Operational Research & Analytics

Can Operational Research Breed the New Generation of Data Scientists?

Young Scientist at Work

There is little doubt that data scientists are becoming highly in demand, though sexy maybe an adjective too far! (Davenport and Patil, 2012). Whilst this term is clearly bound in a bubble of hype, and has a job description that may be asking too much of most candidates (see our previous post), such protests will mean little to those able to cash in on the reported six-figure salaries the title commands (e.g. Miller, 2013). So what does this mean for operational research (OR) specialists? Does data science offer the potential to boost their salaries or a threat to their job opportunities?

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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.

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What is Operational Research?

What is Operational Research?

Operational research (OR), also known as operations research or management science*, is the practice of using quantitative models and scientific methodologies in order to improve decision making. Although it may be seen to have its antecedents in the Scientific Management (or Taylorism), OR came to particular prominence during the second world war (for a far more detailed discussion of this early history see Kirby (2003)).

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    The ORATER Project, C/O MJ Mortenson, School of Business & Economics, Loughborough University, Leicestershire, LE11 3TU