In this post, I’ll list few examples from various industries to help you differentiate between business intelligence and data science problems.
Sometime back, I blogged about “Business Analytics Continuum” and in the post we saw that Every Organization has DATA but they use their business data at different levels because of their maturity level. Excel (or other transactional reporting tools) is usually the starting point for any organization – it helps them see WHAT happened. They advance to the next stage, where they get capabilities to slice and dice their data – To find out WHY – and usually this capability is delivered using Business Intelligence tools & techniques. Once the data culture spreads – Thanks to a successful Business Intelligence project – then they soon start to outgrow their business intelligence capabilities by asking problems that need predictive capabilities. This is advanced analytics and Data Science stage. To that end, here are 5 examples to help you differentiate between business intelligence and data science problems:
|Business Intelligence.(WHAT & WHY)||Data Science & advanced analytics.|
|Bike Rentals||Can you predict bike rentals on an hourly basis?|
|Credit Risk||Can you predict the credit risk of the customer during contract negotiations stage?|
|Customer relationship management||Can you predict customer churn?|
|Flight Delays||Can you predict whether a scheduled flight will be delayed by more than 15 minutes?|
|Customer feedback||Can you classify a customer feedback comment into “positive”, “negative” or “neutral”?|
I hope this helps!