Description of the Types of Analytics and Examples

Analytics is the science of analyzing raw data in order to achieve a better
 understanding of the raw data and/or system and to make conclusions about that
 information. The aim of analytics has been said to provide better support to the
 decision-making process.
 

There are four types of analytics – descriptive, diagnostic, predicative and
 prescriptive.
 

Descriptive analytics consider the historical aspect of the data. It can organize the
 data into a convenient form including, but not limited to graphs, tables and control
 charts to further understanding for non-analysts. It is said to answer the question of
 what happened.
 Examples of descriptive analytics include:
 Summarization of a particular business result after a change is made to a
 process
 Determination of whether a quality problem exists in the manufacture of a
 product
 

Diagnostic analytics consider identification of positive and negative anomalies that
 are uncovered after looking at the output of the descriptive analytics and trying to
 determine why that (those) anomalies occurred. Diagnostic analytics is synonymous
 with root cause analysis.
 Examples of diagnostic analytics include:
 Determination of the cause of a spike in sales of a product, or better yet,
 sustained increase in sales
 Determination of why a product unexpectedly deviates out of its control
 parameters and worse yet, its specification parameters.
 

Predictive analytics utilize data and statistical models to determine what might
 happen with various data scientist given scenarios. It is complex and uses
 sophisticated tools. Wikipedia writes that the following scientific disciplines
 comprise predictive analytics
 Machine Learning
 Operations Research
 Computer Vision
 Natural Language Processing
 Applied Statistics
 Signal Processing
 Image Processing
 Metaheuristics
 Date Mining
 Examples of predictive analytics include:
 Using spending patterns, outstanding credit balances, income sources and other
 financial measures to calculate credit scores which will predict a person’s ability
 to afford a mortgage, a new credit card or an auto loan.
 Using spending patterns to identify excess spending such as might occur when
 one’s credit cards are stolen.

 Prescriptive analytics suggests a range of prescribed actions and the potential
 outcomes of each of the actions. It may even go so far as recommending one of the
 prescribed action / potential outcomes as being superior.
 Examples of prescriptive analytics include:

Self-driving cars. Given a location and destination, prescriptive analytics will
 consider a range of driving paths, speeds and traffic to safely travel from start to
 finish in the shortest amount of time. It also updates the prescription as time
 goes on.
 Air travel. If bad weather suddenly appears at an airport, prescriptive analytics
 will consider how to safely hold and/or re-route incoming traffic until conditions
 improve and route these to the air traffic controller.