Chances are, you already know that there aren’t enough analytics experts out there to tackle the work that business needs done. Even as more organizations look for ways to leverage the big data compiled from their own operations or wider sources, the pipeline of talent lags.
Indeed, the research firm McKinsey predicts that by 2018 the U.S. will have 190,000 fewer data scientists than it needs to meet demand. That’s on top of the predicted shortage of 1.5 million managers and analysts who have the skills to effectively put to use the intelligence contained in all those numbers.
To put that in perspective, McKinsey estimates that the use of big data analytics could increase GDP in the retail and manufacturing sectors alone by some $325 billion by 2020. It predicts more than 40,000 exabytes of data will be collected that year – up from 2,700 exabytes in 2007 – across a number of sectors including retail, manufacturing, health care and government services.
Put simply, the analytics talent shortage is no small thing.
For employers, the analytics talent shortage poses an unprecedented challenge.
For employers looking to build their analytics teams, the situation poses an unprecedented challenge. From a limited pool, they have to attract the best talent, then keep those professionals engaged and in place for a number of years as the pipeline builds and the candidate pool deepens.
That’s an expensive proposition. One study pegged the median salary of new data scientists – those with one to three years of experience – at $91,000, while team leaders earned $250,000. PayScale estimates the average salary of a data scientist with five years of experience at $118,000.
Analytics professionals consider everything from an employer’s general culture to its commitment to data-driven decision-making.
Salary expectations, however, are only one part of the equation. Data scientists and other analytics professionals know they’re in demand, and when they’re considering an employer they look at everything from the company’s general culture to its commitment to data-driven decision-making.
You can expect that candidates will do their research to get an idea of how their work will fit into your business. At the same time, understand that your current employees are watching the market to see which other firms are pursuing analytics projects in the most interesting ways, and possibly offering opportunities that are more in line with their interests. In both cases, your commitment to data counts.
So does your commitment to each individual’s success. Like other professionals, those skilled in analytics want to know they have options at your company – options for growth, to take on challenging projects, to gain further education and training. That’s why it’s important to weave data science into your culture. For example, regular informal sessions where the team can learn new skills and share experiences can foster a stimulating environment and speak to your interest in expanding the team’s knowledge and sense of best practices.
At the same time, look for ways to align each team member’s interests, skills and career goals with appropriate projects. Be sure everyone has a career-development plan that will keep them engaged by both their current work and the opportunities you can offer them in the future.
Not only are these smart approaches to recruitment and retention, they have the added effect of developing your company’s reputation as an employer of choice among the data science community. At a time when analytics professionals can pick and choose their opportunities, that’s the type of employer brand you want to have.