- Tags:: 🗞️Articles, Machine Learning, Data Science for Business
- Author:: Necati Demir (Co-Founder & CTO at datagran.io)
- Link:: Machine learning does not produce value for my business. Why? - KDnuggets
- Source date:: 2021-12-07
- Finished date:: 2021-12-28
I particularly liked the description of finding the right metrics to use a target for the Data team:
The edge case of this example is; measuring the revenue of the company as a metric to evaluate the performance of the data scientist team. As you can easily imagine, the revenue is not only tied to the recommendation system results. So, what to do here?
Build intermediate metrics and measure them.
metric 1 → metric 2 → metric 3 →… → final metric
In the recommendation system example; the immediate metric that the data scientist team can measure is the click-through rate (CTR) which measures how many of the recommended items has been clicked on. The next level metric can be the add-to-basket rate which can be used to measure how many of the recommended items has been added to the basket. And, it goes on like that …
CTR → add-to-basket rate → metric x → metric y→ conversion rate
You just need to know that the more you slide to the right, the less the data scientist team will have confidence. As a leader, you need to find the correct balance between the business goals and the metrics that the data scientist team can have confidence in and has an impact on