I have what I believe is a unique career development philosophy; namely, that you should not stay in any organization for more than five years. What’s amusing about this philosophy is how new colleagues sometimes become quite frazzled when I have a coffee with them in the first week or two of their jobs and pose a question such as: “What do you need out of this job to get you to the next one?” Many people look troubled because they feel they've already done something wrong and I'm helping them think about their next role.  

In my own career, I’ve been a one-man test case for how change can help you grow. I trained in the U.K.’s National Health Service (NHS) to become a doctor. After training I practiced emergency medicine in the same system. Between medical training and data leadership, I’ve also worked as a consultant to develop my digital strategy thinking. Now that I’m back at NHS, one key difference is that I have a more mature skill set around data and analytics, as well as better digital strategy thinking that I've been using in our integrated care system work. 

All this movement is less about some sort of career attention disorder and more a personal belief that you should not become too comfortable in an organization. When you do, you run the risk of becoming stale. And you almost discount what’s happening in the rest of the world. We all know of organizations that fail to innovate as the leadership is unhealthily attached to traditional approaches.

To succeed, data leaders need to develop themselves while they develop the organizations they work in. Just as you can't look at the same data day after day, you can’t grapple with the same data challenges forever and stay fresh. To grow, you have to work your way on toward the next set of challenges, and often move inside and outside your industry. You always get the most complete perspective when you look at something from all sides. 

A team of data entrepreneurs
What has my job philosophy yielded? With my NHS data analysis team, we very much have a startup mentality. Most of us feel that we’re in it together. A lot of the initiatives we've created would be regarded as quite rebellious. It's not that we don't obey the rules, because there are always rules to obey, but we work to flex as much as we can. I call a flattened hierarchy. We don’t work as if we’ll all be here for 35 years. In a hot talent market like data science the last thing I want to do is prevent someone from realizing their full potential. In the data science space, it become all the more necessary to institute good principles from the gig economy but also realize that having a strong talent and succession pipeline is critical.

Because we work with data, my team members can operate flexibly and are location-agnostic. We track outcomes and not clock-watching. In fact, my employees refer to themselves as global citizens. Perhaps they’re already preparing themselves for their next opportunity and the new way in which industries will function after the pandemic.

I believe our job as data leaders should be to constantly identify talent, nurture it and develop the next generation. We should provide enough of a play area for folk to experiment and do great things: think more enabler and coach rather than boss, hierarchy and performance management. It’s always in the back of my mind, especially when it comes to my role as a data leader, that once I’ve built a thriving data culture maybe they won’t need me anymore. In my mind that would be a good thing. 

True leadership in the dynamic world of data-driven decision-making is defined by the legacy one leaves behind—that is, by the systems, protocols, and, most importantly, competent heirs—rather than by one's indispensible eternal presence. In order to effectively navigate the enormous complexity of the information age, data leaders should actively work to replace themselves by creating conditions that support development, mentoring, and empowerment. By doing this, organisations are propelled into the future by leaders who are the result of visionary mentorship and who are ready to innovate using both the promise of the future and the lessons learned from the past. It also ensures the continuity and robustness of data-driven endeavors.

The views here represent those of the author only.