June 23-26, 2013
INFORMS Healthcare 2013
October 6–9, 2013
2013 INFORMS Annual Meeting
June 5-6, 2013
Customer Analytics Summit 2013
June 10-14, 2013
Predictive Analytics World
September 8-14, 2013
2013 ASE/IEEE International Conference on Big Data
Industry NewsSmart grid analytics ROI to exceed $121.8 billion globally by 2020
Utilities worldwide must maximize efficiency for constrained energy resources. Many are realizing the smart grid vision by using SAS Analytics and SAS Data Management to discover powerful insights buried in volumes of new data. SAS enables utilities to harness data for pinpoint control and monitoring, usage and demand forecasting, rapid diagnosis and repair, as well as predicting output from renewable sources such as solar and wind. For those capabilities, business analytics leader SAS is ranked No. 1 for smart grid analytics and data management/movement in the recently released utility industry report, “The Soft Grid 2013-2020: Big Data & Utility Analytics for the Smart Grid,” by GTM Research.Read More
Industry NewsFICO analytic cloud to enable real-time customer engagement
FICO will deliver its analytic-powered customer engagement services via the new FICO Analytic Cloud, for creating, customizing and deploying analytic-driven applications and services. Application developers, FICO clients and FICO partners will be able to take advantage of these services to rapidly create, execute and manage high-volume campaigns that engage customers in real-time with mass personalization across channels including brick-and-mortar, social and mobile.Read More
Special ArticlesStudy: Who can best manage ‘voice of the customer’?
Over the next three years, global organizations will make understanding and interacting with the customer their top priority. So says a new study from The Economist Intelligence Unit titled, “Voice of the customer: Whose job is it, anyway?” Yet only 56 percent of respondents to the survey, sponsored by SAS, believe their companies clearly understand the customer today.Read More
Why do large, once-successful companies fail?
The reasons are numerous, but the world and its problems are clearly more complex than ever, and complexity creates the need for analytics and informed decision-making.
By Gary Cokins
Numerous large, once-successful companies have failed in recent years. Some have gone bankrupt; others have been substantially reduced in size and fallen from an industry leadership position. Examples include Wang Labs, Digital Equipment, Borders and Blockbuster. What caused their failures? Were they not sufficiently analytical? Perhaps they had adequate information and analysis but made irrational decisions.
Franck Schuurmans, a guest lecturer at the Wharton Business School and a consultant for Decision Strategies International, has captivated audiences with explanations of why people make irrational business decisions. A simple exercise he uses in his lectures is to provide a list of 10 questions, such as “In what year was Mozart born?” The task is to select a range of possible answers such that you have 90 percent confidence that the correct answer falls in your chosen range.
Mozart was born in 1756, so, for example, you could narrowly select 1730 to 1770, or you could more broadly select 1600 to 1900. The range is your choice. Surprisingly, the vast majority of people get no more than five of the 10 questions correct. Why the poor performance? Most people choose too narrow bounds. The lesson is that people have an innate desire to be correct despite no penalty for being wrong.
Schuurmans’ research goes deeply into the nuances of cognitive psychology and the theories of bounded rationality that earned Herbert Simon the Nobel Prize in Economics in 1978. A key observation is that humans have limited rationality between their ears – our brains were designed to hunt prey. Typically, people defer to mental shortcuts from learning by discovery. The academic term is heuristics. For example, a man decides to take an umbrella when he leaves the house if the sky has dark clouds, but not if it is sunny. The clouds are probably enough for the umbrella decision – but is he 100 percent sure it will rain? No, but he’s probably sure enough for the umbrella decision, which raises the question: Do you know or just think you know?
This is an example of the limits of decision-making. Mental shortcuts, gut feel, intuition and so forth typically work – until problems get complex. I will return to this topic of complex problems, but first let’s discuss reasons why companies fail.
Why Companies Fail
Companies may feel invulnerable today but be aimless tomorrow. Do companies fail because their problems and opportunities have become very complex, but they address them with insufficient information to make decisions such as the umbrella example?
Almost half of the 25 companies that passed the rigorous tests for inclusion in Tom Peters and Robert Waterman’s 1982 book, “In Search of Excellence,” today no longer exist, are in bankruptcy or have performed poorly. What happened in the 30 years since the book was published?
And consider this: Of the original Standard and Poor’s (S&P) 500 list created in 1957, just 74, only 15 percent, are on that list today according to research from Professor Gary Biddle of the University of Hong Kong. Of those 74, only 12 have outperformed the S&P index average. Pretty grim. A few years from now will the sequel to the popular book, “Good to Great” by Jim Collins reveal the praised companies in the original book as laggards?
Perhaps the explanation is that when an organization is enjoying success, it breeds adversity to taking wise and calculated risks. They are too confident that what has worked in the past will continue to work in the future. But each new day requires making strategic adjustments to anticipate continuously changing customer needs and counter-tactics by competitors. Risk management is about balancing risk appetite with risk exposure. If there is not enough risk-taking appetite, then performance will eventually suffer. If the risk appetite is excessive, well, the current global fiscal crisis is evidence of that outcome. How can an organization create sustainability for its long-term performance? What role might embracing analytics play in mitigating risks and providing a competitive edge?
In Sydney Finkelstein’s book “Why Smart Executives Fail,” he observes that the cause of failure is not lack of intelligence – executives are typically quite smart. Failure is not necessarily due to unforeseeable events. Companies that have failed often knew what was happening but chose not to do much about it. Nor is failure always the result of taking the wrong daily actions.
Finkelstein’s explanation involves the attitude of executives. This includes a breakdown in their reasoning and strategic thinking, as well as a failure to create a culture for metrics and deep analysis.
As mentioned, prominent examples of failure are Wang Labs and Digital Equipment. Wang Labs failed in part because it specialized in computers designed exclusively for word processing and did not foresee general-purpose personal computers (PCs) with word processing software in the 1980s, mainly developed by IBM. Digital Equipment was satisfied with its dominance in the core minicomputer market, which it was first to introduce. However, Digital was slow to adapt its product line to the new markets for personal computers. The company’s entry into the PC arena in 1982 was a failure, and later PC collaborations with Olivetti and Intel achieved mixed results.
Often no one challenges the status quo and asks the tough questions. Delusion and fear of the unknown can develop, affecting how organizations handle key relationships with customers and suppliers. When it comes to considering whether to adopt advanced business analytics, or whether to implement and integrate the various component methodologies that constitute analytics-based enterprise performance management (EPM), decision-makers are faced with two choices: do it or not do it. Many organizations ignore the fact that the choice to not act, which means to continue with the status quo and to perpetuate making decisions the way they currently are, is also a decision.
In many cases, executives believe that if a control system is in place, it will do the job for which it was intended. However, in many organizations, systems and policies are constructed for day-to-day transactions but not for robustly analyzing the abundance of raw data to make sense of it all. Sustainability is based on transforming data into analyzable information for insights and decision-making. This is where business intelligence, business analytics and analytics-based enterprise performance management systems fit in.
Increasing ROI from Information Assets
Schuurmans observed that mental shortcuts work except when problems become complex. When problems do get complex, then a new set of issues arise. Systematic thinking is required. What often trips people up is that they do not start by framing a problem before they begin collecting information that will lead to their conclusions. There is often a bias or preconception. One seeks data that will validate one’s bias. The adverse effect, as Schuurmans describes it, is “We prepare ourselves for X and Y happens.” By framing a problem, one widens the options to formulate hypotheses.
How is this relevant for applying business analytics to improve organizational performance? A misconception to information technology specialists is that they equate applying business intelligence (BI) technologies with query and reporting techniques such as data mining.
In practice, experienced analysts don’t use BI as if they were searching for a diamond in a coal mine. They don’t flog the data until it confesses with the truth. Instead, they first speculate that two or more things are related or that some underlying behavior is driving a pattern seen in various data. They apply business analytics more for confirmation than for random exploration. This requires analysts to have easy and flexible access to data, the ability to manipulate the data and software to support their investigative process.
Without initial problem framing and a confirmatory approach, mistakes are inevitable. Sadly, as Schuurmans observed, many do not learn from their mistakes, but repeat them with more gusto.
In his book, “Predictably Irrational,” author Dan Ariely observes, “We are all far less rational in our decision-making than standard economic theory assumes. Our irrational behaviors are neither random nor senseless – they are systematic and predictable. So wouldn’t economics make a lot more sense if it were based on how people actually behave? That simple idea is the basis of behavioral economics.”
I would expand on Ariely’s quote by asking a comparable question: Wouldn’t getting a return on investment from an organization’s treasure trove of stored raw and transactional data be greater and more meaningful if we properly applied business analytics?
The Emerging Need for Analytics
With today’s uncertain recovery from the global recession, the stakes have never been higher for managers to make better decisions with analyzable information. Companies that successfully use their information will out-think, outsmart and out-execute their competitors. High-performing enterprises are building their strategies around information-driven insights that generate results from the power of analytics of all flavors, such as segmentation and regression analysis and especially predictive analytics. They are proactive, not reactive.
The October 2011 issue of the Harvard Business Review includes an, “Learning to Live with Complexity,” that sheds additional light on the topic of complexity. Its authors, Gockce Sargut and Rita Gunther McGrath, note the difference between something being merely complicated and genuinely complex. Think of systems. Complicated systems have many moving parts, like a wristwatch with gears, but they operate in patterned ways. In contrast, complex systems have patterned ways, but the interactions – think variables – are continually changing. In the former one can usually predict outcomes. The math may be easy with linear relationships. In the latter, such as air traffic control, weather and aircraft maintenance delays cause changes in the constant interactions with numerous variables. To make better decisions, the modeling and analytics need to be adaptive and flexible. They need to have self-learning capabilities to get increasingly smarter.
Executives are human and can make mistakes, but in company failures, these are not simply minor misjudgments. In many cases, their errors are enormous miscalculations that can be explained by problems in leadership. Regardless of how decentralized some businesses might claim to be in their decision-making, corporations can be rapidly brought to the brink of failure by executives whose personal qualities create risks rather than mitigate them. In Finkelstein’s book, he observes that these flaws can be honorable – such as with CEOs like An Wang of Wang Labs – or less than honorable, as was the case with rogue CEOs such as Dennis Kozlowski of Tyco, Ken Lay of Enron, John Rigas of Adelphia and Steve Hilbert of Conseco.
To sustain long-term success, companies need leaders with vision and inspiration to answer, “Where do we want to go?” Then, by communicating their strategy to managers and employees, they can empower their workforce with analytical tools to correctly answer, “How will we get there?” This goes to the heart of analytics. Employees make hundreds, possibly thousands, of decisions every day, such as pricing, customer targeting and freight distribution routing. Incrementally better small decisions add up and may contribute more to the financial bottom line impact than the few big decisions made by executives.