The translation between the business owner and the data analyst takes a lot of time in my experience, and Aible shortcuts most of that communication chasm. For example, out-of-the-box machine learning models optimize on accuracy, which gives equal weight to the cost of failing to predict something vs. incorrectly predicting something will happen. There are ways to adjust for this – you can manually adjust thresholds after a model has been run or you can run a model with a penalty matrix. But both of those require a lot of mental gymnastics and accurately communicating business needs to the analysts running the model. It takes time and clear communication. There are many other steps in the process of setting up and iterating models that take weeks. Aible talks to business owners in the ROI and constraints language they use and understand, reducing the back-and-forth discussions. In our case, we predict which nurse might quit their job so a hospital can intervene early and try to retain that nurse. The cost of failing to predict a nurse quitting is very different than the cost of unnecessarily intervening with a nurse who would have never quit their job. Hospitals have limited resources to follow up with nurses to retain them. Building cost-benefit tradeoffs and constraints into the models that reflect the business realities of the organization is hard to do; Aible does this automatically and gives me optimal recommendations we can act upon."
- Tim Darling, Chief Analytics Officer of Laudio