Navigating Organizational Resistance

Organizational resistance is a significant hurdle when deploying models in any business. While data science is rooted in mathematics and statistics, its true power lies in fostering organizational change. Imagine you're part of a team that introduces data-driven decisions, which on the surface seems like a clear benefit. However, this often means altering or even replacing existing decision-making methods, which can be met with resistance. To succeed, you need to be perceived as a trusted advisor seeking the truth, rather than a representative of a particular interest within the organization.

The Challenge of Trust

In many organizations, discussions around data science can become quite heated. Consider the story of a department head who once voiced a common skepticism: "You know statistics, right? Don’t they say you can get statistics to say anything you want? Management is just looking for someone to blame." When faced with such strong emotions, it becomes impossible to continue discussing technical aspects like algorithms or statistical measures. The real issue here is that trust was lost long before such meetings.

It’s crucial to anticipate resistance. Someone within the organization is likely worried about the implications if the project succeeds, and they may even actively work against it. More commonly, there is a mix of apathy and distrust toward models in general, coupled with no intention of using them once they're built.

A Case Study: Lead Scoring in Sales

A real-world example of this phenomenon can be seen in sales support, particularly in lead scoring. Lead scoring is a common application in sales, but it's useless if the sales team doesn’t embrace it. An insurance executive once asked for a lead scoring model to help prioritize outreach to lower-priority agencies, known as bronze leads. The sales reps, however, were reluctant to even meet and discuss their needs, which hinted at the potential failure of the project.

After some effort, a meeting with the sales reps was arranged. During this meeting, it became clear that merely telling them to use the new system wouldn’t suffice. The breakthrough came when the team understood the sales reps' daily routines and scheduling practices. They realized that the only way these bronze leads would get attention was if visiting them was geographically convenient and timely.

The Importance of Early Involvement

This case study highlights a crucial lesson: involve the potential end-users early in the project. Before gathering data or exploring it, get their input. This step is invaluable and significantly reduces the likelihood of encountering resistance when it’s time to deploy the model.

Building trust within an organization is key to the successful deployment of data science projects. By anticipating resistance, involving end-users early, and understanding their daily workflows, you can foster a more collaborative environment. This approach not only improves the chances of your model being embraced but also ensures that it meets the practical needs of those who will use it. Remember, you’re not just in the business of creating models; you’re driving organizational change.