Here Comes the Big Bad Data: Practical Data Uses for Publishers

Big DataWhen many people think about book publishing, they often conjure a romantic image of editors sitting in a room all day—reading, choosing the stories they love and writing handwritten notes in the margins of a manuscript. Those of us who actually work in the realm of publishing, however, understand that a publishing house is a machine, full not only of editors, but also those in slightly less romantic departments, like sales and marketing, accounting, IT, production and many more.

But the outside world doesn’t have it totally wrong: at the end of the day, many of the people working in a publishing house, regardless of their department, got into the business because of a love of literature. And that’s why inserting hard facts and data-driven decisions into the sentimental field of traditional publishing has proved so difficult.

Part of the hurdle is that publishers have to accept that “Big Data” is not a fad. We’ve been bombarded with it for a reason and it’s here to stay. So, that being said, how can publishers make Big Data work for them, instead of working around Big Data?

Let’s say, hypothetically, that Big Data could be used to help an editor acquire a title he or she has their heart set on. Let’s also say that Big Data can help a sales and marketing team champion the books that they love more efficiently. Now that’s something publishers could rally around.

Well, Big Data can do all of that and more. How many acquisition editors have to fight tooth and nail to acquire a title from a debut author and win or lose based solely on how much of an advance they can pay? Now with market research around competitors and bestselling titles at the major retailers, an editor can give a prospective author or agent concrete evidence that he or she is the leader in that particular genre with a proven success rate—not relying on anecdotal data. It’s powerful to be able to tell a high-profile author of children’s history titles, for example, that your house consistently sees the most juvenile nonfiction titles in Amazon’s “Top 1,000.” A promise on steady return for authors can make them less focused on payment up front.

Furthermore, publishers can also use Big Data to get out from under the thumb of their retailers. Instead of scrambling for every retailer co-op, publishers can use past credibility in order to secure favorable placement.

For example, when retailers are choosing the titles that they will showcase for, say, Presidents Day, a publisher could whip out their stats and say that on average their historical nonfiction titles pertaining to past presidents have sustained not only a higher price point than the average but also higher overall ratings and better reviews at that retailer than similar publishers competing for that space. Only a savvy publisher with information at their fingertips, though, will be able to supply those stats and win that placement.

For the love of books, publishers need to start utilizing Big Data in all of their dealings. It’s not enough to rely solely on anecdotal evidence anymore, if publishers want to stay ahead of the curve and be competitive in the market, especially when trying to gain leverage in dealings with authors, agents and retailers.

For more details on how to get the most out of Big Data, make sure to check out Vearsa CEO Gareth Cuddy’s talk, “Big Data: Everything You Want to Do But Don’t Know How,” at the upcoming Digital Book World Conference, or visit

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One thought on “Here Comes the Big Bad Data: Practical Data Uses for Publishers

  1. David Aldridge

    I’m all for data analysis and business intelligence etc, and I dare say that the publishing industry does not make as much of it as it could do.

    However I have a hard time recognising that these use cases, of manageable size, well-structured and well-understood data sets, could be described as Big Data.

    In fact, I have trouble seeing any company in the publishing industry aside from major retailers, and Amazon in particular, as having any use for Big Data skills.



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