Salaries are on the rise for IT professionals, increasing anywhere from 3% to 7%, according to the latest surveys. For big data professionals, however, the news on compensation is even better. In a DICE study, IT pros adept at big data-related languages, databases and skills (like R, NoSQL, MapReduce and Hadoop) are getting premium pay, accounting for nine of the top 10 paying jobs.
This finding is borne out by Burtch Works, whose study in April found big data professionals earned $90,000 in median base pay and even more ($145,000) if they were managers. Data scientists (defined by BurtchWorks as professionals who also work with unstructured data) were paid even more, with individual contributors earning $120,000 in median base pay and managers claiming $160,000.
With such a promising outlook, the question that many in IT must be asking is how they can prosper from the surging interest in big data and analytics. Are jobs in big data and data science just for people with advanced degrees in math and statistics? Is there a way to move into a big data role from, say, an Oracle administrator position or Java developer?
The answer to both of those questions, is “maybe.” Here are five considerations when trying to answer those questions for yourself.
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There is more than one path into data science and big data, as the profession encompasses a range of functions and specialties. According to a study by Greta Roberts, CEO of Talent Analtyics, there are 11 basic tasks that get performed within the data science workflow:
- Analytics design
- Data acquisition and collection
- Data preparation
- Data analytics
- Data mining
- Visualization
- Programming
- Interpretation
- Presentation
- Administration
- Managing other analytics professionals
Professionals who perform these tasks, Roberts says, can be categorized into four groups: data preparation analysts, analytics programmers, analytics managers and generalists. While all four groups perform the whole range of functions, they also tend to specialize in a subset. For example, data preparation analysts spend significant time gathering and preparing data; analytics programmers spend about a third of their time writing code; and analytics managers spend half their time managing the team. Only the “generalist” category reports no particular specialty.
While functions like statistical modeling, algorithm development and data modeling are a long way from classical IT work, says Linda Burtch, managing director of Burtch Works, several IT functions are crucial to the success of any big data and data science initiative. One is operationalizing the integration of data from disparate systems.
“No one is more specialized to do that than IT,” Roberts says. “The infrastructure needs to be built to keep data updated and operationalized. That’s a critical part of the data scientist role that IT could go for.”
Another is security. Once data silos are broken down, so is access control, Roberts says. “When the data is integrated, how do you handle security? There’s a natural place that IT could move into and even sneak into other parts of the workflow from there.”
Programming is another “fabulous entre” into the analytics workflow, Roberts says, especially for people willing to learn languages like R and Python. IT professionals who work in business intelligence functions are also close enough to analytics that they may be able to make the leap, according to Burtch.
IT professionals might also be a natural for organizing and storing unstructured data in new types of databases like Hadoop, Burtch says. Currently, this function often falls to data scientists, but Burtch says it might increasingly move into the IT world. “They need to know what the endgame is going to look like in order to do this effectively,” she says.
Jack Levis, director of process management at UPS, says IT is crucial when it comes to gathering and preparing data, transparently delivering analytics insights to the people who need them, and creating the infrastructure that can scale to the level that the business intends for its analytics initiative to ultimately go. “I couldn’t do what I do without IT,” he says. “I need our worldclass IT people to accomplish what we’ve done. But the role is a support role vs. doing the analytics themselves.”
While data science and big data professionals clearly need particular skills, natural aptitudes are even more important, according to Roberts. For instance, all analytics professionals tend to have two distinct traits: very strong intellectual curiosity and a drive to create out-of-the-box solutions, she says.
Further, once you burrow into each of the four categories that Roberts defines, data preparation analysts tend to have a strong aptitude for detail and avoiding errors and mistakes, and they are not politically motivated. Analytics programmers are similarly uninterested in climbing the corporate hierarchy, and they tend to be collaborative workers, as they need to gain alignment on their work. Analytics managers are more assertive, competitive in their career goals and team-oriented. Generalists are a hybrid of all these traits.
There are plenty of ways for IT professionals to pick up big data skills, whether it’s taking a class in entry-level statistics or enrolling in an online data science course. Coursework ranges from a full master’s degree at a well-known academic institution, such as University of California at Berkeley or North Carolina State University; to a certificate from a university (like Oklahoma State University), vendor (like SAS) or industry group (like The Institute for Operations Research and the Management Sciences); to individual coursework at an online provider, such as Cloudera, Coursera or Udacity.
Many people simply learn by engaging in competitions like Kaggle or the Heritage Health Prize Competition, Burtch says. “Many of these people don’t even have a background in math or statistics,” she says. “They come from all walks of life – they just want to stretch their knowledge and learn.” Such informal training opportunities are a good way to take your career in whatever direction you want to, without having to jump into a formal degree program, she says.
According to Roberts, it’s less important to show which skills you’ve acquired than to demonstrate self-motivation and an eagerness to learn. In her interviews with more than a dozen top hiring managers, Roberts says, the No.1 mistake they named was hiring solely on the basis of skills. “That bodes really well for IT because what they’re looking for is the ability to learn ‘the next thing,’ not whether you’ve already learned it,” she says. “Let’s say you’re using a certain type of software in your current job — did you require formal training, or did you pick it up on your own? And what other things are you picking up on your own to demonstrate that you’re a fast learner?”
One job candidate she knows had multiple degrees but was frustrated about his job prospects. He started pulling down data sources to analyze what successful people did to find a job. “He didn’t know Hadoop, but he had the intellectual curiosity to take an analytics-based approach to find out how people get jobs, and he got a job that way.”
Most important, Roberts says, be honest with yourself about whether the environment of big data and data science is for you. In addition to natural attributes like creativity and intellectual curiosity, prospects need to be comfortable with near-constant uncertainty. This stands in direct contrast to what IT professionals are accustomed to, she says. “They want to stomp out uncertainty by keeping the network stable or standardizing on software. So, be honest — is it going to make you nutty to jump into an environment where you often don’t know what the deliverable is going to be, and almost everything you work on has both success and failure associated with it?” That aspect of the job, she says, isn’t something you can fake. “If the hiring manager asks for you to ‘Tell me about your crazy mind and the work you did on the weekend for the love of the game,’ you’ll be outted quickly,” she says.
Brandel is a freelance writer. She can be reached at marybrandel@verizon.net.
