During the run-up to 2012 U.S. presidential election, a 34-year-old statistician named Nate Silver “used an elaborate series of calculations to correctly call the outcome in all 50 states.” At the time, this was considered an incredible achievement because other pundits were predicting it would be the “tightest election race in decades.” [“The man you can count on,” Daily Mail, 8 November 2012] Silver’s achievement demonstrated just how far computer-assisted forecasts have come. If you want to know where some analysts believe forecasts are heading, you might want to look to Amazon, which, in December 2013, was granted a patent “for what the company describes as ‘anticipatory shipping,’ or a way of initiating the delivery process before a customer even clicks buy.” [“Amazon Wants to Send You Stuff Before You’ve Even Decided to Buy It,” by Matt Vella, Time, 18 January 2014]
“A momentous shift in business strategy is taking place, writes Dana Gardner. “Big Data, cloud computing and mobility, in tandem, are having an enormous impact on how businesses must act — and react — across their markets. The agility goal of real-time responses is no longer good enough. What’s apparent across more business ecosystems is that businesses must do even better, to become so data-driven that they extend their knowledge and ability to react well into the future. In other words, we’re now all entering the era of the predictive business.” [“The Dawning of the Predictive Business Era,” E-Commerce Times, 7 October 2013] During an interview, Tim Minahan, the chief marketing officer for SAP Cloud, told Gardner, “Predictive business isn’t just about advanced analytics. It’s not just about Big Data. That’s certainly a part of it, but just knowing something is going to happen, just knowing about a market opportunity or a pending risk just isn’t enough.” Minahan explained:
“You have to have that capacity and insight to assess a myriad of scenarios to detect the right course of action, and then have the agility in your business processes, your organizational structures, and your systems to be able to adapt to capitalize on these changes. Too often, we get enamored with the technology side of the story, but the biggest change that’s going to occur in business is going to be the culture change. There’s the need to adapt to this new Millennial workforce and this new empowered customer, and the need to reach this new emerging middle-class around the world. In today’s fast-paced business world, companies really need to be able to predict the future with confidence, assess the right response, and then have the agility organizationally and systems-wise to quickly adapt their business processes to capitalize on these market dynamics and stay ahead of the competition. They need to be able to harness the insights of disruptive technologies of our day — technologies like social, business networks, mobility, and cloud — to become this predictive business.”
It should be obvious from Minahan’s discussion that predictive business isn’t about certainty. There are simply too many factors and too much complexity for even the smartest of computers to predict anything with certainty. Asad Khan, a senior lecturer at Monash University, agrees that one “area that has greatly benefited from big data analytics is demand-driven forecasting where decisions are formed from analyzing huge volumes of data.” [“Explainer: What is big data?” The Conversation, 27 January 2014] He also believes, “The potential for forecasting will continue to increase dramatically as location and other field data from sensors are included.” Nevertheless, he writes, “Algorithms that will tap into the full potential of big data are not quite ready yet, particularly if sensory and device data are to be included within more conventional information systems such as online shopping and other web-based services.” He concludes, “The competition to draw more accurate conclusions from universally-available big data on the internet is increasing.”
Khan’s mention of sensors points us to another arena in which predictive business is going to be a boon. That arena involves the Internet of Things (IoT), a vast network over which machines will communicate. For years companies have been trying to ensure that equipment remains safe and functioning. Based on past repair records, they were able to establish preventative maintenance schedules. The situation improved when computers came along and companies could perform activities like vibration monitoring to sense how well some machinery was operating. In the age of embedded sensors that allow operating machinery to communicate over the IoT with cognitive computing systems, machines will be able to assess their own health and alert maintainers that something needs to be done. As noted above, however, the age of predictive business is about much more than keeping machines running safely. Robert Enslin, President of Global Customer Operations for SAP, notes, “Software is helping engineers create more effective diagnostic tools to heal and prevent illness and disease. It is enabling manufacturers to design, build, test and perfect products on a PC before moving to production. It is enabling students to learn across great distances through virtual classrooms.” [“The age of predictive business,” Financial Times, 23 January 2014] Enslin believes that all of these activities are “only a warm-up act for what’s to come.” He explains:
“Today, the world is on the verge of a whole new era of human achievement — one in which software joined to digital technology and lightning-fast computers will enable organizations of all kinds to not just understand the past, but to predict the future with astonishing accuracy. This is what we are calling the ‘Age of Predictive Business.’ For early-adopter businesses and entrepreneurs who embrace this new technology, the coming Age of Predictive Business can create fresh insights into customers, new strategies to intractable problems, and a new path to competitive advantage that will enable them economies to outperform their global peers while impacting the world for the better. The secret at the centre of this new era is the mountain of data being created by digital devices of every kind, and how it is being used to assess, understand and improve our world in ways that our predecessors could only have dreamt about.”
Lest you think that Enslin is simply being hyperbolic about the age of predictive business, he offers the following analysis:
“Studies show that companies that embrace the promise of predictive business outperform their competitors by an average of 20 per cent. The McKinsey Global Institute estimates that retailers who harness the power of ‘big data’ may be able to increase their operating margins by over 60 per cent. Similarly, the Economist Intelligence Unit found that companies that have embraced a data-driven culture are three times more likely to rate themselves as substantially ahead of their peers in financial performance.”
Jean Paul Issen insists, “Predictive analytics is one of the most powerful approaches that companies can use to compete and win with analytics.” [“What is predictive analytics?” The Knowledge Exchange, 19 December 2012] He continues:
“Anticipating customer needs, preferences, attitudes and behaviors can help marketers to make informed decisions and increase positive outcomes of their strategies. As the ultimate goal of predictive analytics is to anticipate the outcome of future events, business applications for predictive analytics are broad. They include predicting sales and marketing customer behaviors, fraudulent insurance claims, military supply chain problems, customer attrition, and the spread of the infections, such as the H1N1 flu.”
Eric Siegel calls predictive analytics a concept that “is dazzling yet daunting.” [“Predictive Analytics Delivers Value Across Business Applications,” Predictive Analytics World] He knows that predictive systems are a long way from being able to deliver certainty; but, he doesn’t care. He insists “the predictions don’t actually have to be all that accurate to deliver great value to your business.” The reason that certainty isn’t critical is because most companies are looking for insight and have traditionally had to rely on hindsight. Predictive analytics can provide insights even if they can’t provide certainty. The late Soviet Admiral of the Fleet, Sergey Gorshkov is often credited with having said, “Better is the enemy of good enough.” Later, Harvard Business School professor Clayton Christensen demonstrated the truth of the adage in his book The Innovator’s Dilemma. In the age of predictive business, companies that are waiting until forecasting systems get better to jump on the bandwagon may find themselves missing the wagon altogether.
