Introduction
Data Scientists have become numerous business intelligence analysts. While there are some similarities, there are also some stark differences between the roles. It is important to distinguish between these roles as they become more defined in their own rights, especially today. That is exactly what Matt Przybyla(A senior data scientist and a top writer on the subject) does in his article “Data Science vs Business Intelligence: Here’s the Difference” on Medium(the article can be found here).
Summary of Article
First Matt gets into what a data scientist does. He primarily talks about how a data scientist is focused on model exploration. The data scientist wants to design a new model or refine an old one to make it easier for the business to keep track of these things and to generate some predictive insight automatically with the model. The data scientist will go through multiple models, figure out which ones work best, refining them until finally, they end up with a final product to deliver as their deliverable. This is a very long process and can sometimes take months according to Matt. Business intelligence, on the other hand, is much shorter as far as individual projects go, with some even lasting just a week. Also, the main purpose of a business analyst is to deliver the numbers – or insights – to the shareholders in the problem. Also, business intelligence analysts usually don’t use models and use SQL, Excel, and other software to complete their work.
My Take
Overall, I liked this article. It outlined the differences between the two positions, which I always thought were roughly the same, and they are(to an extent). However, one deals with more machine learning and those types of models, while one is more short-term and looking to help the business make decisions right now. I also thought that the connection between the two is that the business intelligence analyst could do some work to get insights on a topic for a certain company, and then the data scientist could design a model to generate those insights, or at least find anomalies for people to investigate, with a model. One thing that I liked about the business intelligence analysts is their interactivity with the shareholders, as I would imagine that livens up the workplace when you talk to people and interact with them, especially when you don’t see them every day. One thing that I liked about the data scientist position was the fact that it deals with machine learning models, which I enjoy working with. So overall there are some similarities and some major differences between the two positions and they each provide their own unique value to the business.
Conclusion
All in all, I really enjoyed this article. I found it informative, easy to understand, and interesting. It really helped me see what these two positions are becoming as time goes by and how they are now separating from each other and pursuing more distinct roles. Overall, I highly recommend you read the article(the article can be found here).