Why Data Scientists Must Know About Change Management

This week I was invited to give a guest lecture on data science for a group of change managers. We discussed the social effects of predictive maintenance on the workforce and how to deal with the implementation of this concept from a change management perspective. At the end of the lecture I came to realize that it’s the data scientist who should know about change management rather than the other way around. This might make the implementation of the improvements found by data scientists much more efficient since this tackles behavioral change: One of the biggest hurdles in implementation of our ideas and realizing our goals.

Change management may be seen as a domain opposed to data science. Data science is hard, change is soft. Change management is not about the solution of messy problems but about the process of change, data science is all about the solution after getting our the messy data. One of the core ideas of change management is that the change comes as part of a process and not at a sudden, let alone predicted, time. Change comes from within peoples’ acceptance to change.

One of the models of change management that we discussed during my lecture states that “Change is the result of a change in behavior due to the reaction on a new reality that exists due to interventions.” Change is a result not the starting point. Goals are what you are heading for and change is needed to reach those. But to reach it one must think retroactive, back to the point where interventions need to be implemented to create the new reality.

And that is what I realized: creating a new reality is typically what we data scientists are doing – based on our insights on how a system behaves – based upon a dataset. A successful change implementation however does not start with the new reality: it starts out working with the ones that are to react on this new reality. I conclude that is might be effective for us data scientists to embrace the concept of change management and seek collaboration with this methodology before implementing our results.

Let me illustrate my ideas by elaborating on a specific case. Let us imagine a factory producing a not-too-complex product. Maintenance and overhaul is performed by the technical department. Every day coworkers on production line and employees from maintenance department work with the machinery and as such they know and sense the state of the devices. Take James, a 45 year old maintenance engineer who has worked at this company for more than 25 years. He feels whether maintenance of a machine is needed simply by twisting bolts and arms. Of course machinery sometimes fails, but hey – it happens.

Now predictive maintenance comes in. Data scientists take their seats and start working on prediction of upcoming maintenance before anyone could sense that the machinery would fail. Note that the intention to start doing data science within the organization is an intervention in itself.