In the 12/17/14 INFORMS Today Podcast, Glenn Wegryn observes that analytics is divided into two distinct camps. He notes that they tend to come from different organizational backgrounds and he describes them in the following way:
- Data Centric – use data to find interesting insights and information to predict or anticipate what might happen;
- Decision Centric – understand the business problem, then determine the specific methodologies and information needed to solve the specific problem.
That analytics appears to be divided into distinct camps should not be surprising, since, as I explained in Confusion Over Analytics, analytics should be understood as a conceptual grouping of the quantitative decision sciences as a whole. Therefore, it is to be expected that disciplines within the quantitative decision sciences have distinctive backgrounds, methods and approaches.
The data centric/decision centric categorization can be a useful way to think about analytics, since two disciplines contained within the analytics conceptual grouping fit these categories perfectly: data science (data centric); operations research (decision/problem centric). Using this categorization, a framework can be constructed, within which, the various types of analytics, data science, and operations research can be related to each other in a logically consistent way.
Both common uses of the term analytics appear in the preceding diagram: to represent statistics and computer science and to represent all the quantitative decision sciences. This conceptual framework highlights a promising area for collaboration between data science and operations research (prescriptive analytics), while recognizing that most prescriptive quantitative analysis does not require intensive data analysis.
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.
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.