4 Things Digital Data Collection Predicts for 2016

Prediction is in the cards. Here are four major developments you will experience in 2016 courtesy of predictive analytics.

Prediction is in the cards. Here are four major developments you will experience in 2016 courtesy of predictive analytics:


+ Consumer demand for predictive analytics will surge.

+ Predictive analytics will become a standard safeguard for business.

+ Termination of NSA bulk data collection will be reconsidered.

+ 2016 presidential candidates will use predictive analytics to appeal to voters.

The forecast says predictive analytics. As its wide-scale use rapidly grows, this prognostic technology affects everyone every day. It touches your moment-to-moment life as a consumer, patient, employee, and Internet user. From a higher vantage point, it overhauls business and government by more adeptly driving the major operations of commerce, manufacturing, health care, and law enforcement.

Predictive analytics also takes center stage in this year's most pressing matters: national elections and national security. It changes the game as presidential candidates use it to win more votes. And it intensifies the debate on NSA data collection since this tool helps discover terrorism suspects, yet depends on bulk data to work.

First, let's define it. Predictive analytics is applied science that learns from data to generate many predictions, e.g., whether each individual person will click, buy, like, default on payments, or become ill. These predictions boost the odds across millions of organizational decisions simply because the predictions themselves are considerably better than guessing.

Predictive analytics is the Information Age's latest evolutionary step. This singular, universally applicable force can improve every large-scale thing we do — how we build things, sell things, and prevent bad things from happening — because every function benefits from prediction. As its deployment takes hold across industries, we have moved beyond engineering the infrastructure that manages big data to implementing the science that taps its contents for more intelligent decision-making.

Combating risk, boosting sales, and streamlining manufacturing, predictive analytics has emerged as a commonplace business practice necessary to sustain competitive advantage. Forecasts put its market at $6.5 billion within a couple years and a U.S. shortage of analytics experts at 140,000 in the near term. LinkedIn’s latest number one “Hottest Skills That Got People Hired” is “statistical analysis and data mining.”

First Prediction for 2016: Consumer Demand for Predictive Analytics Will Surge

Predictive targeting is the antidote to information overload, and consumers are realizing they rely on it. This is how Google serves relevant search results, Facebook orders your endless news feed, email critically filters spam out of your inbox, retailers send less junk mail, and today's hot promulgator of online media, Mashable, pinpoints what will go viral.

Overwhelmed by an explosion of options, consumers — at times, unknowingly — turn to prediction in order to navigate the choice of movie (Netflix), music (Spotify), book (Amazon), flight (Hopper), accommodation (Airbnb), price at which to rent out your own home (also Airbnb), romantic date (Match.com), person to tag in a photo (Facebook), ad that interests you (also Facebook), people to “friend” (Facebook and LinkedIn), least-congested driving route (Waze and IBM), and optimal shipping routes (for UPS drivers). If you drive for Uber, the company is working to predict which neighborhoods will have the highest demand and which destination each passenger will request. 

Precautions are targeted, too: Predict bad things in advance and you can prevent them from happening. User-friendly self-diagnostic tools predict when you will die and foretell fates worse than death: divorce, cyberattacks on your business, failed medical procedures, and erectile dysfunction.

Second Prediction for 2016: Predictive Analytics Will Become a Standard Safety Measure Across Industries

Such preemptive safety applies much further, extending to society’s entire infrastructure. Corporations, regulatory agencies, and nonprofits predict fire and lead poisoning, as well as failed manholes, train wheels, nuclear reactors, and satellites. U.S. cities are developing predictive models informed by Yelp reviews to determine which restaurants to inspect first. Shell predicts oil refinery safety incidents. Even sea vessels with the highest probability of a dangerous or costly incident are identified, providing “a big impact on the entire maritime industry in reducing risk and driving safety standards higher,” said Bryan Guenther of the predictive vessel screening company RightShip.

Which at-risk students and juveniles require extra support? Prediction is the means to effectively target intervention. To this end, universities predict dropouts and failing grades. Florida's Department of Juvenile Justice drives per-offender rehabilitation-assignment decisions based on the prediction of future repeat offenses.

A broad range of crime and cheating is detected the same way. The IRS, PayPal, banks, and credit card companies scan for probable fraud. FICO scores your credit reliability with a predictive model. Activision detects cheating in the popular game Call of Duty, thus intervening on thousands of cases per day. Law enforcement organizations predictively investigate, monitor, audit, warn, patrol, parole, and sentence.

Third Prediction for 2016: NSA Bulk Data Collection Will Be Reconsidered 

The predictive approach also pertains to counter-terrorism and prompts a new discussion about the scope of NSA bulk data collection. Predictive models provide triaging for the information overload that intrinsically plagues law enforcement: There is a “haystack” of civilians within which hide a small number of “needles” (i.e., suspects and persons of interest). Predictive targeting of enforcement efforts has the effect of shrinking this haystack and multiplies the effectiveness of the search. But its implementation requires bulk data, including mass data about ordinary, innocent civilians. With this revelation, November's shutdown of NSA telephone metadata collection will be thoroughly reexamined during 2016. 

Fourth Prediction for 2016: Predictive Voter Persuasion Will Fuel Presidential Campaigns

No marketing campaign begs to be optimized more than that of a presidential candidate. These are the country's most visible, preeminent marketing initiatives, and their sales cycles are on the long side, playing out for more than a year of interaction with customers (voters). Obama for America 2012 changed the game by demonstrating that predictively targeting the campaign's sales force — the volunteers who knock on doors and place phone calls — won more votes than traditional campaign targeting. The same approach also boosted Barack Obama's $400 million of TV ad buying to persuade 18 percent more voters.

Unsurprisingly, 2016 presidential candidates are gearing up to also apply predictive analytics. Even as early as July 2015, Hillary Clinton’s analytics job postings enlisted staff for “helping the campaign determine which voters to target for persuasion.” Bernie Sanders’ campaign website included “Director of Data and Analytics” as one of only five posted job listings. Meanwhile, “The DNC is building out further its data infrastructure about voters in battleground states,” said Obama for America's former Director of Statistical Modeling Daniel Porter.

To summarize, 2016 will usher in some unmissable results of the Information Age's latest contribution, the more effective execution of major operations across sectors with predictive analytics.

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Eric Siegel, Ph.D. is the founder of the Predictive Analytics World conference serieswhich includes events for business, government, healthcare, workforce, manufacturing, and financial servicesthe author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die—Revised and Updated Edition (Wiley, January 2016), executive editor of The Predictive Analytics Times, and a former computer science professor at Columbia University. For more information about predictive analytics, see the Predictive Analytics Guide.

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