Differences Between Data Science And Business Intelligence

May 17, 2022

Data science is an encompassing term that is hard to define. Data science is an umbrella field that splinters in many directions. The Smart Data Collective explains the difference between two types of data science in, “The Difference Between Business Intelligence And Real Data Science.” According to the article, real data science is combining old and new data, analyzing it, and applying it to current business practices. Business intelligence (BI) focuses more on applications, such as creating charts, graphs, and reports.

Companies are interested in employing real data science and business intelligence, but it is confusing to distinguish the two. Data scientists and BI analysts are different jobs with specialized expertise. Data scientists are experts in predicting future outcomes by styling various models and discovering correlations. BI analysts know how to generate dashboards for historic data based on a set of key performance metrics.

Data scientists’ role is not based on guesswork. They are required to be experts in predictive and prescriptive analyses. Their outcomes need to be reasonably accurate for businesses’ success. BI needs advanced planning to combine data sources into useful content, data science, meanwhile, can be done instantly.

There are downsides to both:

“As you cannot get the data transformation done instantly with BI, it is a slow manual process involving plenty of pre-planning and comparisons. It needs to be repeated monthly, quarterly or annually and it is thus not reusable. Yet, the real data science process involves creating instant data transformations via predictive apps that trigger future predictions based on certain data combinations. This is clearly a fast process, involving a lot of experimentation.”

Business intelligence and real data science are handy for any business. Understanding the difference is key to utilizing them.

Whitney Grace, May 17, 2022

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