Statistician Eclipse? No, Disintermediation Is Now Part of Data Space

July 22, 2015

The fact is that many folks who are managers responsible for profits and losses can add. Some can do basic manipulations of data in their head; for example, “What is my commission on a sale of 10,000 shares of GE?”

But the vast majority of the folks I encountered before I retired struggled with mathematics. The fact that physics, bioengineering, even civil engineering depends on math is knowledge that sits on the sidelines in the race to pay the mortgage.

The folks who are able to use math to solve problems and earn a living are in the majority at outfits like Halliburton, Google, Diffeo, and other companies with less dependency on MBAs, marketers, lawyers, and the other “soft” disciplines that constitute the majority of an organization’s talent pool.

Enter statistics. Now the notion of Big Data, like the silliness about cognitive computing and semantics, is a token, a mental shortcut, a bit of jargon. Why worry about what is required to make sense of data, whether big or little? Why concern oneself with the challenges of determining a proper local diffeomorphism between manifolds?

I read “The Risky Eclipse of Statisticians.” I liked the article. I recommend it.

The main point is that statisticians are being marginalized by data scientists. I am not sure what a data scientist is. I am sure what a statistician is. I had a relative who was pretty good with statistics. He (VI Arnold) worked for another guy {Kolmogorov) who also was good with statistics.

The data scientist thing sounds a little too New Age for me.

I did note this passage in the write up:

What speaks even louder volumes is that statisticians are often left out of some of the biggest national discussions happening around Big Data today. For instance, UC Berkeley’s Terry Speed observes:

US National Science Foundation invited 100 experts to talk about Big Data in 2012. Total number of statisticians present? 0.

The US Department of Health and Human Services has a 17-person Big Data committee. Total number of statisticians? You guessed it…0.

Justin Strauss, co-founder at Storyhackers, who previously led data science programs in the healthcare industry, can attest to this more generally. He says he has “seen an underrepresentation” of statisticians at conferences and other events related to Big Data. But statistics is the foundation of understanding Big Data. This was supposed to be their decade–their time to shine in the limelight. So, what changed? As renowned statistician Gerry Hahn once said:

“This is a Golden Age of statistics, but not necessarily for statisticians.”

The article explains the gap.

For me, however, the write up does not drive home a point which I think is important.

Statistics and the practice thereof is not speedy, necessarily intuitive, and not all that easy, even for mathematically gifted folks.

Consequently short cuts are needed to get over the skills gap and around the bellyachers who suggest that bad data will get worse with bad analysis and lead to the probability of really bad decisions. I bet you can think of a few real bad decisions in your own company, can’t you, gentle reader. Here’s a hint: The probability of the junk bond market working like the model predicted.

The push for point and click data analysis systems is a response to a dearth of people who can “do” statistics without taking short cuts. Some companies are trying to bake in safeguards so the system user does not generate a real or figurative train wreck.

I am supportive of multi dimensional teams. On the team should be individuals who are data integrity savvy. There should be people who know the business and the competition. And, in my opinion, a real live statistician should be involved. She needs a computer, a mobile phone, and a cattle prod. Zap the strays who don’t know the systems and methods appropriate for the question at hand. Zap. Zap.

Stephen E Arnold, July 22, 2015


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