Operational Research & Analytics

Operational Research & Analytics: A Marriage Made in Maths?

Analytics and Operational Research

Many authors have commented on the synergies and relationship between Operational Research (OR) and analytics (as discussed in our previous article). Liberatore and Luo (2010) describe the growing interest in analytics as an opportunity to “promote the [OR] profession and expand its reach”. This post will further explore the relationship between the two, and attempt to assess the extent to which the OR community has embraced this opportunity.

Note: you may wish to read the two previous posts in this series that discuss the definition of both Operational Research and business analytics.

The relationship between OR and analytics has been the discussed in many articles, blogs and books. In their representation of analytics, and a company’s progression towards "analytics maturity", Lustig et al (2010) argue that OR-type methods represent the most "advanced" form of analytics. A representation of their argument is shown in figure 1.

The Path to Analytical Maturity

Figure 1 – The Three Forms of Analytics and the Path to Maturity (adapted from: Lustig et al (2010) and Agosta (2004))

Whilst there are some issues with this formulation (which will be discussed in a forthcoming article), it has become a widely accepted representation of analytics. In a survey of the INFORMS membership (the US equivalent of the OR Society), Liberatore and Luo (2011) report that 82% of respondents either agreed or strongly agreed with this model as a definition of analytics.

There are two aspects of this model that are of particular significance to the OR community, and the purpose of this article. Firstly OR-type methods are held as the "highest" or most advanced of the techniques discussed. By default, the popularisation of this model is likely to increase exposure and interest in the OR discipline. Secondly, in the phrasing of each as questions ("why is this happening?", "what will happen next?", and "what should we do next?") makes each maturity level easily comprehendible.

This example shows both the relationship between analytics and OR, as well as how this relationship can benefit OR. However, there is some evidence that there could be more opportunities for the OR community to strengthen this association.

Within the academic community as a whole research has been shown to have vastly increased in the last two years. Chen et al (2012) report 126 articles were published in 2011 containing the phrase “business analytics” in title or abstract; as many as had been produced in the ten years prior (252 in total). As a comparison, 1,200 academic journals listed in the IAOR (International Abstracts in Operations Research) were searched (all of which are associated with OR). Compared with the 252 Chen et al identified, only 29 were found in these OR-related journals. From this, 11 were published in INFORM’s Interfaces, and 9 in the interdisciplinary Decision Support Systems (nearly 70% of the total output). However, more positively, over 40% were released in 2012, possibly indicating increases in such research.

However, even if the amount of OR research specifically referencing analytics may not be in keeping with the remainder of the academic community, there has been considerable activity from both INFORMS and the OR Society. INFORMS publishes a bi-monthly magazine (the appropriately named Analytics Magazine), has run several analytics conferences, and has recently released a certificate in analytics. The OR Society in the UK has recently formed a new network for analytics professionals, as well as a quarterly publication, and are hosting an event on analytics and big data.

Analytics and OR can have much to offer each other and a reciprocal relationship can both increase awareness for each, as well as providing new methods and approaches which can benefit businesses seeking to improve its decision making. Whilst there is scope for further OR research into analytics, there are indications that the community is embracing the opportunities that analytics affords, along with the challenges, opportunities, tools and technologies of the analytics era.

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REFERENCES

Agosta L (2004). Data Mining is Dead – Long Live Predictive Analytics. DM Review, 14: 37-38.

Chen H, Chiang RHL and Storey VC (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36: 1165-1188.

Liberatore M and Luo W (2010). The Analytics Movement: Implications for Operations Research. Interfaces 40: 313-324.

Liberatore M and Luo W (2011). INFORMS and the Analytics Movement: The View of the Membership. Interfaces 41: 578-589.

Lustig I, Dietrich B, Johnson C and Dziekan C (2010). The Analytics Journey. Analytics Magazine, November/December 2010, pp11-13. Available from: http://viewer.zmags.com/publication/c5e7ab79#/c5e7ab79/12, [accessed April 2013].

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