Analytics is a very broad term, but no matter the type of data, or size of data you are using, generally all analytics can be categorized in four ways: describe, diagnose, predict, and prescribe. These four categories range from summarizing historical information to providing insight for the future by investigating the data and attempting to answer the following questions:
- Descriptive Analytics: What happened?
- Diagnostic Analytics: Why did it happen?
- Predictive Analytics: What might happen?
- Prescriptive Analytics: What to do next?
Calculating some summary statistics such as mean, median, mode and standard deviation is an example of descriptive analytics.
Similarly matching, or creating a scatter plot diagram are examples of diagnostic techniques that can help identify outliers.
Predictive techniques involve using regression analysis and attempts to find similarities between historical data sets to be able to forecast into the future.
Lastly, prescriptive analytics involve using decision support systems, machine learning and artificial intelligence to analyze the information and recommend a course of action. Multiple predictions are generated and an optimized solution is recommended. For example, if you use a GPS on a long road trip, you may have experienced ‘dynamic route updates’, or the suggestion of new alternate routes that may be quicker due to slowing traffic or an accident.
“12.6. BUSINESS INTELLIGENCE & DATA ANALYTICS” from Information Systems for Business and Beyond by Shauna Roch; James Fowler; Barbara Smith; and David Bourgeois is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, except where otherwise noted.