The Coronation of Predictive Analytics: A Four-Year Retrospective
The highlights from The Burtch Works Study: Salaries of Predictive Analytics Professionals 2016, which examines updated compensation and demographic data on over 1,200 analytics professionals across the US.
This post is an adapted excerpt from our report, The Burtch Works Study: Salaries of Predictive Analytics Professionals 2016, which examines updated compensation and demographic data on over 1,200 analytics professionals across the US. You can download the report for free, and see how analytics compensation varies by job level, region, industry, residency status, and gender, plus more fascinating insights.
The transition that has occurred in the market for predictive analytics professionals (PAPs) between 2013, when we published our first Burtch Works Study, and 2016, has influenced some of the most notable business trends over the past decade. There are several major shifts that we have watched develop, including the widespread adoption of data-driven initiatives, more professionals entering the analytics market, and a blending of predictive analytics and data science, all of which are shaping the business landscape and will continue to do so over the coming years.
Chief among these major trends that have developed in the so-called “Big Data” space over the past few years is the extensive proliferation of quantitative initiatives. As we’ve pointed out in previous reports, predictive analytics teams are cropping up everywhere, including legacy corporations, growing startups, government teams, and non-profits, from Silicon Valley to Wall Street.
Earlier this year, Burtch Works and Forrester fielded a joint survey to senior analytics leaders to gauge the state of customer analytics adoption across the US. And, as Forrester Research points out in their resulting State of Customer Analytics 2016 report, a greater proportion of analytics teams are now considered “leaders” (as opposed to followers or laggards) when compared to the 2014 report – the average business now uses at least six data sources vs. four sources two years ago. However, it is interesting to note that the proportion of basic analytics teams that Forrester refers to as “laggards” has remained almost the same, showing that there are still many ground-floor challenges associated with growing an analytics capability that must be addressed.
Perhaps the trend that has been the most readily visible to us as quantitative recruiters is the influx of professionals entering the analytics market, whether from one of the new Predictive Analytics Master’s programs or from another career path. Educational opportunities are diversifying due to the availability of more Master’s programs, in-house training, bootcamps, and online learning options, which have become quite popular due to the overwhelming number of job opportunities available in analytics.
Forrester’s data seem to point to an influx also, as only 26% of analytics leaders reported that finding predictive analytics talent is one of their top three challenges this year, down from to 35% in 2014. A caveat to this increase in the talent supply is that many of these are inexperienced professionals, and so the boost is primarily at the very junior end – professionals with three or less years’ experience. The supply of PAPs with four or more years’ experience remains challenged.
As we pointed out last year, the distinction between data scientists and other predictive analytics professionals continues to blur. Put simply, we distinguish between the two because data scientists, by our definition, manage unstructured data or continuously streaming data, using computer science skills that are uncommon for traditional predictive analytics professionals, who manage structured data.
Data scientists are a subset of PAPs who have the computer science skills necessary to acquire and clean or transform unstructured or continuously streaming data, regardless of its format, size, or source. Unstructured data may include: video streams, audio data, social media web scrapes, sensor data, raw log files, or long blocks of written language. For information about data scientists and their compensation, you can download our latest data science salary study, published in April 2016. Our predictive analytics reports focus specifically on PAPs that work with structured data.
However, as time goes on, more traditional predictive analytics professionals are learning typical data science platforms and tools like Hadoop, Spark, or Python, and this blending of the two fields will likely continue. This year, our SAS, R, or Python flash survey of over 1,100 quantitative professionals marked the first year that SAS was not the dominant tool choice among analytics professionals, and the three-year trend points to open source options picking up steam.
Although we’re not quite there yet, we are heading for a future where predictive analytics is the norm, not the exception. Soon, “business as usual” across corporations will include quantitative experts embedded throughout the company, including the C-Suite.
Original. Reposted with permission.
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