They are used interchangeably since all of them involve working with data to find actionable insights. But I like to differentiate them based on the type of the question you’re asking:
What are my sales number for this quarter?
What is the profit for this year to date?
What are my sales number over the past 6 months?
What did the sales look like same quarter last year?
All of these questions are used to report on facts and tools that help you build data models and reports can be classified as “Business Intelligence” tools.
Why is my sales number higher for this quarter compared to last quarter?
Why are we seeing increase in sales over the past 6 months?
Why are we seeing decrease in profit over the past 6 months?
Why does the profit this quarter less compared to same quarter last year?
All of these questions try to figure why something happened? A data analyst typically takes a stab at this. He might use existing Business Intelligence platform to pull data and/or also merge other data sets. He/she then applies data analysis techniques on the data to answer the “why” question and help business user get to the actionable insight.
- What’s next:
What will be my sales forecast for next year?
What will be our profit next year for Scenario A, B & C?
Which customers will cancel/churn next quarter?
Which new customers will convert to a high-value customer?
All of these questions try to “predict” what will happen next (based on historical data/patterns). Sometimes, you don’t know the questions in the first place so there’s a lot of pro-active thinking going on and usually a “data scientist” are doing that. Sometimes you start with a high level business problem and form “hypothesis” to drive your analysis. All of these can be classified under “data science”.
Now, as you can see as we progressed from What -> Why -> What’s next, the level of sophistication needed to do the analysis also increased. So you need a combination of people, process and technology platform in an organization to go from having a Business Intelligence maturity all the way to achieving data science capabilities.
Here’s a related blog post that I wrote on this a while back:
..And you can check out other stuff I write about here: