Adopting Analytics Culture: 1. Why Change Management? (1 of 7)

Cognitive Bias

PART OF A SERIES ON ADOPTING ANALYTICS CULTURE: 1 of 7

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  1. What does change management have to do with business analytics?

Along with feverish interest in business analytics (BA) and ‘Big Data’ has been an interest in how organizations can adopt ‘analytics culture’ to evolve what has been called ‘analytics maturity’ (Kiron and Shockley 2011; Kiron, Shockley et al. 2011). The notion of analytics maturity as an aspect of organizational culture recognizes that analytics skills and technologies can only drive value if the organization has a core orientation towards evidence-based decision making.

Cognitive Bias

Cognitive Bias

Analytics maturity, which involves adopting evidence-based decision processes, challenges the traditional top-down management paradigm.  In the typical large commercial organization, decision-making is still tied largely to the intuition-based judgment of ‘expert-managers’. Decisions are driven by expert judgment and often rely on intuition and heuristics (quick cognitive decision making shortcuts ‘baked into’ the structure of human mind).

While intuitive and heuristic decision making is not in-of-itself ‘wrong’, can be quite powerful, and will not necessarily lead to faulty decisions, recent research has explored the susceptibility of rapid, closed-loop decision making to well documented cognitive biases (http://en.wikipedia.org/wiki/List_of_cognitive_biases). As well, in complex organizational settings, the principal-agent problem exists, whereby incentives may be misaligned to the detriment of organizations and larger social interests (http://en.wikipedia.org/wiki/Principal%E2%80%93agent_problem). The U.S. Mortgage Crisis and subsequent global Financial Crisis in particular evidenced both heuristic biases and agency interests gone awry.

Particularly where circumstances are inherently complex, where the amount of variables and overlapping systems overwhelm the capacity of individuals, even experts will fall back upon heuristics which may lead to sub-optimal decisions.  Nobel prize-winning psychologist Daniel Kahneman’s work ‘Thinking, Fast and Slow’ goes into detail concerning the susceptibility of  individuals and groups to leaping to sub-standard conclusions given complex problem sets (http://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow).

A number of case studies concerning well-publicized decision failures have similarly pointed out the traps individuals and groups easily fall into when presented with complex problems. Examples include detailed case studies related to, among others, the Challenger and Columbia space shuttle disasters, the collapse of Long-Term Capital Management, derivatives-based investment melt-downs, the Dotcom investment bubble, intelligence failures surrounding 9/11, the lead-up to the Iraq War, friendly fire incidents in military theaters, the Hurricane Sandy disaster, and numerous recent trading scandals. The Great Courses company offers an informative set of lectures by Professor Michael Roberto addressing ‘The Art of Critical Decision Making’ which examines some of these cases in detail: http://www.thegreatcourses.com/tgc/courses/course_detail.aspx?cid=5932

The past two decades have been replete with dramatic decision failures surrounding complex, interconnected systems and scenarios. It is asserted that such scenarios, involving immense social and technical systems which incorporate a multiplicity of actors and variables, are more and more the status quo for modern, global business. The complex of globalization, financial complexity, multi-stakeholder politics, large datasets, intricate computer-based systems, and the proliferation of communication channels via real-time digital media are all combining to make individual and intuition-based judgment more and more susceptible to dramatic failures when intuition leads to a reliance on snap heuristics rather that process-oriented decision making best practices.

Given the status quo paradigm of the expert-manager, it is asserted that adopting analytics-based culture, or evidence-based decision making, amounts to an organizational management paradigm shift. Proposing to shift the methods and basis for organizational decision making from power hierarchies and vested managers proposes to change power dynamics:  the organizational contract regarding access to information, decision rights, assessment systems, and incentive schemes.  Analytics culture, at root, proposes new organizational architectures and management control system schemes.  It is further asserted that change management, a recognized practitioner discipline for changing organizational culture, is the clearest mechanism to adopting such paradigmatic organizational change.

Thus the discipline and methods of organizational change management, by nature, sit at the center of attempts to adopt and implement analytics-based decision making. Analytics maturity implies that organizations make decisions primarily based upon evidence enhanced by analytical insight. Implicit are business processes which connect data/business analytics, communication/information sharing, and decision making. The struggle to adopt the mechanisms of analytics-based decision making necessitates organizational change management. However, as such mechanisms involve decision-rights and access to information, attempts to reengineer existing processes often meet organizational resistance.

The adoption of business analytics-based decision making involves streamlining decision processes via aligning techniques, technologies, and stakeholders.  Particularly in terms of re-aligning stakeholder power networks, adopting analytics-driven decision processes can easily encounter organizational resistance. Emerging technologies and methods put strain upon organizational leadership: new decision processes must be married to organizational structure and culture to be truly viable. Improved understandings of decision making processes in organizational networks can help to improve the robustness of business decision making change programs.

REFERENCES

Kahneman, D. (2011). Thinking, Fast and Slow. New York: Farrar, Straus and Giroux.

Kahneman, D., & Klein, G. (2009). Conditions for Intuitive Expertise. American Psychologist, 64(6), 11.

Kiron, D., & Shockley, R. (2011). Creating Business Value with Analytics. MIT Sloan Management Review, 53(1), 10.

Kiron, D., Shockley, R., Kruschwitz, N., Finch, G., & Haydock, M. (2011). Analytics: The Widening Divide. MIT Sloan Management Review (Special Report), 21.

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About SARK7

Scott Allen Mongeau (SARK7) is an INFORMS Certified Analytics Professional (CAP) and a Data Scientist in the Cybersecurity business unit at SAS Institute. Scott has over 20 years of experience in project-focused analytics functions in a range of industries, including IT, biotech, pharma, materials, insurance, law enforcement, financial services, and start-ups. Scott is a part-time PhD (ABD) researcher at Nyenrode Business University. He holds a Global Executive MBA (OneMBA) and Masters in Financial Management from Erasmus Rotterdam School of Management (RSM). He has a Certificate in Finance from University of California at Berkeley Extension, a MA in Communication from the University of Texas at Austin, and a Graduate Degree (GD) in Applied Information Systems Management from the Royal Melbourne Institute of Technology (RMIT). He holds a BPhil from Miami University of Ohio. Having lived and worked in a number of countries, Scott is a dual American (native) and Dutch citizen. He may be contacted at: webmaster@sark7.com All posts are copyright © 2015 SARK7 All external materials utilized imply no ownership rights and are presented purely for educational purposes.

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