More than 90 percent of marketers and publishers plan to make audience data a primary and permanent asset of their enterprise strategy, according to a recent study by the Winterberry Group. It’s a striking figure.
Yet less than 10 percent describe their organizations as “data-driven” although nearly half expect to reach that lofty status this year. For smaller data marketing agencies, the Winterberry researchers raise a compelling, allied point: It will take more data scientists skilled in data modeling, segmentation and attribution to bridge the gap.
Inasmuch as there’s a growing workforce shortage to fill the demand, marketers need to find and develop new analysts that can have a direct impact on how enterprises use data to make informed business decisions. Imagine having a chest of cutting-edge tools to solve problems and grow organizations but not enough qualified technicians to apply them.
Of particular importance to data scientists is a recent Capgemini survey of 500 organizations that showed the demand for analytics and big data skilled professionals well exceeded the supply. Roughly 65 percent of the employers acknowledged high demand for qualified analytics scientists but only 50 percent said had employees proficient in that area. The gap was even larger for big data.
Keep in mind that a skills gap is not the same thing as a workforce shortage. A skills gap means either job hunters or employees lack the proper training and resources to do the job correctly. A shortage of qualified workers means there’s not enough skilled people to fill the need.
How, then, in a gap period, do smaller data marketing agencies acquire and retain talent?
- Travel the road less taken. Look at other fields for analytical, detail-oriented people who can achieve data scientist skills. Gamification is a solid place to start.
- Think young. Engage the millennial audience in recruitment campaigns.
- Plan in advance. Draw out a clear advancement path for Gen Y and Gen Z talent.
- Look inward. The next great data scientist may be already working at your company.
- Use a wide angle lens. Data scientists know the tools inside and out but there’s art to the science. Today’s non-scientist may be tomorrow’s data whiz.
- Forget perfect. No one perfect candidate fits every single job requirement. But some come close. Find quick learners whose curiosity about big data/analytics will take them far.
For smaller agencies, trained and qualified big data analytics candidates don’t often just show up, we have to be a bit more resourceful in finding and recruiting them.