Interesting Snippets

Unlike either a traditional bank or a money management tool, Simple provides its users with a Visa card, then gives them detailed, instant data on each transaction. Transactions can be mapped and tagged, and electronic funds transfers let users send money… (the app) currently only for iPhone, includes similar features to the web interface: users get access to transactions, payment schedules, and ATM locations. It also looks like checks can be deposited straight from the app by taking a picture

The difference with live testing is not just that there is no time to learn and apply lessons. It’s more radical than that: There are no clear lessons to learn, no rules to extract. At the gaming network IGN, for example, executives found that crisp, clear prose was outperforming hyped-up buzzwords (like free and exclusive) on certain parts of the homepage. But in previous years, the opposite had been true. Why? They talked and talked about it, but no one could figure it out. Soon they realized that it simply didn’t matter. A/B would guide them at ground level, so there was no need to worry about why users behaved in one way or another.

One consequence of this data-driven revolution is that the whole attitude toward writing software, or even imagining it, becomes subtly constrained… A number of developers told me that A/B has probably reduced the number of big, dramatic changes to their products. They now think of wholesale revisions as simply too risky—instead, they want to break every idea up into smaller pieces, with each piece tested and then gradually, tentatively phased into the traffic. But this approach, and the mindset that comes with it, has its own dangers. Companies may protect themselves against major gaffes but risk a kind of plodding incrementalism. They may find themselves chasing “local maxima”—places where the A/B tests might create the best possible outcome within narrow constraints—instead of pursuing real breakthroughs… “If you rely too much on the data, you never branch out. You just keep making better buggy whips.

our Facebook “likes” equaled our actual “selves”—creating a phenomenon that is, for governments as well as corporations, the most tempting target imaginable. This trove of information is to an ordinary census database what a super-collider is to a slingshot.

Thanks to technology’s mass appeal and accessibility, on a daily basis we collectively produce 2.5 quintillion bytes of data, and the growth rate is so high that 90% of all information ever created was produced in the last two years alone.

What we can do now has never been possible before: the next IT revolution is happening in the “I” - the information - not the “T”.

Johnson and his team are currently working on futurecasting 2020. Obviously, it’s not done yet. But I asked if I could get a “rough draft” of what we should be looking for. And one of the things he’s modeling for the year 2020 is the “Secret Life of Data.” “Algorithms will talk to algorithms, machines will talk to machines, and humans won’t be involved. When data takes on a life of it’s own, what will that do? How will we remember that when that data comes back, it’s ultimately meant for humans? It has to make our lives better. We can’t forget that.” That’s not all, he adds. There’s also what he refers to as the “Ghost of Computing” – what happens when computers get so small that they disappear, and we have an entire world filled with computational intelligence?

Every time you turn the key to start your brand spanking new car, it activates a database that quietly gathers all kinds of information, including behavioral data. This information is extremely useful for dealers wanting to know just how well the car’s been treated. There’s no point in concealing just how fast you’ve driven it or how many times the airbags have been activated. The key will reveal all, and you can be sure that the valuation of the vehicle will be commensurate with the data the dealer has accessed.

Financial services is the “next big one for us,” said Manoj Saxena, the man responsible for finding Watson work. IBM is confident that with a little training, the quiz-show star that can read and understand 200 million pages in three seconds can make money for IBM by helping financial firms identify risks, rewards and customer wants mere human experts may overlook….
Watson the financial assistant will be delivered as a cloud-based service and earn a percentage of the additional revenue and cost savings it is able to help financial institutions realize.

with the machine, you have more privacy than if a person were watching your clickstreams, picking up collateral knowledge. A human could easily apply analytical reasoning skills to figure out who you were. And any human could use this data for unauthorized purposes. With our data-driven advertising world, we are relying on machines’ current dumbness and inability to “know too much.”

This is a double-edged sword. The current levels of machine intelligence insulate us from privacy catastrophe, so we let data be collected about us. But we know that this data is not going away and yet machine intelligence is growing rapidly. The results of this process are ineluctable.

It’s like an arms race to hire statisticians nowadays,” said Andreas Weigend, the former chief scientist at Amazon.com. “Mathematicians are suddenly sexy.” As the ability to analyze data has grown more and more fine-grained, the push to understand how daily habits influence our decisions has become one of the most exciting topics in clinical research, even though most of us are hardly aware those patterns exist. One study from Duke University estimated that habits, rather than conscious decision-making, shape 45 percent of the choices we make every day, and recent discoveries have begun to change everything from the way we think about dieting to how doctors conceive treatments for anxiety, depression and addictions

For decades, Target has collected vast amounts of data on every person who regularly walks into one of its stores. Whenever possible, Target assigns each shopper a unique code — known internally as the Guest ID number — that keeps tabs on everything they buy. “If you use a credit card or a coupon, or fill out a survey, or mail in a refund, or call the customer help line, or open an e-mail we’ve sent you or visit our Web site, we’ll record it and link it to your Guest ID,” Pole said. “We want to know everything we can.”

Also linked to your Guest ID is demographic information like your age, whether you are married and have kids, which part of town you live in, how long it takes you to drive to the store, your estimated salary, whether you’ve moved recently, what credit cards you carry in your wallet and what Web sites you visit. Target can buy data about your ethnicity, job history, the magazines you read, if you’ve ever declared bankruptcy or got divorced, the year you bought (or lost) your house, where you went to college, what kinds of topics you talk about online, whether you prefer certain brands of coffee, paper towels, cereal or applesauce, your political leanings, reading habits, charitable giving and the number of cars you own. (In a statement, Target declined to identify what demographic information it collects or purchases.) All that information is meaningless, however, without someone to analyze and make sense of it. That’s where Andrew Pole and the dozens of other members of Target’s Guest Marketing Analytics department come in

The microscope, invented four centuries ago, allowed people to see and measure things as never before — at the cellular level. It was a revolution in measurement.

Data measurement, Professor Brynjolfsson explains, is the modern equivalent of the microscope. Google searches, Facebook posts and Twitter messages, for example, make it possible to measure behavior and sentiment in fine detail and as it happens.