Early in my career, I spent a great deal of time looking at and analyzing employee compensation data. Among my early discoveries: even the secretaries in Hawaii make a great deal of money. (The cost of living is quite high there, I'm told.)
While I've since moved on to other areas, a part of me will always be interested in what people make. To that end, I found Matt Asay's article "Big Data, Apple Driving Industry's Biggest Salaries" very interesting, not to mention the data behind it. The following table represents 2012 salaries on some top tech jobs:
(Fortran and PeopleCode made the list. Not exactly contemporary technologies, but who am I to judge.)
In Asay's words, "Enterprises are clearly willing to pay a premium to find or retain good people to help make sense of their data." Or, to quote a favorite Rush song of mine, "Big Money got a mighty voice."
Retaining data rock stars
Of course, the data above represent averages. Making $110,000 a year in San Francisco and Omaha are two very different things. And let's not forget that data scientists and rock stars can pretty much write their own tickets these days. The demand for skilled data professionals is much higher than its supply. New research by the McKinsey Global Institute (MGI) forecasts a 50 to 60 percent gap between the supply and demand of people with deep analytical talent.
There's no easy way to retain employees with hot skills like Hadoop and NoSQL. When I worked in corporate HR 17 years ago, certain employees' compa-ratios were off the charts. They made more than the theoretical maximums of their pay grades or bands. And there's nothing wrong with that.
And, in 1996, the available information on employee salaries wasn't nearly as prevalent as it is today. Want to find out how much you should be paid? It's not terribly difficult to find a proxy for your value on the external labor market.
Simon's tips for keeping big data rock stars
Good people typically aren't cheap. That sentiment is especially true today for good data people, and the data on good data people bear that out.
Don't be afraid to adjust internal compensation structures. Respond to a hot market or risk losing a raft of key employees. Beyond that, try to be proactive. Realize that you won't be able to keep everyone, but don't be afraid to counter employee offers with lucrative offers if one or more of the following conditions hold true:
- They are that valuable to you.
- Your organization can't find replacements.
- Your organization is in the middle of a key big data "project" (although I hate using that term to describe big data).
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