versus a sip from another that spurs unease or even disgust. In wine, it's all about finding connections between the physical reactions (taste and smell) that a person has and the words they use to describe how that wine makes them feel.

Other industries can do the same – find ways to quantify customer emotions by listening closely. Then, you can use those insights to better meet their needs on a deeper, more emotional and, arguably, more permanent level.

After spending time with Pam and Billy, I'm now eager to see how other industries – even the most venerable ones – can leverage and experiment with these learnings using digital technologies. With the rise of prediction and proliferation of AI and analytics, the possibilities for unexpected, game-changing innovation are truly endless.

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