Sagepath Reply’s Digital Analytics Practice is focused on using data in meaningful ways. As enterprises are looking for new ways to decrease operating costs and unlock new sources of revenue, we are finding that predictive modeling has the potential to really move the needle.
However, it is easy to pour hundreds of thousands (or millions) of dollars into data endeavors without seeing a return on investment. In this blog post, we have compiled some of Sagepath Reply’s top lessons learned for delivering successful Data Science Predictive Modeling use cases.
Keep the Discovery phase of the project efficient and concise. While effort here goes a long way to having everyone happy in the end, it’s important that it’s time bound. Remember, the use case may not be viable for several reasons - not enough data, business value isn’t there, end users won’t adopt the model recommendations, etc. This is totally normal for data science initiatives, but in order to ensure you are positively impacting your bottom line - it’s best to get to this point as quickly as possible so that the team can move on to the next use case (if needed). We have found the following tips helpful:
The modeling phase of the project is where the rubber hits the road. Once again, it’s important that the modeling phase is time-bound and that the focus remains on an MVP model. Tuning the model can be a never-ending process, which is okay if you have the ROI to justify the resource investment. Sagepath Reply’s recommendation is to focus on an MVP model that can be piloted by the business. Here are some tips that we have found helpful in ensuring an efficient modeling phase:
Sagepath Reply recommends that every data science use case includes a Pilot Phase. We view the Pilot phase as the gatekeeper before our clients invest into “productionalizing” the model. Here are some ideas that can be used to drive a successful pilot phase:
It takes more than a Data Science team to successfully deliver Data Science use cases. Sagepath Reply is a full-service digital agency who can help in many aspects of use case delivery: