“Big Data is both an over-hyped buzzword and a real trend,” writes Gregory Piatetsky (@), President of KDnuggets, “reflecting the rapidly growing digitization of our world, and the amazing, and sometimes scary implications.” [“My Brief Guide to Big Data and Predictive Analytics for non-experts,” KDnuggets, February 2015] Piatetsky notes that data are just numbers, which are both meaningless and harmless if they lie fallow in a database. “What makes [Big Data] so powerful,” he asserts, “is Predictive Analytics (also called Data Mining or Data Science) — the ability to model our world, predict events, and make data-driven decisions, with accuracy approaching and sometimes even exceeding our human abilities.” Anytime someone discusses a machine using Big Data to predict the future, the scary factor surfaces. Some people think of movies like “Minority Report” whose plot involved a special police unit that arrested people based on predictions rather than actions. In the movie, however, it was human “Pre-cogs” not machines making the predictions. Regardless, there are some unsettling implications associated with machine-generated predictions in areas such as law enforcement, financial services, and healthcare.
For the most part, however, the benefits of predictive analytics far outweigh any potential downside. Eric Siegel (@), Founder of Predictive Analytics World & Text Analytics World, asserts, “Predictive analytics is a game-changer.” [“How Predictive Analytics Reinvents These Six Industries,” Datafloq, 11 February 2015] Siegel’s most interesting point, however, is that predictive analytics affect different industries in different ways. If different industries have one thing in common, however, Siegel believes it’s inefficiency. “I’m going to break it to you gently,” he writes. “Despite all the advanced technology lining your pocket, car, home, workplace — and even the proverbial cloud floating virtually above your head — the world is a remarkably inefficient, wasteful place.” If there is anything that companies are looking to eliminate, waste certainly must top the list. Waste, Siegel notes, comes in many forms — from junk mail that gets trashed before it is read to fraud that goes undetected. He believes that predictive analytics can address all sorts of inefficiency and waste in order “to improve the effectiveness of the frontline operations that define a functional society.” He elaborates:
“Millions of predictions a day improve decisions as to whom to call, mail, approve, test, diagnose, warn, investigate, incarcerate, set up on a date, and medicate. In this way, predictive analytics reinvents how our world’s primary functions are executed, across sectors. It boasts an intrinsic universality: A great, wide range of organizational activities can be improved with prediction — specifically, by way of predicting the behaviors and outcomes of people, the future of individual customers, debtors, patients, criminal suspects, employees, and voters. It’s that generality that makes this technology so potent and ubiquitous.”
Because predictive analytics have great potential for providing companies with a good return on investment, Siegel notes that over the next five years the predictive analytics market will grow to $5.2 to $6.5 billion per year. Below is a brief discussion of how predictive analytics are being (or will be) used in various economic sectors:
Mick Hollison (@mickhollison), Chief Marketing Officer at InsideSales, writes, “Predictive analytics is the use of statistics, machine learning, data mining, and modeling to analyze current and historical facts to make predictions about future events. Said another way, it gives mere mortals the ability to predict the future like Nostradamus or Carnac the Magnificent (but without funny hats).” [“Love or Hate It, Why Predictive Analytics Is The Next Big Thing,” Inc., 17 September 2014] He continues:
“I would argue that the most valuable use of predictive analytics is in marketing and sales. Imagine the ability to accurately predict not only who your best leads and prospects will be, but when and how will be the most effective ways to reach them and then to engage. This ability alone will empower marketers and salespeople in the coming seasons to be radically more productive and profitable than they are today. Used properly, it can transform the science of sales forecasting from a dart-throwing exercise to a precision instrument. … When these sciences coalesce and align, predictive analytics can allow marketers to target the best possible prospects with the most compelling offers with surgical accuracy.”
Hollison is so convinced that predictive analytics will be the next big thing for marketing that he thinks it will look like someone waved a magic wand over the marketing department. As he puts it: Marketing Sales = Magic. Not everyone is so enchanted with predictive analytics. For example, Corey Eridon (@) writes, “You can’t anticipate every unknown. And you have to account for that when you’re leaning on predictive analytics. The problem is, not enough people do. And that’s the problem with predictive analytics in marketing.” [“The Problem With Predictive Analytics,” Where Marketers Go to Grow, 11 June 2014] Predictive analytics aren’t perfect (otherwise their results wouldn’t be called predictions), but those results are still better than anything we’ve been able to achieve in the past.
Ingo Mierswa (@ingomierswa), CEO and Founder of RapidMiner, writes, “Given that predictive analytics software is increasingly easier to use, it’s no surprise the technology is being adopted more and more in the financial services industry. In general, it is applied there in two ways: 1) Against customer data; and, 2) Against internal and market data for risk management.” [“The Value of Predictive Analytics in Financial Services,” InformationWeek, 26 November 2014] Mierswa goes on to discuss how financial services companies use predictive analytics to determine things like customers churn; marketing and sales targets; credit worthiness; non-compliant trading activity; and fraud. He concludes, “Predictive analytics is providing even better and faster ROI in financial services.”
Mark Feffer (@markfeffer) reports that human relations departments generally lag behind other corporate departments in the use of data analytics. “According to Deloitte’s Global Human Capital Trends 2014 report,” he writes, “just 14 percent of HR departments are currently using data analytics. That compares to 77 percent of operations organizations, 58 percent of sales organizations and 56 percent of marketing organizations.” [“HR Moves toward Wider Use of Predictive Analytics,” Society for Human Resource Management, 6 October 2014] There are a number of areas in which predictive analytics could be useful, including: retention analytics and attrition risk; quality of hire analytics; leadership potential assessments; diversity and inclusion; learning analytics; safety analytics; payroll analytics; healthcare analytics; and workforce planning.
“In medicine,” writes Dr. Linda A. Winters-Miner, “predictions can range from responses to medications to hospital readmission rates. Examples are predicting infections from methods of suturing, determining the likelihood of disease, helping a physician with a diagnosis, and even predicting future wellness.” [“Seven ways predictive analytics can improve healthcare,” Elsevier Connect, 6 October 2014] No one likes to get sick or suffer from an infection after surgery. Rachel King (@sfwriter) reports, “The University of Iowa Hospitals and Clinics is reducing the rate of surgical infections by using predictive analytics. At the end of 2014, the rate of infections for patients of colon surgery dropped 58% over a two year period.” [“Analytics Predict Which Patients Will Suffer Post-Surgical Infections,” The Wall Street Journal, 11 February 2015] Winters-Miner concludes, “In developed nations, such as the United States, predictive analytics are the next big idea in medicine — the next evolution in statistics — and roles will change as a result.”
Bala Deshpande reports, “Manufacturing generates about a third of all data today, and this is certainly going increase significantly in the future.” [“How predictive analytics can shape manufacturing of the future,” Simafore, 1 October 2013] He adds, “Typically there are two broad segments within manufacturing where [predictive analytics] technologies provide a tangible return on investment. Within each segment, there are many different application areas, each of which requires the overall process of data mining.” The first segment is operations and the second segment is smart manufacturing. Within the operations segment, Deshpande notes that predictive analytics can be used for forecasting, cost & price modeling, warranty data analytics, and product development. In the smart manufacturing sector, predictive analytics can be used for fault detection, failure prediction, in-process verification, and management processes. The network that will make smart manufacturing possible is the Internet of Things. Deshpande concludes, “The above areas are only a small selection of examples where predictive analytics and big data can make an impact in manufacturing. Each one of them merits a serious study by themselves.”
Information Builders writes, “Predictive analytics provides a unique way for agencies to use internal and external data to improve all facets of their operations, such as communicating with constituents, planning and executing strategies, and allocating and managing staff and funds. It combines powerful, fully automated discovery and analysis technologies that enable government organizations to learn from historical data and prepare for the future.” [“Predictive Analytics for Federal Government,” Information Builders’ WebFOCUS”] Among the potential areas in which predictive analytics can be used, Information Builders lists: claim management, tax collection, and Medicare/Medicaid. Predictive analytics uses found in the corporate world could also prove useful to governments since government agencies also have to manage infrastructure, fleets of vehicles, and so forth.
Siegel concludes, “As predictive analytics’ adoption widens and deepens across sectors and across organizational functions, an inter-industry synergy emerges. Stories are shared between sectors — the lessons learned and proof-of-concepts viewed from neighboring industries inspire and catalyze growth. There’s a cyclic effect. And that is what the ‘big’ in big data really means — big excitement and big impact across industries.”