Cliff Guren is the founder of Syntopical, a consulting company that provides strategic planning and business development support to publishers and technology companies developing content-driven products and services. He’s also held senior management positions at industry-leading companies such as Apple, Asymetrix, Quebecor, Microsoft, Skiff (a Hearst-funded start-up) and Bluefire Productions.
Cliff is a recognized industry leader known for his passion for developing and launching new publishing-related technologies, and he’s also a speaker at DBW 2017, where he’ll discuss the roles big data and machine learning are playing in publishing.
We spoke to Cliff to learn more about his session at DBW, as well as why big data and machine learning are becoming such critical parts of the publishing landscape.
What is the future of machine learning in book publishing? Is it currently a practical investment for publishers?
Machine learning is already being used in publishing. At the simplest level, every time you see and respond to the squiggly line below a misspelled word or awkward phrase, you’re interacting with a machine-learning-driven system. That system incorporates a dictionary, a thesaurus and a set of style guidelines. In all likelihood, that content was licensed from a publisher.
Every time you see an online book recommendation, it’s highly likely that you’re interacting with a machine-learning-driven system. Those recommendations are based on metadata that came from a publisher, and perhaps a machine-learning-driven analysis of the text itself. Machine learning is being woven into the fabric of both commonly used and highly specialized services all around us. Book publishing is already part of the quilt.
There are many other machine-learning-driven tools and platforms that are available today that publishers can use to enhance their businesses. For example, Bibblio has an interesting set enrichment and discovery tools that are being used by publishers such as Oxford University Press and National Geographic.
What about big data?
Big data is not inherently useful or valuable. Its utility and value depend on how it’s organized, managed and used. Many of the current applications of big data are related to distribution and retail, including inventory management, recommendations and pricing. But there are interesting, and perhaps more socially significant, applications in other related areas, including education and scientific research.
Both of these domains will be beneficiaries of big data and machine learning. Why? Because together, big data and machine learning enable us to see patterns that span horizons that had not been visible before.
For example, it’s one thing to understand how a few hundred or a thousand students have used a textbook or online module to learn a new scientific principle, but when you can see how tens of thousands or even hundreds of thousands of students have worked with the content, responded to assessment questions, and then advanced to mastery of the subject, you’re likely to have a new perspective on how to best teach that subject that isn’t revealed in smaller sample sizes.
Will publishers be able to get by without making these technological investments?
They will be able to get by, but it’s unlikely that they will thrive. For some, investing in big data and machine learning will mean making a financial investment. They will choose to build, buy or partner to develop machine-learning-driven systems that amplify the value of their content.
For example, it’s easy to imagine a number of ways that a customized machine-learning-driven system could benefit a large medical or legal publisher. For others, it will mean an investment of time and intellect—understanding what the technologies are, how they are changing discovery, marketing and retailing, and figuring out how to leverage those changes to improve the performance of their books.
Your DBW 2017 session is titled “Big Data, Machine Learning and Chatbots: Current Applications for Publishers.” Can you give us a preview of what you’ll be discussing?
Big data, machine learning and chatbots are high on the list of this year’s most talked about new technologies, but these innovations are not particularly easy to understand. So first, I’m going to walk through the basic, underlying technologies and concepts that drive big data, machine learning and chatbots. Then, we will take a look at how these technologies are being used in publishing-related applications. I hope that the attendees of this session will leave confident in their understanding of these important new technologies, and with a few ideas for how they can be leveraged in their business.
What are you most looking forward to at DBW 2017, and why did you want to be involved?
While I always learn something new at DBW, the thing I look forward to most is checking in with longtime friends and acquaintances, and making new friends. Publishing is a fascinating business filled with smart, articulate, kind and friendly people. It’s always a pleasure to spend time in company of the people in our community.
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