Magazine Winter 2014 Research Feature

Should You Outsource Analytics?

Outsourcing analytics can offer benefits — but requires a carefully constructed relationship.

David Fogarty and Peter C. Bell Reading Time: 15 min 

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Image courtesy of Walmart

One of the factors that has contributed to the growth and success of Wal-Mart Stores Inc. is the strength of its analytics.

Image courtesy of Walmart

The surge of interest in big data has led to growing demand for analytics teams. Having big data capabilities can help companies become more efficient and improve overall competitiveness. Companies with superior data analytics capabilities have found ways to build long-term advantages. FedEx Corp., for example, has for years used its team of analytics professionals to create and maintain a competitive advantage through enhanced revenues and lower costs. One of the factors that has helped Wal-Mart Stores Inc. become one of the world’s largest and most successful retailers is the strength of its analytics.1

Assembling analytics teams, however, is difficult. For one thing, many companies lack the in-house knowledge and experience needed to put together an analytics team. What’s more, the labor market for analytics professionals has grown increasingly tight. Fortune recently reported, “Online help-wanted ads for data analysis mavens have shot up 46% since April 2011, and 246% since April 2009, to over 31,000 openings now, according to job-market trackers.”2 The shortage of analysts — particularly those capable of developing and leading world-class teams that can enable a company to create a competitive advantage from its data and analytics — is driving organizations to consider outsourcing their analytics activities. However, choosing analytics providers and structuring effective working relationships that deliver value require managers to have a clear understanding of what they’re looking for and the potential risks involved.

Analytics is the latest in a string of activities companies are outsourcing to business process organizations (BPOs). As the telecommunications boom that began in the late 1990s led to improving communications with emerging markets, Fortune 500 companies began shifting call centers offshore to locations such as India and the Philippines to take advantage of less expensive labor. India was especially attractive because of its large English-speaking population and highly educated labor force.3 Once call centers were established to handle customer service and telesales, companies began identifying other activities that might be suitable for outsourcing or offshoring: IT services, computer programming, legal research, application processing and accounting.

Analytics was a late arrival to the business-process-outsourcing menu of services.

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About the Authors

David Fogarty is an instructor at the University of Liverpool in the United Kingdom and has led business analytics organizations at Fortune 500 companies for more than 25 years. Peter C. Bell is a professor of management science at Ivey Business School at Western University in London, Ontario.

References

1. P.C. Bell and G.S. Zaric, “Analytics for Managers: With Excel,” (New York: Routledge, 2012).

2. A. Fisher, “Wanted: Data Scientists. No Math Chops? No Problem,” Fortune, May 10, 2013, http://management.fortune.cnn.com.

3. P.C. Bell and D. Fogarty, “Competing With Analytics by Taking Analytics Offshore,” Ivey Business School case no. 9B13E008 (London, Canada: Ivey Publishing, 2013).

4. A.C. Devata, R. Kumar and T. Stratopoulos, “Business Process Outsourcing: A Manager’s Guide to Understanding the Market Phenomenon,” chap. 5 in “Technology and Offshore Outsourcing Strategies,” ed. P. Brudenall (Houndmills, Basingstoke, United Kingdom and New York: Palgrave Macmillan, 2005), 97-115.

5. L.P. Willcocks, J. Hindle, D. Feeny and M. Lacity, “IT and Business Process Outsourcing: The Knowledge Potential,” Information Systems Management 21, no. 3 (summer 2004): 7-15.

6. Bell and Zaric, “Analytics for Managers.”

7. Bell and Fogarty, “Competing with Analytics.”

i. See H.K. Klein and M.D. Myers, “A Set of Principles for Conducting and Evaluating Interpretive Field Studies in Information Systems,” MIS Quarterly 23, no. 1 (March 1999) 67-94; and Z. Zainal, “Case Study as a Research Method,” Jurnal Kemanusiaan 9 (June 2007): 1-6.

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Comments (2)
Peter Klausz
First off I would like to say I really enjoyed this article.  Based on my experience as a management consultant.  Most companies don't have enough analytics (or at least complex analytics like predictive modeling) because they are unlikely to use insights from analytics to make a transformation. I always ask a final follow up question to executives after a conversation about analytics.  

"What are the chances that your company would change it's current operations if advanced analytics should you that you should?" 

More often that not they love the idea of analytics but dislike the potential outcomes.  In supply chain strategy this could mean moving major facilities to more appropriate locations and many businesses can't stomach it.
Peter Klausz
operationconsultinggroup.com
Giannigiacomelli
Great article. Based on my work as CMO and Product Innovation leader of Genpact (the GE spinoff that also happens to be one of the largest analytics players with global - including India - operations), your summary seems factually correct. 

I would however argue that you should consider a very significant key to understanding the strategic value of using outsourcing - that the analytics "process", just like many other business processes that have increasingly "gone global", can be broken down and parts of it done by outsourcing partners - complementing the in-house teams. 

This is by now a standard feature of advanced process operations. Large in-house shared services, while they do not operate at the same scale as the largest BPO houses, do the same - slice the process into components that can be delivered at scale globally by tapping into global resources. The preparation of the data, as well as the testing of a large range of hypotheses, is something that "industrialized analytics" can do. 

Of course, that is not the same as asking a few co-located and context-rich analysts to do creative jam sessions to solve for specific new problems. The two types of work are different - and both economically viable. Mixed them up makes little managerial and interpretive sense. 

I am happy to continue the conversation on gianni.giacomelli [at ] genpact.com