Both data science and business intelligence are concerned with converting raw data into business insights so that higher-level managers can make the right decisions at the right time. So what’s the difference between them? In this article, I will explain the difference between Data Science and Business Intelligence.
Difference Between Data Science and Business Intelligence
There are many similarities between data science and business intelligence because the goal of data science and business intelligence begins with raw data and the conversion of data into actionable insights so that business leaders can make the right decisions at the right time.
Data science and business intelligence differ from each other based on their approaches. In Business Intelligence, we use forward-looking methods like forecasting using historical data. BI deeply explores past and present data to make decisions about the future and describe the manager on the future course of action.
The role of a Business Analyst seems very identical to that of a Data Scientist, so how are the two different? The answer is that a Data Scientist seeks to make discoveries by using advanced algorithms to analyze and generate predictions from a very large amount of data. The approach used in Data Science is very valuable for the long term future of a company.
A business analyst tries to explore historical data to make predictions about the future course of action. As a Data Scientist tries to discover new paradigms and new ways of looking at data to provide a new perspective on the future course of action.
Data Science Vs Business Intelligence: Approaches
Below are some of the most important difference between data science and BI based on their approaches.
- Data Sources: Business Analyst uses structured data sources only like relational databases, while in Data Science we use structured and unstructured data sources like social media and machine-generated data.
- Reporting: In BI, we use reporting tools such as data tables and dashboards, while in Data Science, we report through words and data visualizations and not tables because the sources of data used in Data Science is very complex that managers will not be able to understand.
- Expertise: A business analyst relies on computer and business technologies, while in data science we use statistics, math, programming, and business knowledge.
When To Use Data Science and When Business Intelligence
If you don’t know when to use Data Science and when to use BI, you will get your answer in this section.
If you want to use data insights to get a feel for how to handle how your business works, you need to hire someone who is a business analyst. A business analyst will create interactive dashboards and tabular data reports to provide recommendations to your business to help you better understand how the business works.
But if you want to get answers to very specific questions based on the data generated by your business, you need to trust a Data Scientist. Here, the Data Scientist will be invited to provide solutions to the very specific problem related to the data and the job of a data scientist will be to generate results and then transmit them to the business analyst who will help him make recommendations.
There are therefore both similarities and differences between Data Science and Business Intelligence. If I want to wrap it up in one statement, I’ll just say that a Business Analyst uses the solutions created by a Data Scientist to help a business realize and build a solution.
I hope you liked this article on what is the difference between Data Science and BI. Feel free to ask your valuable questions in the comments section below.