It is 2017, almost everyone is familiar to the concept of recommendation engines. Netflix recommends your favorite shows and movies with great accuracy. Amazon recommends books that you might want to consider reading, products that you might consider buying and services that might benefit you. LinkedIn & Facebook recommends “People you may know” out of a large database. Google recommends sites while you type in your search term. It is fast, it is relevant, it is intuitive and it just is a better user experience. You now realize that this approach is now quite pervasive and effective. Then why is it that you and your BI user community today does not have access to recommended reports or dashboards? Let’s dive into this to understand this better.
How do recommendation engines work?
Recommendation engines are one of the most widely adopted use case for Big Data. It is all about looking at a large pool of data, apply machine learning algorithms to make predictions on what would be the most relevant information in the context of a specific user. Now you may ask what has this got to do with my BI Investments? Well, perhaps there is a missed opportunity here that is worth exploring. Your BI and Big Data end-user community could benefit from a recommendation engine. While there is quite some literature on recommendation engines and what they do, I would like focus on how you can leverage this concept to extend your existing investments in BI.