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Disciplines Related to Analytics

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As discussed in our previous post, there is much contention and debate about the definition of analytics. Secondly, there is clear evidence that analytics is fundamentally interdisciplinarian, involving a variety of traditional subject areas in its application. In order to better define analytics, this post will review the arguments made in the literature concerning the different disciplines involved.

Reviewing analytics degree courses from the UK & Ireland and worldwide, one immediate observations is that the courses themselves are included in a variety of departments or faculties. Of the 16 masters courses in the UK & Ireland (identified March 2013), 8 are run by computer science/engineering departments; 6 are within business or management schools; 1 is split between a computing and a business school; whilst 1 is within an Information System department (which is arguably somewhere between a business and a computing school). From searching masters degrees worldwide a similar split occurs, with additionally some universities listing courses within mathematics/statistics.

This quick analysis demonstrates that there is some discrepancy about the type of discipline analytics might be considered as, and that there are a variety of aspects involved that stem from different academic traditions. In order to better understand the field, and to help answer the question of "what is analytics?", this post will seek to create a taxonomy of the disciplines that inform it.

Evans (2012) consider there to be three core disciplines involved in analytics, as shown in figure 1.

Analytics: Operational Research, Statistics and Information Systems

Figure 1 – Disciplines Influencing Analytics (Evans, 2012)

Not only does their conceputalisation highlight the three disciplines they see as key (which, in the contexts of traditional disciplines, could be listed as statistics, operational research (OR), and Information Systems (IS)) but also it describes four sub-disciplines at the intersection of two or more of these: data mining, simulation and risk, "what if?", and data visualisation.

However, other authors have also suggested the inclusion of alternative disciplines. Cases have been made by the following authors for the inclusion of various fields in the make-up of analytics:

Authors Discipline / Field
Silvi et al (2010) Management Accounting and Finance
Varshney and Mojsilovic (2011)   Signal Processing
Azvine et al (2003) e-Business / e-Commerce
Aigner et al (2007) Psychology

Whilst argument can be made for the inclusion of each, it can also be said that such disciplines may not be appropriate for all businesses seeking to engage in analytics.

As can be seen there are some inconsistencies in the literature regarding the appropriate disciplines, and there is no real consensus on what should be included. Accordingly a ‘taxonomy’ has been created, essentially an extension of Evans' model to include other academic disciplines with may have a role, in attempt to further this debate (figure 2).

Business Analytics Disciplines

Figure 2 – A Taxonomy of Disciplines involved in Business Analytics

The various disciplines involved have been split into three overall sections: technological (e.g. computing), quantitative methods, and decision making. As with Evans' representation some of the disciplines are at an intersection between multiple sections. Similarly to their model, data mining (and machine learning) is conceived as overlapping between technology and quantitative methods. OR, though clearly with a very strong quantitative tradition, also has many aspects that are related to decision making and decision support, in particular soft OR, (e.g. Checkland, 1981; Rosenhead and Mingers, 2001). Finally ergonomics, which is the study or humans in systems and as such includes aspects such as Human Computer Interaction (HCI) and more behavioural studies, accordingly incorporates technology and decision support.

A second aspect is that there are effectively two-tiers. The inside tier (those with arrows directly connecting with analytics) includes the disciplines that directly impact on the practices, tools, and ideology of business analytics, whereas the outer tier shows their more established parent disciplines.

Whilst a case may be made for the inclusion of many of the disciplines discussed above, and others such as Economics (particularly the use of econometrics), this taxonomy is designed to apply to almost all businesses, whereas some other aspects may only apply to the use of analytics in specific domains. In summary, what this taxonomy and related literature does make clear is that analytics should be considered as essentially an interdisciplinerian field, a conclusion that has significant effects on attempts to design training and education for future analytics professionals.

Did we miss something? Have your say by leaving a comment below!

Special thanks to Professor James Evans for allowing us to use his diagram in this article.

 

REFERENCES

Aigner W, Bertone A and Miksch S (2007). Tutorial: Introduction to Visual Analytics. HCI and Usability for Medicine and Health Care: Lecture Notes in Computer Science, 4799: 453-456.

Azvine B, Nauck C and Ho C (2003). Intelligent Business Analytics – A Tool to Build Decision-Support Systems for eBusiness. BT Technology Journal, 21: 65-71.

Checkland P (1981). Systems Thinking, Systems Practice. Wiley: New York.

Evans JR (2012). Business Analytics: The Next Frontier for Decision Sciences, [Online]. Decision Line, 43: 4-6. Available from: http://www.decisionsciences.org/decisionline/Vol43/43_2/dsi-dl43_2_feature.asp, [accessed April 2014].

Rosenhead J and Mingers J (2001). Rational Analysis for a Problematic World: Problem Structuring Methods for Complexity, Uncertainty and Conflict (2nd Edition). Chichester: John Wiley & Sons.

Silvi R, Moeller K and Schlaefke M (2010). Performance Management Analytics - The Next Extension in Managerial Accounting. SSRN. Available from: http://ssrn.com/abstract=1656486, [accessed March 2013].

Varshney KR and Mojsilovic A (2011). Business Analytics Based on Financial Time Series. IEEE Signal Processing Magazine, 28: 83-93.

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