The term analytics emerged in November, 2005. In the chart shown below, the relative number of Google searches on the term analytics from 1/1/04 to the present are displayed.
- Searches For Analytics
In addition to the dramatic growth in the use of the term analytics, there has been a proliferation in the way it is used, with phrases such as text analytics and healthcare analytics common.
Numerous definitions of analytics have been proposed, but consensus and clarity have been elusive:
- The lead article in the December, 2013 issue of OR/MS Today was entitled ‘The Evolution Of Analytics’. In the body of the article, the authors presented a 200 year history of statistics!
- In recent surveys, operations research professionals have expressed widely differing views on the relationship between operations research and analytics.
However, there is one point on which there is agreement — analytics is related to many different disciplines:
- Davenport, Cohen and Jackson, in the May 2005 research report ‘Competing on Analytics’ mention statistics, operations research, industrial engineering, econometrics, and mathematical modeling as examples of analytics.
- Rahul Saxena, co-author of the December, 2012 book ‘Business Analytics’, on slide #5 of a slideshare presentation, lists 14 disciplines as being antecedents of analytics. The list includes Business Intelligence, Computer Science, Statistics, Operations Research, Industrial Engineering, and Finance Planning & Analysis.
A Simple Explanation
How can we explain the sudden appearance of the term analytics and its connection to so many different disciplines? Did a new meta-discipline, representing a new problem solving paradigm, suddenly emerge?
Let me offer a simpler, and more plausible explanation. In November, 2005 a new concept emerged, and went viral: the idea that it is useful to be able to group together several distinct disciplines, and refer to them collectively with a single term.
This concept makes it possible for statistics, computer science and operations research to be represented by the word analytics in the same way that biology, chemistry, and geology are represented by the word science. Organizations can now consider quantitative decision sciences collectively for purposes of planning and resource allocation.
In current practice, the term analytics is used to represent two different groupings of disciplines:
- a base grouping (statistics and computer science) – e.g., data analytics;
- an extended grouping (all quantitative decision sciences) – e.g., business analytics.
Conclusion
When analytics is understood to be a conceptual grouping of quantitative decision sciences, confusion disappears:
- the sudden emergence of analytics and its relationship to other disciplines is explained simply and logically;
- the varied usage of the term analytics becomes understandable;
- it is not necessary to postulate the emergence of a new meta-discipline;
- there is an additional way of thinking about the quantitative decision sciences.
Good article. Too many people wanna study analytics just because it is popular and never try to understand the meaning of analytics. The explanation you proposed here is very clear and plausible. But how do we demonstrate our capabilities when applying for analytics job with non-business analytics degree? I feel that industrial engineering is not welcome when applying for business analytics job.
If you mean by an analytics job, working in the area of data science/big data, then I would say that industrial engineering and data science/big data are two different disciplines. While there is some overlap, the background required for each of them is different.
I would also say that a discipline that is hot right now, may not be so hot in the future. And, in any event, your future success will depend on finding work that is consistent with your interests and aptitudes, and for which you have passion.
Sorry I mean persuasive not plausible.