Both business analytics and data science are similar in the sense that they need to collect data, model/interpret it, and make projections.
But they are two different things!
And here’s the difference:
business analytics typically apply the process toward answering highly defined business questions, mostly using direct business data as input.
Business analytics has a very specific meaning and role definition. Its aim is to collect data from the business, model / interpret it toward a business goal, and make projections which can be implemented by people within your business.
With applications ranging from product recommendations to predicting the half life of radioactive particles. Here’s the key: data science need not directly answer a business-y question.
For example, let’s say you are running a juice bar Your business buys sugar, water, and fruits, and repackages them into juice that you sell from a store front.
As a business analytics person, you will be interested in questions like:
a) is my business profitable?
b) how can it be more profitable?
c) if not profitable, why?
d) which expense item is causing the lack of profitability?
e) can I sell my lemonade at a higher price at certain times of the day?
As a data scientist, you may not only work on the above problems, but also work on problems like:
a) what is the typical demographic profile of a lemonade drinker?
b) will playing rap music or classical music make customers tip more?
c) what is the optimal geographical location to put a lemonade stand?
As you can see, the questions are a bit broader, have wider scope, and can take non-business data as inputs to answer.