Data Analytics: Not Just for ‘Scientists’
There’s often been talk of insatiable demand for data scientists, but you don’t have to be a “scientist” to benefit from this demand. Plus, you don’t need to be part of a distinct data analytics team or department – there is a need for skills all across enterprises.
Data analysis as a broad discipline is hot – perhaps the hottest job category out there right now. Close to three in five employers say expect to increase number of jobs requiring overall data analysis skills in next five years, according to a recent survey of 400 executives by the Society for Human Resource Management (SHRM).
SHRM’s survey, sponsored by the American Statistical Association, also found that 82% of employers have positions that currently require data analysis skills, and 78% reported difficulty recruiting for data analysis positions in the last 12 months.
Data analytics is in demand in the core areas of enterprises. The most common functional areas for data analysis positions are accounting and finance (71%), human resources (54%) and business administration (50%). Usually these are full-time positions at mid-level management (79%) and individual contributor (73%) levels. In addition, 60% require senior management or executives to have data analysis skills.
Again, in most cases, you don’t need an advanced degree or the title of “scientist” to be able to be part of this movement. Seventy-six percent report data analytics is in the domain of those with “analyst-based titles,” while 54% are overseen simply by “analysts.” Only nine percent report data analytics are handled by those with the formal “data scientist” designation.
Essential data analysis can come from a formal college degree, but professionals can develop these skills on the job. Only about one-third of organizations prefer a degree in analytics, computer science or statistics. For advanced skills, most organizations require either a bachelor’s (57%) or a master’s (25%) degree.
A separate survey conducted earlier this year by Money, in partnership with Payscale.com, confirms the thirst employers have for data analytics skills. The leading skills of the 21 most career skills involved a data analytics skill set – including SAS skills, followed by data warehousing/data mining and data modeling. “Mainstream American companies have come to realize that in order to become more effective in the marketplace, they need to analyze data,” says Matt Sigelman, the CEO of Burning Glass Technologies, quoted in the Money report. “We’re seeing those skills showing up at a premium in a variety of industries, including marketing, logistics jobs, and operations management jobs, just to name a few.”
Of course, the shape of this demand depends on the kind of organization. Publicly and privately owned for-profit organizations were more likely than government organizations to have data analysis positions in the marketing, advertising and sales function, the SHRM survey report finds. Publicly owned for-profit organizations were more likely than nonprofit and government organizations to have positions requiring data analysis skills in the supply chain and operations function.
Interestingly, there isn’t a lot of flexibility built into these positions. The vast majority of organizations (98%) that required data analysis skills had full-time positions. Few organizations had part-time, contract/temporary and internship positions.
It isn’t just data “analyst” or “scientist” jobs that are in demand — there are also interesting new opportunities springing from the data analytics revolution. TechRepublic’s Mary Shacklett explored some emerging opportunities in a recent post:
Citizen data scientist: This is likely to be “an analytically talented individual from the organization who does not have a formal degree in data science or engineering but who takes on the mantle of developing complex algorithms and queries of data that can yield breakthrough information for the company.”
IoT specialist: This is a skillset that will be borne by manufacturing engineers or machine operators and technicians who will be charged with “harvesting information from sensors in these machines and then moving the sensor-based input into software and systems that are running machines, coordinating machine-based operations and handoffs, and checking on machine health,” Shacklett observes.
Data hygienist: “To really get data to the level of business precision organizations want, employees with first-hand knowledge of the business must refine the data by hand. These by-hand employees who refine and clean up data come from administrative and clerical functions.”
Data orchestrator. This is someone charged with orchestrating data movement across the enterprise. “Increasingly, application developers and systems analysts will assume key roles in determining the different speeds and resting places of data throughout the enterprise.”
Paraprofessional analytics: This will come out of machine intelligence systems that “are already assisting with medical diagnoses and legal research. In law, medicine, and other fields, these analytics are generating new forms of research work that paraprofessionals (e.g., physician assistants, paralegals, etc.) in organizations will most likely perform.”