This morning, O'Reilly Media published the results of its 2016 Data Science Salary Survey. The report covers a wide set of topics such as salary differences by gender and countries as well as details for the types of skills that can give employees an edge when it comes to earnings. We tooked a closer look at the Business Intelligence answers and what we found out might surprise you.
The "Business Intelligence market for Big Data" has gotten a lot of coverage recently as acquisitions, consolidations and private equity news have made the front page of magazines. Just this summer, Thoma Bravo acquired Qliktech for $3B and mega vendors like Salesforce and Workday made acquisitions that some might call "game-changing" .
It is no surprise that, when Information Management decided to extract nuggets from the O'Reilly's survey, it decided to focus on Business Intelligence tools and the types of analytical skills Data Scientists should acquire in order to increase their earnings potential. Below our quick analysis. To get to the full report, go here.
- The Data Scientist title is not the best way to capture the most earnings. Unless you're in upper-management, it appears that Architects, Engineers, Developers, Principal/Leads and Managers make more money than Data Scientists.
- The industry with the highest salary reward potential is the Financial Industry. Education and Non-Profit come last. Disappointing but not completely surprising.
- California, The Pacific Northwest are the regions that reward Data Scientists the most. Surprisingly, the Midwest seems to have better upper potential than the Northeast or the Mid-Atlantic region.
- Finally, and perhaps the most surprising stat of all: the Microsoft BI stack tools (Excel, PowerPivot and Power BI) contribute the least to a Data Scientist's ability to increase their earnings potential.
Note that Tableau was not selected as a "BI Tool" but rather was put in the "Visualization category" so you can decide to interpret this as you wish. Nonetheless, please see below the "Salary by BI tool averages" (ironically displayed in a Google visualization) to see average salaries by Business Intelligence tools.
You can find the raw numbers for averages, high and low values in the below table (the source information can be found in this morning's Information Management article here)
Still confused about what the Business Intelligence space is about? Read our review of Gartner's latest Magic Quadrant here.
Looking to kick-start a BI on Big Data project and don't know what issues you might encounter? Watch our Do's and Don'ts BI of Big Data Webinar here
For everything else, contact us here!