This post is an excerpt from our upcoming report, The Burtch Works Study: Salaries of Predictive Analytics Professionals 2015, which examines updated compensation and demographic data on over 1,700 analytics professionals across the US. The full report is available for free download here.
Continuing a trend, the use of predictive analytics continues to grow. Investments in data-driven decision making have become ubiquitous – they are being made by organizations in business, government, law, entertainment, non-profits, education, and more. As Burtch Works predicted at the beginning of the year, legacy corporations, sometimes less nimble and more process-bound than startups, are getting on board, to remain competitive with smaller, faster-moving firms in the marketplace. Innovative data analytics teams are everywhere, productively using predictive analytics.
Although it is not new that companies wish to use their data to make better business decisions, this is now imperative and analytics as a discipline is maturing. With data sources becoming more numerous and complex, and with increasing amounts of unstructured data, we’re seeing more analytics professionals acquire the skills and tools necessary to manage these data. Predictive analytics professionals themselves are evolving, and the delineation between data scientists and other predictive analytics professionals is getting fuzzier by the day.
At Burtch Works, we have historically defined predictive analytics professionals as those who can “apply sophisticated quantitative skills to data describing transactions, interactions, or other behaviors of people to derive insights and prescribe actions”. Predictive analytics professionals are distinguished from business intelligence professionals or financial analysts by the enormous quantity of data with which they work, well beyond what can be managed in Excel.
That definition also encompasses data scientists, however, who are distinguished from other predictive analytics professionals by their ability to work with unstructured data, resulting in different compensation. Data scientists are a subset of predictive analytics professionals 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 data, audio data, social media web scrapes, sensor data, raw log files, or long blocks of human language.
So what’s next? This focus on being data-driven, and the way it distinguishes the leaders from the laggards, has moved analytics professionals to the forefront when critical corporate decisions must be made. Analytics professionals are poised to take leadership positions in global corporations within the next 10 to 20 years. Some of the so-called “digital natives”, where data has always been an integral part of the product, are already headed up by quantitative pros, but even for those firms that are just getting on board with the data movement, this transition to analytical leaders is imminent.
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