Tuesday, 21 of November of 2017

Category » Humor

Analytics Lessons from Penises, Professors & Prohibitions

A recent All Analytics post of mine, Analytics Lessons from Penises, Professors & Prohibitions, did not sit well with some of the professionals in LinkedIn’s Advanced Business Analytics, Data Mining and Predictive Modeling discussion group. Two members of that group made some rather strong comments about the piece.

I offered to address each of their comments here on my blog. Carey Butler accepted. His questions and my replies appear below. These are followed comments from Don Philip Faithful who said “Thank you for asking, Meta, although it isn’t really necessary to do so. In a public forum, I fully expect my comments to cause riots.” (Riots?) No riots here, I think, Don.

What follows is a heckuva long blog post.

The original article, Analytics Lessons from Penises, Professors & Prohibitions is here: http://bit.ly/alla022

The LinkedIn discussion is here (this requires an account) http://linkd.in/1eeoz22

Questions from Carey Butler:

I have a few questions. Couldn’t you find something better to write about? How is this “tough analytics”? How did this earn the label “research”? Isn’t this kind of article more social engineering than factual and informative information? Can we not elevate our “tough analytics” to something more important than the size of peoples organs? This article does more to disgust, than to edify. Did you notice the source article also entertains by citing its “Most Popular” with “Charles Manson and I Are Going To Get Married.”? Aren’t the issues you discuss far more important than the story you have chosen to frame them with? Do you not see that our collective focus is being brought down to base level with such “research”?

— Carey Butler

Carey,

Thank you for reading my latest article, and for asking questions. I’m happy to respond to each and every one.

Question 1: Couldn’t you find something better to write about?

I write about many aspects of data analysis. For example, I am coauthor of a how-to book for users of a popular data mining product and an upcoming academic book on Big Data analytics, and author of many articles (links to many of them can be found at http://bit.ly/metaarticles).

May I point out that the title of the article begins with “Analytics Lessons…”. I believe that these lessons are important, and that the cases mentioned illustrate the points effectively and in a memorable way. If you don’t agree, that’s OK, you’re not the first person ever to dislike something that I have written, and you won’t be the last. However I have other feedback that tells me many others enjoy and find it useful.

Question 2: How is this “tough analytics”?

Analytics challenges are not always about fancy math or data management. Tough, in these examples, refers to the difficulty of obtaining accurate and unbiased data. No amount fancy math or data management will correct the problem of having inadequate data.

Question 3: How did this earn the label “research”?

re•search [ri-surch, ree-surch] noun
1.diligent and systematic inquiry or investigation into a subject in order to discover or revise facts, theories, applications, etc.
Source: http://dictionary.reference.com/browse/research?s=t

Ansell conducted a systematic investigation in order to revise facts. They needed accurate, current data about penis size, so they collected it in a methodical manner. That, sir, is research.

Question 4: Isn’t this kind of article more social engineering than factual and informative information?

Are you speaking of the article that I wrote, or Floyd Elliott’s humor piece that I quoted, or Ansell’s research study? I’ll just cover all of these.

My article described a challenging research case and what it has in common with more mundane research. “Social engineering” http://en.wikipedia.org/wiki/Social_engineering_%28security%29, according to Wikipedia, is the use of trickery or fraud to obtain personal information. I did not encourage the use of trickery or fraud, did I? Not social engineering.

Floyd Elliot’s piece, “That’s Not Normal” is a work of humor. Or is it? On the surface, a light-hearted laugh, but beneath the surface, a thinly veiled deception with the real motive of driving men to discuss math! And look at the comments – they are about math! Could this be… social engineering? Oh wait, Floyd said clearly that men would discuss math if it had to do with their penises, and then provided an example. There was no deception, he was upfront about it. Not social engineering after all.

And how about Ansell? Did they trick men into unwittingly revealing private information? Hmmm, let’s think about this. They asked men in a bar to step into a tent to present their penises for measurement. It’s true that we don’t know exactly what was said. And the men had probably been drinking. Yet it is hard to imagine the man who expected that a gloved technician approaching his genitalia with a ruler had some other purpose in mind. Once more, not social engineering.

Question 5: Can we not elevate our “tough analytics” to something more important than the size of peoples [sic] organs?

The analytics community devotes a lot of energy to understanding what gets people to click on links and purchase items they do not need. Tremendous resources are poured into tracking trivial digital interactions. Netflix offered a $1million prize for a model to predict what movies people will like and I never once heard any data analyst complain that the problem was unimportant.

Ansell makes condoms. These modest pieces of latex enable millions of people to protect their health and life, and to have control over their own reproductive destinies. These are very important matters, Carey, and I find them all worthwhile.

If, Carey, you would like to open a discussion about something you find more important than protecting health, life and reproductive destiny, please do so. I will not stand in your way.

One last thing – we’re adults here, so let’s call a penis a penis, not an organ.

Question 6: This article does more to disgust, than to edify. Did you notice the source article also entertains by citing its “Most Popular” with “Charles Manson and I Are Going To Get Married.”?

No, Carey, I did not notice that. However, I would not have been surprised to see that, given that the piece appeared in the comedy section of the Huffington Post.

What I did notice was Floyd Elliot’s remarkable mix of serious math with lighthearted presentation, and the relevance of those things for my own work.

Question 7: Aren’t the issues you discuss far more important than the story you have chosen to frame them with?

Maybe so.

The issues are very important. Yet for readers to learn my position on the issues, I must first have their attention.

Take you, for example. You are so offended by what I have written that you felt moved to respond, and sharply. Yet you looked at not only my article, but an article that was referenced within it.

Question 8: Do you not see that our collective focus is being brought down to base level with such “research”?

No, Carey, I do not see that.

The research is serious, but people are silly. I see that people will focus on challenging topics, if there’s something in it for them. If that something is a giggle, it’s OK with me.

And now, some thoughts from Don Philip Faithful:

Like Carey, I think you could have used a more meaningful example to make your points. However, since you have chosen this specific research to make your case, I guess it is fair game for me to comment on the research itself. I’m trying to determine the usefulness of the data since the research seems to contribute to a certain level of intellectual masturbation. Nazi scientists collected all sorts of phenotypical data, drawing inferences regarding racial intelligence and fitness. If it is necessary to accept the research to legitimize your points, then in short you have failed to substantiate your case. For instance, we should not just follow the rules of law but also the rules of social conduct, making your first point superfluous. At the same time, I’m not questioning your points, just their applicability and usefulness beyond the measurement of genitalia.

Dear Don,

Since I have already discussed the meaning and usefulness of Ansell’s research above, let me address some of the unique aspects of your commentary.

Item 1: the research seems to contribute to a certain level of intellectual masturbation

Don, bro, have you looked at some of the other online discussions among data analysts. In one group, I saw a discussion that went on for pages and pages over whether statistics is math. That’s a fine example of unproductive use of intellect.

Ansell conducted practical research to support manufacturing.

Item 2: Nazi scientists collected all sorts of phenotypical data, drawing inferences regarding racial intelligence and fitness.

Every doctor on planet Earth collects data about each and every person who walks in the door, and draws inferences from it. Teachers also collect data. So do all sorts of people in all sorts of professions with all sorts of motives. Yet you have chosen to mention the Nazis.

As a Jewish woman whose family members actually faced Nazis, I am somewhat opinionated about your choice of words. Likening people or work you don’t like to Nazis on the thinnest of premises is disrespectful, Don. And it’s a pretty weak argument as well – measuring people is a routine activity. Measuring doesn’t make one a Nazi, nor anything similar.

Item 3: If it is necessary to accept the research to legitimize your points, then in short you have failed to substantiate your case.

Hmmm. The research is there to illustrate points, not really to legitimize them. How much evidence do you need, for example, to accept the point that you should know the laws regarding your research and obey them?

And there’s nothing superfluous about reminding people to know and obey the law.

Item 5: I’m not questioning your points, just their applicability and usefulness beyond the measurement of genitalia.

Hoo boy, Don. They were pretty everyday data collection guidelines. If issues like knowing the law and monitoring staff don’t strike you as applicable to cases other than the three mentioned in “Analytics Lessons from Penises, Professors & Prohibitions” http://bit.ly/alla022, you have a pretty unconventional viewpoint on data collection.


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Text Analytics Is Hard (That’s What She Said)

New piece on Smart Data Collective: Text Analytics Is Hard (That’s What She Said)

The other day I was hit with a new one – for me, at least. The question was – how would you write a classifier to identify sentences appropriate for the retort, “that’s what she said”? It turns out that identification of “that’s what she said” jokes in the making is rather popular among linguists. Go figure.

An academic study of a not-at-all businesslike text analytics application has something to teach us in the business community.


Men and women want mostly the same stuff

This is hilarious – a summary of research on the wants of men and women, as expressed in social media, from Netbase. We have a lot more similarities than differences! You must read this for yourself, but here’s a hint – we all want food!

BTW, I tweeted about this a few days ago, just once, and was stunned when I noticed that my tracking link was drawing hundreds of clicks. Turns out that the tweet got picked up by msn now. I would never have known if I hadn’t tracked. The moral: use tracking links and you may learn something!


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Big Data movie quotes

Some days you just cannot take work seriously. Apparently that’s what happened last week for J. Graeme Noseworthy (@graemeknows). He sent out this little tweet:

It’s time for some #bigdata Friday fun. Today, I’m going to modify classic movie quotes. “I love the smell of big data in the morning.”

And with that for a start, he proceeded to churn out dozens of perversions of great movie quotes as only someone who has spent way too much time talking up data appliances could. Midway through, I sent him a little comment, and he replied “@metabrown312 I’m literally sitting in my office laughing out loud. People keep walking by and looking at me like I’m crazy… which, I am.” That’s what this line of work will do to you!

You can search twitter for #bigdatamoviequotes, or enjoy the convenience of reading them here (reprinted with permission from the author):

“Back off man, I’m a data scientist.”

“Tell me something, my friend. You ever dance with your data in the pale moonlight?”

“Don’t ask me about my data, Kay”

“Louis, I think this is the beginning of a beautiful database”

“Big Data moves pretty fast. If you don’t speed up your queries once in a while, you could miss it.”

“The greatest thing you’ll ever learn is just to analyze and be analyzed in return.”

“Discovered by the Germans in 1904, they named it San Diego, which of course in German means ‘big data'”
“The greatest trick the data ever pulled was convincing the world it didn’t matter.”

“Big data, big data, talk about analytics, my girls got ‘em.”

“Hokey infrastructure and ancient software are no match for a good data warehouse appliance by your side, kid.”

“Gentlemen, you can’t fight in here! This is the data warehouse!”

“To crunch your data, see them analyzed before you, and to hear the lamentation of the executives.”
”Analyze or analyze not, there is no try.”

“Are you telling me you built a data model… out of a Delorean?”

“I’m as busy as hell, and I’m not going to analyze this anymore!”

“Frankly, my data, I don’t give a query.”

“Cinderella story. Outta nowhere. A former Oracle admin, now, about to become an IBM Fellow. It looks like a mirac…it’s in the memory!”

Striker: “Surely you can’t be optimized.” Rumack: “I am optimized…and don’t call me Shirley.”

“Mrs. Robinson, you’re trying to analyze data. Aren’t you?”

“As IBM is my witness, I’ll never be unstructured again.”

“You’ve got to ask yourself one question: ‘Do I feel optimized?’ Well, do ya, data?”

“All right, Mr. DeMille, I’m ready for my analytics.”

“I’ll get you, my pretty, and your Big Data too!”

“Nobody puts Big Data in a corner.”

“Carpe notitia. Seize the data, boys. Make your queries extraordinary.”

“Of all the data warehouses in all the companies in all the world, she plugs into mine.”

“Elementary, my dear IBM Watson.”

“Keep your data close, but your analytics closer.”

“You had me at ‘big data.'”

“Mama always said life was like a data warehouse appliance. You always know what queries you’re gonna optimize.”

“Houston, we have a query.”

“You’re gonna need a bigger database.”

“You know how to analyze big data, don’t you, Steve? You just plug into Netezza and go.” “Show me the analytics!”

“There’s no place like Netezza.”

“E.T. L. phone home.”

“Love means never having to wait for analytics.”

“Toto, I’ve got a feeling we’re not in-memory anymore.”

“May the analytics be with you.”

“Data. Big Data.”

“Here’s looking at you, queries.”

“A census taker once tried to test me. I analyzed his data with some fava beans and a nice Chianti.”

“What we’ve got here is big data to analyze.”

“I’m going to make him a database he can’t refuse.”


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