Sometimes I get bogged down by the technical details of learning how to program in R and Python and not seeing the endgame. Today I went down a little Google rabbit hole of “business analytics 101” top-clicks, because I felt like I wanted to gain some perspective on what the full lifecycle of data for making business decisions is.
I found this blogpost, which is basically a mini-course-lecture on Business Analytics in the Current Moment: https://practicalanalytics.co/bianalytics-basics/
- The data supply chain: “Raw Data -> Aggregated Data -> Intelligence -> Insights -> Decisions -> Operational Impact -> Financial Outcomes -> Value creation.”
- There is a race to turn this “big data” into a personalized, comprehensive portrait of an consumer, customer, prospect or visitor.
- Pattern detection, personalization, visualization, test-and-learn experimentation via rapid prototyping is the new norm.
- A whole new form of self-service consumer engagement enabled by analytics (discover, analyze, visualize, explore) is just starting to take shape.
- World-class companies excel because they’ve made tough decisions about which analytical processes they must execute well, and they’ve implemented platforms need to streamline those processes. Result? Their platforms have become an asset rather than a cost, and tend to foster new experiences for customers.
- I’m looking at you, Tessitura 😡
- And finally, some tools to hone: