Successful competitive differentiation rarely occurs by accident. It comes from careful deliberation and disciplined execution. Big Data and analytics offer considerable opportunities to improve both competitive intelligence and operational differentiation.
For example, McKinsey¹ surveyed twelve emerging technological trends likely affect profound disruption of industries and cultures. Analytics was not explicitly mentioned. Analytics-based capabilities are however enabling contributors to most of them.
Analytics similarly enables Porter's² innovation "modes" leading to competitive advantage. Teece's³ dynamic capabilities "microfoundations" of sustainable competitive advantage are similarly aided by analytics. I defer elaboration on this assertions to subsequent installments.
Although caution should be applied in treating analytics as "just another technology"⁴, frameworks for strategic alignment of technology in fact lend themselves to many aspects of analytics. Ross' method for treating enterprise architecture as an essential aspect of strategy⁵ exemplifies. Merrifield's method for capabilities analysis⁶ similarly lends itself to identifying hypotheses testable by analytics.⁷
My discussion here considers yet another framework. Nolan⁸ considers fundamental questions about the role of IT in corporate strategy. He uses a strategic-impact grid to derive answers. His framework lends itself to answering similar questions about analytics.
For example, McKinsey¹ surveyed twelve emerging technological trends likely affect profound disruption of industries and cultures. Analytics was not explicitly mentioned. Analytics-based capabilities are however enabling contributors to most of them.
Analytics similarly enables Porter's² innovation "modes" leading to competitive advantage. Teece's³ dynamic capabilities "microfoundations" of sustainable competitive advantage are similarly aided by analytics. I defer elaboration on this assertions to subsequent installments.
Although caution should be applied in treating analytics as "just another technology"⁴, frameworks for strategic alignment of technology in fact lend themselves to many aspects of analytics. Ross' method for treating enterprise architecture as an essential aspect of strategy⁵ exemplifies. Merrifield's method for capabilities analysis⁶ similarly lends itself to identifying hypotheses testable by analytics.⁷
My discussion here considers yet another framework. Nolan⁸ considers fundamental questions about the role of IT in corporate strategy. He uses a strategic-impact grid to derive answers. His framework lends itself to answering similar questions about analytics.
A strategic-impact grid for analytics.
Figure 1 shows Nolan's strategic-impact grid adapted for analytics. Nolan, et al, focus on two dimensions of IT's contribution to a business. Figure 1 paraphrases these as:- Nature of analytics' contribution to competitive advantage; and
- Tolerance for shortfalls in information availability, accuracy.
Following Nolan, I discretize this two-dimensional space into four regions. This obviously represents a simplification. For instance, analytics' contribution to a particular firm's competitive approach may fall somewhere on a continuum between "Offensive" and "Defensive". Consistent however with Box' philosophy on modeling,⁹ a model such as ours need not necessarily capture all aspects of a phenomenon with arbitrary precision in order to be useful for explanation.
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| Figure 1 — Strategic-impact grid for analytics: Organizational modes and role of analytics in their operations. After Nolan.⁸ |
The "nature-of-contribution" dimension answers the question, "How does analytics contribute to my competitive differentiation?" We say that our analytics is competitively offensive if it provides the basis for competitive differentiation. If alternatively analytics is necessary simply to maintain competitive parity, its contribution is categorized as defensive.
The "tolerance for shortfall" pertains to the business-criticality or -essentiality of the analytics capability. I borrow here a useful concept from U.S. Federal policy for the acquisition of IT.¹⁰ Low-tolerance analytics capabilities contribute directly to the delivery of the organization's primary offering to customers. The organization's tolerance for lapses in analytics-capability availability or accuracy is low if such lapses adversely impact meeting commitments to customers.
The four modes of analytics contribution to strategy.
Subdividing our two-dimensional strategy-impact model in Figure 1 into four quadrants yields four modes of strategic contribution by analytics.
Strategic Mode. The strategic mode applies to firms for which analytics provides a basis for competitive differentiation. Analytics-derived information contributes directly differentiation the organization's product or service. Strategic-mode firms develop and exploit information asymmetries.¹¹ Three obvious approaches to information asymmetry occur:
- Access to data not available to competitors (e.g., Google);
- Extraction of superior economic utility from information (e.g., Walmart); or
- Differentiated ability to extract information from data that are generally available to others (e.g., Kaspersky lab).
Factory Mode. Factory-mode organizations rely upon analytics for business-essential functions. They do not however necessarily achieve information asymmetries. Factory-mode firms seek to maintain at least information parity.
Distinction between information asymmetry and information parity is the essential point here. Analytics-based capabilities are effectively "table stakes" for survival many industries. Business-criticality may lead such firms to claim that they "compete on analytics". Analytics-based capabilities are not however basis for competitive differentiation. This logic mirrors the argument from Carr's controversial 2003 article "IT doesn't matter."¹²
On what do factory-mode organizations base their analytics capabilities? They tend to apply mainstream, out-of-the-box tools to generally available data sources. Cloud-based analytics tools¹⁵ commoditize many powerful data-mining capabilities. Consequently, maintaining business-essential information parity is no longer a luxury in many industries.
Turnaround Mode. Turnaround-mode organizations use analytics to reposition themselves strategically. Despite the location of the turnaround-mode quadrant in Figure 1, these organizations may either try for information parity or information asymmetries.
Introspective diagnostics or extrospective competitive baselining may employ capabilities yielding information parity. Experimenting with new offerings or delivery models¹⁶ may entail exploration of operational approaches based on information asymmetries.
Support Mode. Not all industries necessarily lend themselves to differentiation based on information asymmetries. Nolan⁸ singled out industries with a creative focus as support-mode in IT terms. This often applies to analytics as well.
Recent research¹⁷ regarding the fashion industry suggests weaknesses in the ability of mainstream analytics to anticipate demand in the financial industry. Limitations in the abilities of predictive algorithms to anticipate movements by financial markets similarly exemplify scenarios in which analytics-derived information asymmetries remain problematic.
Analytics-based competitive differentiation comes down to information asymmetry.
Analytics capabilities are becomingly increasingly ubiquitous. This follows the near-pervasive penetration of information technologies a decade and a half ago. Technology-based capabilities — having achieved ubiquity — yield only tenuous bases for competitive differentiation. They become necessary for competitive parity. But in terms of differentiation, they cease to matter.
Really, how do we achieve information asymmetry? The next Quant's Prism installment addresses this through a variation on a framework by Davenport.¹⁸ I introduce a strategic-alignment framework focused on linking data science to fundamental phenomenology of the business.
References
¹ "Disruptive technologies: Advances that will transform life, business, and the global economy," McKinsey Global Institute, McKinsey & Company, May 2013, http://goo.gl/HWV6ly.
² M. Porter, Competitive Advantage of Nations, New York: Free Press (Macmillan), 1990, pp. 45 - 47, http://goo.gl/ntAQL8.
³ D. Teece, Dynamic Capabilities, Oxford, UK: Oxford University Press, 2009, Kindle edition, Loc 62 - 639, http://goo.gl/yAOa03.
⁴ D. A. Marchand and J. Peppard, "Why IT fumbles analytics", Harvard Business Review, Jan - Feb 2013, https://goo.gl/fbfPHe.
⁵ J. W. Ross, P. Weill, and D. C. Robertson, Enterprise Architecture as Strategy, Boston: Harvard Business School Publishing, 2006, http://goo.gl/YX1cVQ.
⁶ R. Merrifield, J. Calhoun, and D. Stevens, "The next revolution in productivity," Harvard Business Review, June 2008, https://goo.gl/j96ktR.
⁷ T. Davenport, "Keep up with your quants," Harvard Business Review, July - Aug 2013, https://goo.gl/sY5HTo.
⁸ R. Nolan and F. W. McFarlan, "IT and the Board of Directors," Harvard Business Review, October 2005, https://goo.gl/AKbG7P.
⁹ G. E. P. Box, "Science and statistics," Journal of the American Statistical Association, December 1976, pp. 791 - 798, http://goo.gl/x6Fe6B.
¹⁰ "Title 40/Clinger-Cohen Act (CCA) Compliance Table", Defense Acquisition Guidebook, https://goo.gl/5VUQ2V.
¹¹ U. Birchler and M. Bütler, Information Economics, New York: Routledge (Tayor & Francis), 2007, http://goo.gl/5hB9Ra.
¹² N. Carr, "IT doesn't matter", Harvard Business Review, May 2003, pp. 41 - 49, https://goo.gl/7gCYJX.
¹³ J. King, "How analytics helped Ford turn its fortunes," Computerworld, December 2, 2013, http://goo.gl/C8h7tS.
¹⁴ C. Boulton, "Navistar CIO looks to big data analytics to fuel turnaround", November 30, 2015, http://goo.gl/6Sbpo8.
¹⁵ D. Henschen, "10 Cloud Analytics & BI Platforms For Business", InformationWeek, January 22, 2015, http://goo.gl/IX7av9.
¹⁶ S. Thomke and J. Manzi, "The discipline of business experimentation," Harvard Business Review, December 2014, https://goo.gl/OLRNsF.
¹⁷ M. Seifert, et al, "Effective judgmental forecasting in the context of fashion products," Journal of Operations Management, Elsevier, http://goo.gl/bRt88Y.
¹⁸ T. Davenport, Analytics at Work, Boston: Harvard Business School Publishing, 2010, http://goo.gl/nfYtlo.
² M. Porter, Competitive Advantage of Nations, New York: Free Press (Macmillan), 1990, pp. 45 - 47, http://goo.gl/ntAQL8.
³ D. Teece, Dynamic Capabilities, Oxford, UK: Oxford University Press, 2009, Kindle edition, Loc 62 - 639, http://goo.gl/yAOa03.
⁴ D. A. Marchand and J. Peppard, "Why IT fumbles analytics", Harvard Business Review, Jan - Feb 2013, https://goo.gl/fbfPHe.
⁵ J. W. Ross, P. Weill, and D. C. Robertson, Enterprise Architecture as Strategy, Boston: Harvard Business School Publishing, 2006, http://goo.gl/YX1cVQ.
⁶ R. Merrifield, J. Calhoun, and D. Stevens, "The next revolution in productivity," Harvard Business Review, June 2008, https://goo.gl/j96ktR.
⁷ T. Davenport, "Keep up with your quants," Harvard Business Review, July - Aug 2013, https://goo.gl/sY5HTo.
⁸ R. Nolan and F. W. McFarlan, "IT and the Board of Directors," Harvard Business Review, October 2005, https://goo.gl/AKbG7P.
⁹ G. E. P. Box, "Science and statistics," Journal of the American Statistical Association, December 1976, pp. 791 - 798, http://goo.gl/x6Fe6B.
¹⁰ "Title 40/Clinger-Cohen Act (CCA) Compliance Table", Defense Acquisition Guidebook, https://goo.gl/5VUQ2V.
¹¹ U. Birchler and M. Bütler, Information Economics, New York: Routledge (Tayor & Francis), 2007, http://goo.gl/5hB9Ra.
¹² N. Carr, "IT doesn't matter", Harvard Business Review, May 2003, pp. 41 - 49, https://goo.gl/7gCYJX.
¹³ J. King, "How analytics helped Ford turn its fortunes," Computerworld, December 2, 2013, http://goo.gl/C8h7tS.
¹⁴ C. Boulton, "Navistar CIO looks to big data analytics to fuel turnaround", November 30, 2015, http://goo.gl/6Sbpo8.
¹⁵ D. Henschen, "10 Cloud Analytics & BI Platforms For Business", InformationWeek, January 22, 2015, http://goo.gl/IX7av9.
¹⁶ S. Thomke and J. Manzi, "The discipline of business experimentation," Harvard Business Review, December 2014, https://goo.gl/OLRNsF.
¹⁷ M. Seifert, et al, "Effective judgmental forecasting in the context of fashion products," Journal of Operations Management, Elsevier, http://goo.gl/bRt88Y.
¹⁸ T. Davenport, Analytics at Work, Boston: Harvard Business School Publishing, 2010, http://goo.gl/nfYtlo.
