Home > Media Mentions > Step One in Making Big Data Work For You: Think Small

Step One in Making Big Data Work For You: Think Small

Published on http://www.digitalbookworld.com/.

Life is full of little contradictions, hyperbole, and oxymoron (especially in the blogosphere). How we treat (or, often, miss-treat) sales data and consumer information in this industry is no exception. Having served as a publisher for ten years, I would be the first to incriminate myself.

Too often we extol the need for more analytics and actionable information and place a high value on relatively non-actionable data like best-seller lists, while at the same time blatantly disregarding the richer, more actionable data sources (and herein lies the contradiction). When it comes to digging in and using data for deeper levels of understanding we often are overwhelmed and take the easy way out, claiming that our superior knowledge of the industry (a.k.a. the “Golden Gut”) gives us liberty to make bold decisions without supporting evidence.

We’ve got to fix our internal wiring as an industry. Let me explain.


To be fair, there is a place for hunches and book experience – after all, no consumer study or BookScan report could have predicted the success of Harry Potter – especially after J.K. was allegedly turned down by several publishers with her cute story before it found a home. (But perhaps if the previous publishers who turned her down were able to look at some data and see that certain consumers were drawn to these stories, they might have been able to cash in.)

The point is that data should not be used to make the decision – rather, it should be used to inform the best possible decision. For our industry, decision-making should be a balance between art and science. Too often, though, I see the scales tipped toward the art of publishing and at times a distrust in data or a dismissal of consumer information. I have a hard time absorbing the rationale — as if a hunch were more scientific.

What passes for research in the area of editorial/acquisitions is a quick scan for comp titles in Amazon before running off to a pub board meeting.

Lest marketing and sales feel left out, I often see a big disregard for doing the hard research work there as well. We often play to an authors wishes or trust inertia on where we place our marketing bets as opposed to looking at the sales and consumer data to get a real understanding of who is buying the book and what triggers the sale for them. In sales departments it tends to be no different: We arm ourselves with all kinds of lovely book information about a title and a vague sense of what the buyer is looking for only to be shocked when the returns truck shows up full. Perhaps if we came in better armed with research on who the book shopper was and what they were looking for, the sell-in would be easier and the success higher.

So what does this all have to do with Big Data and starting small?

My strong suggestion is to think small if you want to embrace big data (the oxymoron).

What I most often consult publishers to do (or anyone in the industry for that matter), is to first take an entire inventory of all of the data assets you own or subscribe to and then choose five data points you will commit to “wire into your company’s DNA” (as Hachette CEO David Young once told me).

That’s right: start small with just five data points.

Whatever you choose should help you either substantiate an acquisitions ‘hunch,’ justify a marketing spend, or validate a selling decision. It must also be agreed that as company policy you will not make a decision without this data. (If you would like some examples of data points you should be looking at, drop me a line at Kelly.Gallagher@Bowker.com and I will send them to you.) Once you start using these data points – you can begin to layer more data into the process.  At the end of the day every report or use of data – big or small – must have a rationale for why you are using it and how to measure the impact.

View full story.

 

 

This entry was posted in Media Mentions and tagged , , , , . Bookmark the permalink.

Comments are closed.