From the course: Business Analytics Foundations: Predictive, Prescriptive, and Experimental Analytics

Business analytics compared

From the course: Business Analytics Foundations: Predictive, Prescriptive, and Experimental Analytics

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Business analytics compared

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- [Narrator] If you have been watching the analytics horizon over the past few years, there are a few buzz words that stand out, the most important of them being: Business analytics, business intelligence, data engineering, and data science. How are these terms different? How are they similar? Let us find out. When you look at the various activities associated with data inside a business they can be defined as the following. It starts from getting data from data sources and building data pipelines. Then data is processed, transformed, and stored. The processed data is used to build dashboards and reports. This then becomes the basis for exploratory analytics, statistical modeling, and machine learning. They then translate into business recommendations and actions. So, which of these process elements are covered in each of these terms? Let us start with data engineering. Data engineering covers the acquisition, processing, transformation, and storing of data. This is the heavy-lifting work to get the data ready for analytics. Business intelligence is the basic analytics of data, which includes dashboards, report, exploratory analytics, and getting business recommendations out of them. Business analytics covers all the activities of business intelligence. In addition, it includes advanced activities, like statistical modeling, machine learning, and also delivering business actions out of these. Data science covers all the process activities. It is data engineering and business analytics added into one.

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