A new survey out from O’Reilly looks at how much money do successful data scientists or analysts make, and which tools increase their salaries most. The survey pool comes from attendees at the Strata Conferences held on both coasts and focusing on big data and data scientists.
Big data has become the latest buzz word floating around the C-suites and marketing offices of public companies looking for extended consumer reach as well as Big IT shops looking for extended government contracts. Who are the knowledge workers making up Big Data? The industry is still relatively new and includes both self-taught workers and newly minted data scientists that rely on an expanding ecosystem of largely open source data tools to extract insight from large pools of information.
Everyone, we are told, from the police to beverage companies are using big data to understand who we are and what we might do. Virginia is leveraging its universities to build on big data research for a variety of applications.
The report takes a look at the correlations between those tools and what people in these professions are being paid. Some of the trends will be obvious to the Sand Hill Road set, but they also underline trouble ahead for public sector IT shops hoping to harness the most for their Big Data dollar. In terms of pay, report data shows that early startups are paying the most for top talent, a trend which report author John King notes isn’t all that surprising. “When I first looked at the data, the fact that startups were paying more struck me, but after talking to people about it, and looking closer we see that these startups that are well funded are willing to pay for top talent to get ahead. It’s not just starry eyed recent grads anymore like the stereotype says,” he tells CivSource.
Early startups had the highest median salaries of approximately $130k. Public and private companies were in the ballpark of $110-100k, while public sector and education reported median salaries of approximately $80k. While not surprising that public sector falls on the low end of the pay scale, it can mean that state and local IT shops will continue to see positions go unfilled as they are less able to compete against the better pay packages of the private sector.
Notably, the number of big data tools someone uses is also correlated to their pay – think Hadoop, MongoDB, Oracle, SAS, etc. For workers that are proficient in a greater number of the open source big data tools, especially the newer ones, salaries increased accordingly. For others, like those more familiar with closed systems like SAS, salaries were more likely to come in on the low end. These tools are more established making them less desirable, but they are also less likely to be operable with open source options in greater demand. This trend also speaks to silos already starting to form in big data.
“One of the more fascinating components of this survey to me, was the way different tools were represented. SAS has a big user community, but I think they are more likely to go the SAS conferences, and the tool isn’t open source. So SAS users were a smaller portion of the data set, but it also speaks to how those tools can impact what workers do,” King says.
“I think big data means a lot of different things to different people. I can drive around and see commercials for big data, and only a fraction of the people who see and hear those advertisements are going to know what it means. It’s also true for workers themselves, you can work in big data without being a data scientist. I think that’s the big takeaway here, don’t be afraid to learn how to code or learn how to use these tools. Learn more than one, it can mean a better career path and it’s really not that difficult.”
