Personal Predictive Analytics: Should We Be Careful What We Wish For?
The world of continuous monitoring of numerous sensors for machines and humans, limitless information storage capacity, and big data combined with rapid response logistics to detect and meet personal needs that we don't know we have can be exciting, efficient, and energizing. Or it can be fearsome, enervating, and subject to mistakes ranging from foolish to fatal. That's the sense provided by responses to this month's column.
There was enthusiasm for the potential of these capabilities. As Susan Kalyan put it, "Businesses are becoming so complex that integrating predictive analytics in decision making is not very far away… I think 'predictive analytics' is the 'Moneyball' for businesses." Kapil Kumar Sopory said that, "Prevention is better than cure, and this is what predictive analysis in effect helps us to achieve." Casandra Levine agreed, but introduced a cautionary note. The promise of what can be accomplished by predictive analytics in raising the quality of life and functionality for our society is astonishing, she wrote. "What is not clear yet is how those … qualitative factors will be mitigated (by) leaders who are short on ethics and morality…."
Others, most of whom assumed that the technologies would successfully be applied, were less sanguine about the results, expanding on Levine's concerns.
Karine Gantin asked, "If people are reminded over and over that some giant computers controlled by big corporations and institutions know better than they do about what they will be doing in (the) future and what they'll become … They might turn into poor, weak machines themselves …, abiding by what has been foreseen for them by algorithms." Gerald Nanninga added: "Predictive analytics could … (provide) such specialized, repetitive solutions that society will become polarized into user niches that can no longer properly interact… What a sad state of affairs." Philippe Gouamba cautioned that although we rejoice in being able to know the future, "Here is the problem: What happens when we see a future that we cannot affect? That is when predictive analytics become extremely uncomfortable."
Several responses sought to deal with the concerns. Arie Goldshlager commented that, "Predictive analytics can and do produce remarkable results," but they can also produce many false negatives and positives that have to be anticipated and proactively managed. Jeff Rutherford suggested that control in the hands of beneficiaries of the analytics will address needs for privacy. As he put it, "if I'm using a computer wearable such as the new iWatch, and I'm offered a service that will alert my doctor of any health abnormalities via the device…, I'm going to agree to that. My health will win out over privacy in that case."
We have learned to live with and benefit from predictive analytics when they are not applied to individuals. All forecasting is a case in point. When the application becomes personal, it raises many questions that will have to be addressed.
In this case, should we be careful what we wish for? What do you think?
In 2002, the film Minority Report introduced many of us to the world of predictive analytics. In it, an innovative technology allows Washington, D.C. to go without a murder for six years by helping Tom Cruise, chief of the Precrime Unit, to identify, arrest, and prosecute killers before they commit their crimes.
This was a case of the movies catching up to the business world. At that time, predictive analytics had been applied to the continuing maintenance of everything from CAT scan machines produced by GE to elevators made by Otis. It enabled these firms to sell "up time" rather than just products, thanks to a number of sensors and the continuing remote surveillance of the performance of these products.
Predictive analysis applied to humans is now one of the hottest concepts to come along. It is being made possible by a system of customer loyalty programs, big data, and cloud computing that enables the continuous collection, storage, combination, and analysis of data about each of us from a number of disparate sources. Pretty exciting, no?
Some years ago, we heard the story about the GE maintenance engineer who, based on information from the firm's advanced monitoring and predictive analytics, visited one of his hospital accounts to repair a CAT scan machine that had not yet failed. As he was confronted by puzzled hospital administrators, the machine indeed stopped functioning. More recently, many of us have heard the story about the Target customer who was sent information about products of interest to pregnant women before she knew she was pregnant. Target's Big Data analysis of hers' and others' purchases, combined with related information, had placed her in a cohort with other women known to be pregnant.
Predictive analytics will be essential to the development of concepts such as 30-minute package delivery that companies like Amazon have been contemplating. For years, logistics have been managed by principles such as that of "postponement and speculation." The idea is that to approach the best match between supply and demand at a reasonable cost, a supplier has two basic choices. One is to delay (postpone) committing inventory to a particular supply point for as long as possible through such things as careful forecasting of demand, rapid manufacture, and fast transport. The other is to invest (speculate) in long but economical production batches, slow but economical transportation, and large amounts of inventory that ensure an in-stock position when an order is received.
An argument can be made that any forecast and inventory is based on predictive analytics. But in the past, these analytics were applied to data that described behaviors of large groups of decision-makers. By contrast, tomorrow's version of this technique will be based on the analysis of massive files of individual profiles, from which predictions will be built that establish stock levels needed to support 30-minute deliveries. Personalized logistics will take a lot more than just drones.
Predictive analytics have the potential to produce remarkable services and longer lives. But before we become too enamored with them, it's important to remember what happened to Tom Cruise in the movie. He is eventually accused on a precrime basis of murder, with only 36 hours to determine whether the charge is accurate and, if not, who implicated him wrongly.
How important are these concepts to our future? Is this a big deal or just another buzz term in business for the next several years? Are you ready for predictive analytics applied to you? If not, what are you going to do about it? What do you think?