I’ve written before on this site about the vast implications of the forthcoming semantic web. In short, it will be a game-changer–but it certainly won’t happen anytime soon. Every day, though, I hear about organizations taking one more step in that direction. Case in point: A few days ago, Harvard announced that it was “making public the information on more than 12 million books, videos, audio recordings, images, manuscripts, maps, and more things inside its 73 libraries.” From the piece:

Harvard can’t put the actual content of much of this material online, owing to intellectual property laws, but this so-called metadata of things like titles, publication or recording dates, book sizes or descriptions of what is in videos is also considered highly valuable. Frequently descriptors of things like audio recordings are more valuable for search engines than the material itself. Search engines frequently rely on metadata over content, particularly when it cannot easily be scanned and understood.

This might not seem like a terribly big deal to the average person. Five years ago, I wouldn’t have given this announcement much thought. But think for a moment about the ramifications of such a move. After all, Harvard is a prominent institution and others will no doubt follow its lead here. More metadata from schools, publishers, record companies, music labels, and businesses mean that the web will become smarter–much smarter. Search will continue to evolve in ways that relatively few of us appreciate or think about.

Understanding Why

And let’s not forget about data mining and business intelligence. Forget about knowing more about who buys which books, although this is of enormous importance. (Ask Jeff Bezos.) Think about knowing whythese books or CDs or movies sell–or, perhaps more important, don’t sell. Consider the following questions and answers:

• Are historical novels too long for the “average” reader? We’ll come closer to knowing because metadata includes page and word counts.
• Which book designs result in more conversions? Are there specific fonts that readers find more appealing than others?
• Are certain keywords registering more with a niche group of readers? We’ll know because tools will allow us to perform content and sentiment analysis.
• Which authors’ books resonate with which readers? Executives at companies like Amazon and Apple must be frothing at the mouth here.
• Which customers considered buying a book but ultimately did not? Why did they opt not to click the buy button?

I could go on but you get my drift. Metadata and the semantic web collectively mean that no longer will we have to look at a single book sale as a discrete event. We’ll be able to know so much more about who buys what and why. Ditto cars, MP3s, jelly beans, DVDs, and just about any other product out there.

Simon Says

In the next ten years, we still may not be able to answer every commerce-related question–or any question in its entirety. However, a more semantic web means that a significant portion of the mystery behind the purchase will be revealed. Every day, we get a little closer to a better, more semantic web.

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What say you?

As the author of four books, I pay close attention to the publishing world, especially with respect to its use of emerging technologies. Brass tacks: I often doubt wonder if traditional publishers have truly embraced the Information Age. While I understand the resistance, can’t data analysis and business intelligence help publishing houses improve their batting averages (read: select more successful books, reach their readers better, and avoid expensive mishaps)?

These are just some of the issues broached at O’Reilly’s Tools of Change for Publishing conference. This annual event bills itself as the place in which “the publishing and tech industries converge, as practitioners and executives from both camps share what they’ve learned from their successes and failures, explore ideas, and join together to navigate publishing’s ongoing transformation.” From a recent article reflecting upon TOC 2012:

If one thing was clear from this year’s TOC it’s that the publishing business is finally getting serious about data and analytics. This can mean looking at the granularity of day-to-day marketing strategies — such as when to send out a tweet to get maximum re-tweets on your social network (there’s an app for that), making quantitative assessments of the number of books a “library power patron” will buy based on their reading habits (one bought for every two borrowed), or the likelihood of a German to feel bad about downloading a pirated e-book from BitTorrent (not so much).

Years ago, most publishers selected books exclusively upon the recommendations of acquisition editors. AEs have been the gatekeepers, those coveted folks who somehow knew which books would be successful.

Except many of them didn’t.

Bad Batting Averages

In fact, the number of misses by big publishers is pretty astounding. Stephen King received hundreds of scathing rejection letters before he proved himself a book-selling machine. Publishers passed on initial manuscripts of Chicken Soup for the Soul, a franchise that has reached tens of millions of people. John Grisham self-published his first book.

I could go on but you get my point: relying upon hunches and intuition isn’t exactly a recipe for successful decisions, and book sales are no exception to this rule. Slowly, publishes are recognizing this fact and embracing analytics and Big Data. They have to; their margins are being squeezed and they have no choice but to adapt or die. While developing the perfect equation to predict book sales may be impossible (there are always Black Swans), no doubt publishers can benefit from a more information-driven approach to managing their business. After all, it worked for the Oakland A’s, right?

Simon Says: It’s Not Just About Previous Book Sales

Individual judgment will always matter in evaluating any business opportunity. No one is saying that machines, data, and algorithms need to completely supplant the need for human intervention. The data may tell us what, but it may not tell us why? Plus, there are always times in which it makes sense to bet big the other way–to ignore the data. Maybe 20 years ago, an author who sold 20,000 copies of a book might sell more or less the same number. These days, however, publishers are using new and often fuzzier metrics like an author’s (or prospective author’s)

• Twitter followers
• site’s Google PageRank or Alexa ranking
• RSS subscribers
• size of mailing list
• number of Facebook fans
• Klout score
• and others

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What say you?