Originally posted as an answer to How business intelligence and Data science is related?

It is tempting to dismiss data science as a mere rebranding of data analysis, business intelligence, and statistics. In many cases, people use the term data science as a sexy synonym for statistics. But once you cut away all the hype, there are many differences between business intelligence and data science.

First, I need to point that data science is relatively new, and we are still trying to figure out what exactly is and isn’t data science. As such, there is no precise definition of data science that everyone agrees upon. This does not mean it is not a real thing, but rather it is an emerging discipline.

In my experience, here are a few key differences between data science and business intelligence.

  1. Choice of tools - A data scientist often works with programming languages such as R or Python. A business analyst will probably be more comfortable with graphical tools like Tableau, SAS, or Excel.
  2. Type of data - Business intelligence typically uses structured data found in traditional relational databases, data warehouses, or spreadsheets. Data science uses non-traditional sources of data. This might include HTML scraped from websites, data obtained from web APIs, metadata extracted from files, and many other sources.
  3. Roles and responsibilities - In a typical BI environment, ETL is the responsibility of the information technology department, while business analysis is the responsibility of the business units. Data science combines those into a single role. A data scientist handles every phase of the data transformation pipeline.
  4. Data analysis and extraction methods - A data scientist is more likely to use more involved data analysis and extraction methods.
  5. Not just business - Data science is not limited to business problems. The tools and techniques are applied outside of the domain of business. That being said, a business analyst might have much more knowledge on a particular business area.

However, the line between business intelligence and data science is often blurry. Both use data to inform decision-making but use different tools and techniques.