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Excerpted from Fizz by Ted Wright. Reprinted with permission from McGraw-Hill, copyright 2015.

As business buzzwords go, Big Data has now achieved a level of ubiquity not seen since synergy or downsizing. But while the problems associated with storing and analyzing petabytes of digital data may be unique to our time, there is nothing new about the struggle to make sense of too much information. Pharaohs, kings, city planners, and industry leaders have all struggled to make sense of too many figures and statistics. But even as the details and quantities change, the principles remain the same.

The trick to getting value out of your data is not in how you collect or store it but in how you slice and dice it. The goal is to come away with information you can act on and insights you can use to help you make decisions. It's not hard to figure out what is going on with your customers. What you really want to know is why.

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The Cereal Company That Learned Surprising Things About Its Customers

Consider the example of a cereal company that wanted to know how its customers were reacting to the financial crisis of 2008. How did the recession change their buying and eating habits? While sorting through reams of data gleaned from a social media listening tool, the company noticed two things. One, despite having less money to spend, the brand's customers didn't want to compromise on the quality of the food they fed their families. Two, they were very concerned about the quality of their garage.

This second part struck the cereal people as odd. So they went back to the data, and a pattern emerged. Many of these customers were buying in bulk for the first time, and they were storing the excess cereal in their garages. Some of them were even banding to– gether to buy in mass quantities not seen outside the wholesale grocery business that they could share among themselves–a process known as breaking bulk. So now their concerns about cereal had grown to include the quality of their garage. Was it providing enough protection from snow and rain? Would it keep out foraging critters? And they were talking about this on social media in the same conversations they were having about cereal.

This was a valuable insight the company could turn into action. Maybe it could offer a plastic storage bin with every four gallons of cereal. Or use a better glue to seal its boxes so critters would be less likely to smell it. The point is, from all this data the company had acquired, it managed to focus in on one tiny bit of information–conversations about garages–and extract a story that helped it make decisions and engage more meaningfully with its audience.

The Why Behind the What

Approached correctly, Big Data is a tool that helps you see the why behind the what. This is vital to word of mouth marketing because you are trying to have conversations with groups of people who already have a preferred way of communicating with one another. Whether it's NASCAR fans or parents who buy cereal, these people are part of a group, and that group has a way of spreading its own information. If you want to insert your story, your only hope is to give them something they will pass among themselves. This means that your story must be relevant to their interests. By examining the data, you can see not only what interests them but why. That allows you to tailor your message more finely than any marketer could have dreamed of even 10 years ago.

If you know the why, you can tweak your story in just the right way. You can change its emphasis. Or you can go back to product development and make informed changes. Knowing the why lets you get into the minds of your customers and see the world, and your product, the way they see it.

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Fifty years ago, marketers who were good at their job had a good sense of what was going on with their customers. They had empathy and insight. This is still true. But today, Big Data is there to amplify and sharpen those instincts.

This is particularly important in a splintered, heterogeneous society like ours. Fifty years ago, there were three networks to watch, a handful of movies in theaters at any one time, and exponentially fewer brands of spaghetti sauce on the shelf. Today, empathy and insight aren't enough. You can't really act on a hunch that parents in a certain region are storing cereal in their garages because they've started buying in bulk, or that twentysomethings in Boston are starting to buy your beer because a local band mentions it in a song. How would you even know these things?

Instinct and empathy are great, but you need data–lots of it–to tell you that.

Unfortunately, Big Data doesn't surrender its secrets easily. You have to be willing to put in the work, and you have to know how to approach it. The key is to break it down and to ask the right questions.


Making sense of large data sets is a bit like putting together one of those 4,000-piece Lego projects. Take, for example, the Lego Death Star (actual piece count: 3,803). When you fi open the box and see all those packages with all those tiny pieces, it seems inconceivable that it will all somehow become a coherent whole. Still, you open all those packages, and you group all the pieces by size and color and start to consult the instructions. Slowly, you begin putting things together.

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Then, it happens. You realize that this one part you've been working on is the prison block and that other section is the trash compactor, and this one here is where (spoiler alert) Darth Vader kills Obi Wan Kenobi. Once you recognize the patterns, you can make faster decisions. Before long, you barely even need the instructions. You can see where this is going.

Making sense of Big Data works the same way. You are looking for patterns. You want to find little subsets that tell a story. In this way, Big Data can be seen as nothing more than many sets of little data put together. Ultimately, that's all it is. And it's useful to look at it that way.

It also helps if you know what you're looking for. If you had never seen Star Wars, you would never be able to recognize the scenes as they emerge. But if you go into it with a sense of what you want to see, you will start to recognize patterns much faster.

Th is where your marketer's instincts come into play. You separate the signal from the noise in Big Data by being as specific as you can about the signal you're searching for. While you obvi– ously won't know exactly what you're looking for until you fi it, you should know your customers well enough to ask informed questions.

But you also have to put some work into getting to know your own data–something that fewer and fewer marketers are doing these days. That is an unfortunate trend, and one you should resist.

A Little DIY Goes a Long Way

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To get the most from your data, you will eventually hire a professional analyst. With data sets that now routinely take up terabytes of storage space, it has become routine to turn to third-party vendors to make sense of it. There's nothing wrong with that. But be careful of allowing yourself to become alienated from your own data.

Analyzing data is a lot like sending your kids to school. Sure, they'll learn a lot there. But that doesn't let you off the hook from teaching them at home too. Likewise, just because you're paying some fancy analytics firm to decipher your data doesn't give you a pass on looking at it yourself. No matter how good your analytics firm is, it doesn't know your business or your customers the way you do. Besides, how will you know what questions to ask the analyst if you're not conducting your own research on the front end? So take the time to go through your own data. Sit with it for a couple of days, and don't expect to find answers right away. The idea is to experiment. Come up with hypotheses. Follow leads, even if they appear silly at first. I promise that if you pay close enough attention and you put in the time, you will see patterns that eventually make sense. Then ask yourself, Why is this happening? And ask your analytics vendor the same questions. You will get far more out of your data that way than if you simply outsource it.

And ultimately, you want to be paying your analytics firm not to decipher your data but to sort it, categorize it, and find similarities within it for you. Then it's your job to search for the stories contained within.

The Right Questions to Ask

Some questions cannot–or should not–be posed to Big Data. "What's next?" "What do my customers want?" Big, vague inquiries like that will only produce big, vague answers. And it's hard to do much with such big answers in word of mouth marketing.

Better to focus on a couple of things you already suspect are happening, and try to understand what is driving those trends. If you've spent the time going through your own data, and if you combine that with what your marketer's instincts already tell you, you should be able to pose good, specific questions to your analytics team that will yield real, actionable insights.

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The more specific the questions you're asking, the more useful the information that you pull from the data will be. And you need to ask your questions in a few different ways. Sometimes, a small change in wording can make all the difference. Try four or five iterations of the same question, and you'll see that you get different answers. That alone will tell you something about how your customers are communicating.

In the end, you do want answers to big questions like, "What's next for my customers?" But you get them by asking the small questions. The Death Star, big and scary though it may be, is still just a collection of little pieces. Small, targeted questions are how you move from data to information. And for word of mouth marketing, it's information we need, not data.

I know Big Data can seem scary. But remember, in the end, it's just a lot of little data grouped together. As word of mouth marketers, it's our job to find the stories inside the data. When you're looking to start conversations that are relevant to your customers, there is no such thing as too much information, particularly in a fractured culture like ours. Just remember to put in the time, be specific about what you're looking for, and keep searching for the why behind the what. Once you understand that, you can join the conversation rather than trying to initiate one of your own.

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