Linda Burtch, Managing Director at Burtch Works | 30+ years’ experience in quantitative recruiting
With the whirlwind of change in the data science and analytics market over the past few years, one could be forgiven for feeling a bit like Dorothy and Toto being tossed around in a Big Data-hype tornado. Luckily, with self-driving cars around the corner, it looks like we’ll be able to skip using glittery, red heels for transportation, and just stick with more practical footwear. What else is in store? Let’s have a look in my crystal ball and see!
1. Continuous learning will be front and center
Now more than ever it will be crucial for analytics and data science professionals to keep their skills sharp and current. This means a commitment both from the individual to invest their personal time, but also from the organization to foster and support training (and retraining) as part of doing business.
2. Companies will look to “train up” existing talent
Continuing along the lines of prediction #1, with the skills gap still presenting a significant hurdle for many companies, more will look to train their current employees to fill their analytics needs. This in-house talent has the added appeal of domain experience.
3. Predictions 1 and 2 lead to longer tenure
Many analytics professionals and data scientists will embrace the idea of learning new skills and tools, and this could, in turn, encourage greater job satisfaction, resulting in longer stays with their companies. This is counter to the attitude of many California tech firms, who tend to promote the idea of “tour of duty” two year stints as the norm.
4. HR analytics will become more common
As many large organizations grapple with the challenges of employee efficiency, engagement, and attrition, the use of talent analytics will become standard at larger companies. As a quantitative recruiter, I’m quite interested to see how this trend continues to develop!
5. Education options: separating the wheat from the chaff
The proliferation of education options for analytics professionals and data scientists over the past few years will begin to thin out. There are copious data science MOOCs, data science bootcamps, and brand new Master’s in Predictive Analytics or Data Science programs, among others, but I predict there will be some weeding out of the ineffective or overpriced options that can’t deliver on their promise of creating career options. However, the demand for these alternatives will not recede, so the ones that are found to be worth the time and investment will flourish.
6. The scrutinizing of analytics ROI
As the maturity of quantitative groups develops, companies will be much keener to judge the return they’re getting on their hefty investments in these groups. Operationalizing the use of data is more critical, but who should be responsible for this – the data scientist? The statistician? Or perhaps a business analyst hybrid such as a “partner” or “program manager”? We shall see.
Investment aside, are the insights and recommendations coming out of analytics groups correct? “Big Data” came under fire as a result of the 2016 election predictions. Analytics had its skeptics before, but now I believe that people will be even more likely to question findings. Quantitative rigor is only getting more complicated, not less, so despite the ever-present skills gap now is not the time to scale down the technical expectations of analytics hires.
8. What’s old is new again, IoT edition
Operations optimization was a big thing in analytics 30 years ago, but then it was dwarfed by applications in marketing and finance. But now it’s back, all because of fast, inexpensive computing and sensors. And, candidates are very interested in jumping in.
9. Mission driven career options become more compelling
Recommender engines? Real time bidding? Ad targeting? Been there, done that! We’re seeing a shift towards mission driven career objectives, where quantitative professionals feel they can contribute to something they find socially responsible, whether it’s to support curing cancer, assisting with the development of clean energy options, or even volunteering their data skills for non-profits.
As always, I’m interested to hear your thoughts on where you think the analytics and data science hiring market might be headed this year. Any predictions you agree or disagree with? Perhaps a few predictions you’d like to add to the list? Let me know in the comments!