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I originally published this article in Analytics-Magazine.org. The article relates to my book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie...
Every day’s a struggle. I’ve faced some tough challenges such as which surgery to get, how to invest for my business and even how to deal with identify theft. With so much stuff coming at me from all angles, daily prosperity relies on spam filters, Internet search engines, and personalized music and movie recommendations. My mailbox wonders why companies still don’t know me well enough to send less junk mail.
These predicaments matter. They can make or break your day, year or life. But what do they all have in common?
These challenges – and many others like them – are best addressed with prediction. Will the patient’s outcome from surgery be positive? Will the credit applicant turn out to be a fraudster? Will the investment fail? Will the customer respond if mailed a brochure?
There’s another angle. Beyond benefiting you and I as individuals, prediction bestows power upon an organization: Big business secures a competitive stronghold by predicting the future destiny and value of individual assets.
For example, in the mid-1990s, Chase Bank witnessed a windfall predicting mortgage outcome. By driving millions of transactional decisions with predictions about the future payment behavior of homeowners, Chase bolstered mortgage portfolio management, curtailing risk and boosting profit.
Introducing ... the Clairvoyant Computer.
Making such predictions poses a tough challenge. Each prediction depends on multiple factors: the various characteristics known about each patient, each homeowner and each e-mail that may be spam. How shall we attack the intricate problem of putting all these pieces together for each prediction?
The solution is machine learning; computers automatically discovering patterns and developing new knowledge by furiously feeding on modern society’s greatest and most potent unnatural resource: data.
Data can seem like such dry, uninteresting stuff. It’s a vast, endless regiment of recorded facts and figures. It’s the unsalted, flavorless residue deposited en masse as businesses churn away.
But the truth is that today’s big data embodies a priceless collection of experience from which to learn. Every medical procedure, credit application, Facebook post, movie recommendation, fraudulent act, spammy e-mail and purchase of any kind is encoded as data and warehoused. This veritable Big Bang delivers a plethora of examples so great in number only a computer could manage to learn from them.
This learning process discovers and builds on insightful gems such as:
Machine learning develops predictive capabilities with a form of number-crunching, a trial-and-error learning process that builds upon statistics and computer science. In commercial, industrial and government applications – in the real-world usage of machine learning to predict – it’s known as:
Predictive analytics — Technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions.
“The powerhouse organizations of the Internet era, which include Google and Amazon ... have business models that hinge on predictive models based on machine learning.”
– Professor Vasant Dhar, Stern School of Business, NYU
Every important thing a person does is valuable to predict, including: consume, work, love, procreate, vote, mess up, commit a crime and die. Here are some examples:
Organizations of all kinds benefit by applying predictive analytics, since there’s ample room for operational improvement; organizations are intrinsically inefficient and wasteful on a grand scale. Marketing casts a wide net; “junk mail” is marketing money wasted and trees felled to print unread brochures. An estimated 80 percent of all e-mail is spam. Risky debtors are given too much credit. Applications for government benefits are backlogged and delayed.
With predictive analytics, millions of decisions a day determine whom to call, mail, approve, test, diagnose, warn, investigate, incarcerate, set up on a date and medicate. By answering this mountain of smaller questions, predictive analytics combats financial risk, fortifies healthcare, conquers spam, toughens crime-fighting, boosts sales and may in fact answer the biggest question of all: How can we improve the effectiveness of all these massive functions across business, government, healthcare, non-profit and law enforcement work?