(This article was originally published at Big Data, Plainly Spoken (aka Numbers Rule Your World), and syndicated at StatsBlogs.)
Today, I have the honor of interviewing Avinash Kaushik, author of the bible known as Web Analytics 2.0, and a digital marketing evangelist at Google. He also has a must-read blog called Occam's Razor. Occam's Razor is a principle championed by statisticians that can be summarized as "as simple as possible but not too simple". It is a principle and therefore it also draws controversy from some quarters. Kaushik's blog is a place where you can pick up Numbersense. He writes about Web data, which is a huge part of Big Data.
***
KF: How did you pick up your impressive statistical reasoning skills?
AK:
I'm
a Mechanical Engineer with a MBA in Finance and Marketing. Somewhere
along the way, data, analysis, statistical reasoning became an integral
part of my life. :) Beyond what our traditional education blesses us
with... using new analytical software has been an opportunity to refresh
old concepts and learn new ones. I'm also partial to books. I'm
attaching a photo of one that's on my desk right now!
I've also come to believe that application of cool,
sexy, statistical techniques yields poor results not because someone
was unfamiliar with them, rather because they don't quite understand
the underlying business drivers, data collection mechanisms, impact of
the business culture, the people involved and other "soft factors."
I've attempted to invest an enormous amount of time in all of those
areas to improve the impact of my analytical efforts.
***
KF: What are your pet peeves with published data interpretations?
AK: Ignorance of the simplest rules.
I get mad, still, when I see the classic correlation = causation.
My expectation was that after that problem has been made fun of in
Dilbert comics, we've reached a level where no one would fall for this
trap. And yet, every day!
Not stating the assumptions clearly or, worse, not
understanding their impact is another cardinal sin that I've come to
dislike a lot. Assumptions are everything! Dig, dig, dig, find them and
understand their impact on the data you are looking at.
Perhaps my favourite is the utter unwillingness to
actually think about the data in front of them. For example, a
prestigious digital company shared the other day that "67% of US buyers
check the social profile of the company before they buy the product."
Now that is complete crap. You just have to think about all the people
making purchases, or yourself, and how often do they/you actually check a
company's social presence before making the purchase? Yet this fact was
provided with a pretty infographic, and reposted on many "popular
tech/social blogs." So few people bring their nose close to the data to
check if it smells. It is distressing.
***
KF: Which sources do you turn to for reliable data analysis?
AK:
(This
is 100% genuine, and not an attempt to play to the gallery...) For
years and years the posts on this blog, Junk Charts, and the comments
have been a valuable source of learning for me.
I'm a big fan of the Guardian's Data Store (
link).
Such a great collection of data visualizations, types of data, thought provoking analysis.
I also use information aesthetics (
link),
as a gateway to discovering new sources.
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Tags: Business, Causation creep, Cause-effect, Current Affairs, data, Errors, Numbersense Pros, science, Significance, Statisticians, Weblogs
