I know that headline sounds totally off base at first glance, but if you stick with this post and read the context behind it, you’ll see that it isn’t a silly question. In fact, this blog was spurred by a discussion between several analytics and data science executives that I sat in on a short time ago.

The issue raised in this blog certainly applies more broadly than the world of analytics and data science. However, due to the rapid evolution, ongoing supply / demand imbalance, and personality types within our field, it is probably a bigger issue for us than for many disciplines.

Shrinking Tenures Are the Norm (Duh!)

It used to be that people would apply for a job with the intention of staying for an extended period of time if hired. Companies similarly planned for a substantive tenure if an offer was made. Of course, that hasn’t been the expectation on either end of the process for quite some time. In fact, people seem to be voluntarily switching jobs more often than ever while companies seem more willing to lay people off than ever.

While we all understand this trend, many corporate policies are still holdovers from the old, lengthy tenure days and haven’t been properly updated to account for the new reality of today. The way new hires are handled is one of these areas.

The Traditional Hiring and Onboarding Process

A lot of large companies have spent many years developing new hire onboarding, training, and development programs. Many are impressively extensive and well thought out. The problem is that those same programs were developed during a time period where lengthier tenures warranted lengthier new hire programs that also had high costs.

During a recent discussion, an executive from a 100+ year old organization was frustrated that their organization was losing candidates due to the training protocols in place. New hires were considered “new” for anywhere from 2 to 3 years, with ongoing training throughout that period. In the past, that might have been considered a plus to a candidate. In today’s world, many candidates find it off putting.

A candidate is only looking ahead at most 2 to 3 years today. If the training lasts that long, then they won’t be ready to advance their career until after that. That means a candidate is looking at a minimum of a 4- to 5-year commitment to get what they want out of the job. Many candidates simply passed on this executive’s openings, in part because they weren’t comfortable with the lengthy implied commitment and the slow career advancement.

Adapting to Today’s Reality

The other executives in the conversation suggested that a different approach is needed. None of us will convince candidates to commit to a long-term relationship today, so we must accept the reality that a 2- to 3-year relationship is very likely. This means changing our onboarding and training protocols to make the most out of the time we’ll have rather than being as intensive and exhaustive as in the past.

The executives had several suggestions to get the most from an employee if you’ll only have them for 2 to 3 years:

• Obviously, ramp up time must shrink, and new employees must get productive quickly. Some may not succeed at the quicker ramp up, but those that do are the ones you need to keep happy anyway

• Rotate new employees through different teams and / or project types once (or even twice) per year. This gives them a variety of challenges and increases the chance that they’ll find a spot they can see staying in for a long time

• Don’t have conversations about a multi-year horizon with candidates unless they bring it up. Focus on the cool projects they’ll be involved with in the short term and how they’ll be exposed to a variety of challenges out of the gate

If organizations need to get new analytics and data science employees productive quickly, then this, somewhat counterintuitively, means not investing too much in onboarding. Not only does lengthy onboarding take more time than new employees will accept, but it also makes it all the more costly and frustrating for an organization when people leave before the investment can pay for itself.

If your team is doing interesting and impactful analytics, many people will choose to stay for longer than a few years. But they have to experience your environment and come to that decision over time and on their own. It can’t be forced or assumed from the beginning. Adjust both your expectations and your investments accordingly.

Originally published by the International Institute for Analytics

Bill Franks, Chief Analytics Officer, helps drive IIA's strategy and thought leadership, as well as heading up IIA's advisory services. IIA's advisory services help clients navigate common challenges that analytics organizations face throughout each annual cycle. Bill is also the author of Taming The Big Data Tidal Wave and The Analytics Revolution. His work has spanned clients in a variety of industries for companies ranging in size from Fortune 100 companies to small non-profit organizations. You can learn more at http://www.bill-franks.com.

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