Why big data only tells half the story

Originally published on May 08, 2014 as a Guest Column in The Globe and Mail.

Thick Data vs. Big Data

For the past few months, big data has been on the tip of everyone’s tongue. I’ve been asked about it in conference and planning sessions, and I was recently stopped in the hallway by a client’s CEO to discuss its impact on business, industry and society.

The first thing you need to know is that big data is BIG.

It has been heralded as the next essential tool for market success. According to a research report by McKinsey Global Institute, “so-called big data will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus.”

To my layman’s way of thinking, big data is technology’s answer to the classic McDonald’s question: “Would you like fries with that?” It scours our electronic footprints to suggest related things that we may be interested in buying, owning or doing. Big data tries to predict our behaviour, in many ways, and as such it could become an indispensable tool for business builders.

How can small and medium-sized businesses use big data? Any startup building digital games and apps may be in a position to collect and scrutinize many terabytes of user data, enabling them to identify behaviours, problems and needs – and new growth opportunities – faster than ever. They can also test different versions of their software and measure the results instantly, so they can continually innovate and bring new products to market ahead of their competitors.

And if you’re not a super-smart IT company, you can still benefit from huge databases (in the cloud and elsewhere) that are increasingly becoming available to third-party users. For instance, retailers could tap into big data services that measure real-time consumer confidence, customer travel patterns, or even the weather – to find out whether to put swimsuits or umbrellas on sale tomorrow.

But big data has its limits. Giant generic databases may be able to tell you where your customers went yesterday, but they won’t tell you if those people were smiling along the way.

For business leaders, this is a critical insight: Big data tells us only half of the story. And it might even leave out the identification of opportunity, which is the most compelling part of the tale.

I’m not big on quoting academic principles, but there is one that has stayed with me since my undergraduate days in consumer behaviour 101: the concept of cognitive dissonance. Loosely defined, it’s the gap between consumer behaviour and feelings.

Consumers might be behaving in a certain favourable way (using your product), but that doesn’t mean they’re happy about it. They may, secretly or openly, crave a better solution. But they don’t know where or how to find it. It is in this gap that opportunity thrives – identifying and addressing the wants and needs of consumers who are still not satisfied.

This is why big data tells only half of the story. To understand the other half, I turned to Leslie Perkins, a seasoned qualitative researcher and strategic planner who now runs her own research firm. “Big data is global; it has a seemingly limitless ability to connect data points,” she says. “It sees patterns in all behaviours: social, consumer, political, religious.

“However, on its own, big data is limited by the intelligence of its analytics, and it lacks emotional insight. Big data’s effectiveness is dependent on analytics to connect meaningful data points, but numerical meaning and practical meaning are extraordinarily different.”

Ms. Perkins adds that big data cannot explain customer motivation or satisfaction, or whether consumers’ relationship with a brand makes them feel like an inspired advocate or an unwilling captive. “Big data is thin,” she points out. “It is simply the trace or outline of behaviours, without context or understanding.”

The complement to big data is not “small data” – it’s what social scientists call thick data. It’s filtered through the lens of human experience and interaction, enhancing numerical data with colour, observation and meaning – not so much a 90-year survey of weather patterns as a look at what people in a beach community do when it rains.

With big data, we use automated algorithms to infer meaning from the patterns in millions of data points. With thick data, we see the stories behind those patterns.

To understand how big and thick complement one another, consider the following comparisons:

  • Big data delivers numbers, thick data delivers meaning. It reveals the social context and connections between data points.
  • Big data is data-centric, thick data is human-centric.
  • Big data would answer where and how far you travel in an average week. Thick data explains the meaning of those trips, and which ones are valued and which ones are loathed.

Adding the qualitative insights of thick data to a foundation of big data adds depth to our understanding of consumers, and helps us do the following:

  • Segment markets based on factors deeper than behavioral metrics.
  • Enable deeper, more robust conversations with consumers.
  • Understand not just what consumers want to hear, but how they want to be spoken to.
  • Engage customers’ emotions to build loyalty and preference, and create brand advocates.
  • Achieve deep and disruptive insights that reveal the meaning and motivators that can change consumers’ perceptions and even affections.

When I coach business leaders I ask them to continually ask themselves one question: “What is keeping my customers up at night?” I ask myself the same question regularly. If I can understand and solve my customers’ challenges before my competitors do, then I will have a thriving business.

Big data is critically important. But it will never be a substitute for face-to-face insights or the understanding of human emotion as a motivator for every decision that we make – in business and life.

Balance both.

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