How Business Intelligence is different from Data Science?
Technology

business intelligence data science

 

Business Intelligence and Data Science are hot topics these days. These terms are very similar, yet different, and often get on top of each other. When we see them as business process analysis, they are not very different from each other, as both support making business decisions based on data facts. The difference between Business Intelligence and Data Science is so fundamental that it is hard to underline. Basically, they differ in the type of questions they can address. While Business Intelligence presents a business with new values from previously gathered data, Data Science is often used to predict future business trends.

Understanding the difference between Business Intelligence and Data Science in deep, it will help break the two down into the key features they offer and analyze them according to a business need.

What exactly is Business Intelligence?

business intelligence data science

Business Intelligence can be explained as the process of collecting, integrating, and analyzing data to develop strategic insights that help decision-making. The basic purpose of business intelligence is to provide a clear picture of a business's current and historical data. BI is specifically more of a process for analyzing data that helps answer specific questions related to a business and helps them make more informed decisions.

 

What is Data Science?

business intelligence data science

Data science is the process of obtaining more value from the data collected, to be used to solve complex problems. With the help of data science, a business translates the data collected into actionable and meaningful insights. These inputs are used by marketers to help them make future predictions, adapt to trends, study customer preferences, and utilize opportunities in order to scale a business.

 

Business Intelligence vs Data Science

business intelligence data science

It will be helpful for most businesses to differentiate between data science and business intelligence, and how they can produce effective results working simultaneously. It is not just choosing one or the other. Rather, selecting the right solution to get the results a business is looking for, may require using both data science and BI.

While both revolve around data, data science holds the bigger umbrella containing a lot more information. BI, on the other hand, can be termed as a part of the bigger picture. BI focuses primarily on generating reports based on internal data; data science mainly focuses on generating insights out of the data provided.

The tools of BI are often limited and are meant for the analysis of management information and the development of business strategies. Whereas, the tools of a data scientist involve complex algorithm models, data processing, and other bigger tools.

One of the easiest ways to differentiate between the two is to consider data science in terms of the future, and BI as the past and the present. It can also be concluded that data science deals with predictive analysis, and BI is more descriptive.

How to use data science and business intelligence together for a business?

business intelligence data science

Although businesses can dig out more meaningful insights from data science and business intelligence as well, using both together will certainly help drive most strategic decisions. Data science reinforces business intelligence that can very well make or break a business.

BI experts can provide insights for data scientists in form of data that can be further used in their algorithm models. BI analysts offer their current understanding of a business to help data scientists build more powerful models and forecast future trends and patterns for a particular business.

One of the biggest hurdles enterprises face is the rapid growth of allied technologies. If these are effectively used together, they can make business transformations for winning in most cut-throat market scenarios. Today, many enterprises struggle to keep pace with the technology change and fail to understand how to integrate newer and better capabilities while keeping intact the existing ones. Many advanced technologies, including big data, IoT, machine learning, and serverless computerization, are capable enough of transforming the business landscape.

To conclude the advantages of using data science and business intelligence coherently:

  • These are two critical sides of the same coin. Their roles may differ, but if used together, they can serve the business to help reach its goals within the required timeframe.
  • There may be differences in the way data science and business intelligence handle objectives and deliverables. But both have the same goal in the end – scaling a business with the help of data.

Conclusion

With the expansion of a business, managing large volumes of data, and analyzing it to achieve the desired results, becomes crucial. Fully integrated analytics and business intelligence platforms can enable the development of insights for faster decision-making and achieving the set business goals. eComstreet is a global company that has all the capabilities you need to help gain valuable insights from your data. Our team has the expertise to integrate and deliver clean, reliable data with the most standardization methods.

Author

Manas Singh

Designation : Sr.Full Stack Developer
Having 11+ years of experience in application development domain, now he is working on Web3 and blockchain technology. He is having sound knowledge of Solidity language and building smart contracts.

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