Communicating Data Science with impact

One of the major differentiators between a new Data Scientist and a more experienced one is how the more senior practitioner spends a lot of time understanding the impact of the work, not just the performance of any given model.

Jesse Heap has a great article of how to maximize the impact and adoption of your work. Here are the highlights:

1. Remember that “key business stakeholders are often reluctant to implement … a new model unless they can clearly understand how it could drive significant impact against their key business metrics”, yet typically they “do not have the proper training to help interpret, understand and properly apply the output from our models”.

2. You must “defin[e] what success looks like in the language of your stakeholder”, and “make sure [you] are comparing results against how things are currently being done”.

3. Once you understand success enough to define what metrics to use, ensure that “these metrics … are discussed prior to any project and in collaboration with your key stakeholders”.

4. And finally, “before jumping right to an advanced ML model, ensure you are exploring simpler approaches first.”