Welcome to the next installment of our Analytics Journey, which explores how we at Ruths.ai apply the CRISP-DM method to our Data Science process. Previously, we looked at an overview of the CRISP-DM methodology as a whole.
For our initial step along the journey, we will examine the stage of Data Understanding, followed by Data Preparation, Modeling, Evaluation, and Deployment. As we explore the process, we hope you follow on the journey and consider how the steps might apply to your company, department, or even simply a current project you are working on.
Using the Business Understanding Stage to Create Buy In
The Business Understanding stage consists of setting objectives, producing project plans, and creating business success criteria. However, this week I want to focus less on the logistical facets of the stage and more on the strategic and even emotional aspect of Business Understanding.
Yes, there is a place for emotion in Data Science. Why? Because understanding emotion and respecting our clients’ industry expertise remains vital to our ability to bring a project to fruition.
When people resist analytics, they are often viscerally and defensively reacting to a perceived threat. They might feel threatened because they do not understand the technology. They might feel analytics disregards years of built up industry knowledge. They might feel analytics threatens their very job itself.
As analytics professionals, we must reassure the user that our methods will help make their job easier not replace it. Our methods will supplement their knowledge while improving efficiency and allowing more time spent on more important questions rather than tedium that can be automated. Most importantly, our methods will not reject their industry knowledge but embrace it in order to build more robust applications.
We can only provide this reassurance and achieve buy in by getting to understand the business and listening, truly listening, to a client’s needs. The Business Understanding stage serves as the perfect time to communicate our shared goals.
So, when you anticipate resistance to data driven ways during a project, embrace the conversation as an opportunity. Listen openly to the objectives and consider not just your department’s perspective but an organizational one.
If you are one wary of analytics, try to communicate what you need to achieve the best results. Data professionals should be there to make your life easier.
Business Understanding serves as the first step in our analytics process, and clear, concise, and well communicated guidelines now can greatly increase efficiency and remove ambiguity that might arise later in the process.
Jason is a Data Scientist at Petro.ai with a master’s degree in Predictive Analytics and Data Science from Northwestern University. He has experience with a multitude of machine learning techniques such as Random Forest, Neural Nets, and Support Vector Machines. With a previous Master’s in Creative Writing, Jason is a fervent believer in the Oxford comma.