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According to the recent landmark McKinsey Big Data study, by 2018 one in two US jobs requiring deep analytical skills will go unfilled, and there will also be an under-served demand for 1.5 million business managers and analysts “who can ask the right questions and consume the results of the analysis of big data effectively.”

McKinsey and others see it simply and singly as a labor shortage problem similar, say, to that in nursing and other boomer care professions.

It is taken for granted in those cases that workers will be consistently productive and effective - thanks to the well-honed protocols and processes within which they operate, and the task-oriented nature of the work.  Within the context of a given employer, each worker of a given type can be expected to produce a roughly equal amount of work, and that amount will be as much as the system can enable or will allow.

Does that sound like Analytics?!

Analytics requires creative problem-solving and unguided discovery. Unlike nursing or welding, the tools, techniques, and technologies used are not highly consistent or necessarily efficient.

Two analysts with similar education, experience, and talent working on similar problems can produce radically different results in terms of speed, depth and accuracy, depending on the tools they have been given to do their jobs.

Furthermore, most analysts are highly dependent on a host of similarly variable IT resources for data acquisition and query processing, which can further impact their productivity.

The analytical skills shortage is real, but it may be overblown because most accounts of it don’t take relative productivity into consideration.  There could be far fewer unfilled analyst jobs if analysts could become more productive.

Said differently, though, the analytical skills shortage is real and it is underestimated because the productivity tax levied by many technologies and tools on which analytics currently depend is going up, not down.

From ETL scripting and SQL to Hadoop and R programming, analytics is rife with impediments to analyst productivity.  More coding and debugging means less modeling and analyzing, does it not?

Negative joblessness notwithstanding, “Full employment through inefficiency!” is hardly a clarion call for analytical dragon slayers.

Is poor, or even declining analytical productivity a problem in your company?  What are you doing to solve it?

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Comment by Bill Luker Jr on December 29, 2011 at 1:23pm

Spot on, Vincent.  My take is that IT-dominated thinking about analytics is the major barrier to making analytics a go-to discipline for business and government executives.  Forget programming.  Whatever happened to thinking, understanding, and knowing?  Or seeing the forest AND the trees?

 

Bill Luker Jr

Comment by Tim Negris on December 20, 2011 at 9:04am

I agree with you both.  To Richard's point, I routinely talk to analysts who are frustrated by the amount of time they must spend in iterative process with IT just to get the data they need to analyze.  And, to Vincent's, nearly as often I talk to business executives who are concerned by the long time to value they see in tools that are very labor intensive and have a steep learning curve.  

Comment by Richard Hren on December 19, 2011 at 1:33pm

I agree with Vincent. There is a profound danger in confuding the role of an analyst with that of a technology enabler of analysis. The roles of programmer, DBA, data extractor and storer can certainly aid in the productity equation. These folks help to provide the raw material which the analyst then "spins into gold". and frankly there is a shortage of both types or people.

Comment by Vincent Granville on December 19, 2011 at 12:35pm

"From ETL scripting and SQL to Hadoop and R programming": Analytics is much more than that, the data science aspect of it is also about conceiving, designing (e.g. algorithms, efficient dashboards), solving, optimizing, modeling - all tasks that do not require programming skills. Good judgment, business acumen, great detective skills are more important than SQL, R, SAS, Hadoop etc. particularly for senior professionals.

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