I awoke bright and early Wednesday morning and engaged in my regular routine. After firing up the coffee maker, I started checking email, Twitter, Google Analytics, and Amazon. Why Amazon? I
compulsively like to know how my books are selling.
Occupational hazard, I suppose.
My second book, The Next Wave of Technologies, doesn’t get too many reviews. I won’t tee off on the publisher here, but let’s just say that the book was not priced at a reasonable number. That’s at least one reason that sales didn’t explode.
That’s all in the past and I’ve come to terms with it. Onward and upward, right? But I noticed a new [popup url=”https://www.philsimon.com/wp-content/uploads/2012/09/lastlicks.jpg”]review[/popup] from 8/28/2012 and it wasn’t particularly flattering.
People can improve enterprise and consumer data, give it context, and make it more relevant.
Now, I know the person who wrote this review, even though she cloaked her name with “Last Licks.” The details of our specific engagement aren’t terribly important, but let’s just say that her version of events isn’t remotely close to mine. Also, let me state unequivocally that I have no problems with critical reviews. I’ve left a few myself on products that didn’t satisfy me–and I’ve received some for my last book, The Age of the Platform. But these reviews were at least related to the product itself. By contrast, the review by “Last Licks” had nothing to do with the actual book. In fact, I sincerely doubt that she bought it, much less opened it.
Can Amazon Improve its Review of Reviews?
Now, I’ve praised Amazon many times before about its fascinating use of different technologies. Fascinating and perfect are two entirely different things, however. The review in question wasn’t really a review at all; it was a disgruntled ex-client’s indictment of my business and web design skills. In an ideal world, Amazon would use semantic technologies to (better) determine if a book review was truly about, well, the book itself.
And here’s where the social data police come in. The social data police help other potential buyers of the book understand that not all reviews are equal or even relevant. Not all one-star reviews are equally meritorious.
Just look at the work of the social data police in the first two comments and then the reviewer’s (j meisler’s) decidedly lame, inscrutable, and rambling response:
I’ve seen the same thing happen with other books and an author’s attempts to buy them. The community chimes in and generally calls out these type of “non-review reviews.”
Amazon has certainly benefitted by embracing advanced technology. But technology itself hasn’t yet (and may never) replace the social aspect of data. Brass tacks: people can improve enterprise data, give it context, and make it more relevant. When it comes to data, these are most certainly not bad cops.
Is your organization embracing the social data police?
What say you?
A version of this post originally ran on the Data Roundtable.