Business Intelligence and Data Science are distinctively different from each other. In the past, Business Intelligence(BI) was a way of collecting data and correlating that data to create reports that help businesses with useful observations. Now there is an explosion of Data everywhere due to various reasons, in terms of volume and variety. A much sophisticated technology than BI is required for businesses to capitalise on market opportunities faster than their competitors.
BI used to focus in interpreting past data. While on the other hand, data science helps companies in mitigating or lessening the uncertainty of the future by providing them with the valuable information about the projected sales. Data scientists extrapolate on the past or previous data and help companies in making predictions for the future.
Differences Between Business Intelligence and Data Science
Below, are mentioned some of the key differences between business intelligence and data science:
- Perspective: BI is designed in such a way so that it can look backward and can analyse real data from real events. On the other hand, data science always looks forward and it predicts the situations that might happen in the future.
- Objective: The core objective of BI is to make decisions better. Outputs like sales statistics and operational metrics help leaders in providing insights of their last decisions so that if required, they can change it to improve the situations. But data science has a different objective. Data science focuses on predictive analysis. BI used to deal with “What happened in the past?” And data science can give you answers like “What will happen in future if you do something?”. So, it is evident that both data science and BI have different objectives.
- Goals: Their goals are always different from each other. Business Intelligence helps a company in improving its strategic decision-making process. But data science has more determined goals. It usually develops advanced algorithms that can directly help a company’s business to operate in a better way.
- Data sources: Due to its static nature, BI data sources are pre-planned and they can be added slowly. Since the approaches of data science are quite flexible, hence data sources can be added on the go if required.
- Information: BI used to give you the answers to the questions which are already known to you. On the other hand, whereas data science provides you new questions and answers because it always encourages organizations to apply insights to new data.
- Storage: Data always needs to be flexible if you calculate it as a business asset. Since BI systems tend to be warehoused and siloed so it would be very difficult to deploy data across the business. But data science can always be distributed real time.
- Job role: In earlier days, BI systems are owned and operated by the IT department of a company. They used to send intelligence to analysts who then interpreted it. But with data science, the analysts are the core persons. Nowadays, the new big data solutions are designed and owned by analysts. These days, the analysts invest most of their time in analysing data and making various predictions so that companies can take some fruitful business decisions.
- Tools: Tools that are used in Business intelligence are statistics and visualization. But data science uses the tools like statistics, machine learning, graph analysis and NLP.
It is understood that business intelligence and data science are different, but it is also true that both professions are demanding. As per the study report of Mckinsey Global Institute, in the U.S. A there is a current shortage of 140,000 to 190,000 professionals with specific analytical expertise and skills. A plethora of survey report by Burtch Works has claimed that near about 89% data scientists on LinkedIn were contacted and offered various job opportunities monthly, and more than 25 percent usually received weekly job offers from various companies for these positions.