Beware the Smart Campaign

Zeynep Tufekci, The New York Times, November 16, 2012


“I AM not a number. I am a free man!” was the famous cry of prisoner Number Six, who could never escape his Kafkaesque village on the 1960s television show “The Prisoner.” This is a prescient cry for an era when numbers follow us everywhere. Jim Messina, the victorious Obama campaign manager, probably agrees that you are not a number. That’s because you are four numbers.

The Obama campaign assigned all potential swing-state voters one number, on a scale of 1 to 100, that represented the likelihood that they would support Mr. Obama, and another number for the prospect that they would show up at the polls. A third metric evaluated the odds that an Obama supporter who was an inconsistent voter could be nudged to the polls, and a fourth score estimated how persuadable someone was by a conversation on a particular issue (which was, of course, also determined by crunching more numbers).
Mr. Messina is understandably proud of his team, which included an unprecedented number of data analysts and social scientists. As a social scientist and a former computer programmer, I enjoy the recognition my kind are getting. But I am nervous about what these powerful tools may mean for the health of our democracy, especially since we know so little about it all.

For all the bragging on the winning side — and an explicit coveting of these methods on the losing side — there are many unanswered questions. What data, exactly, do campaigns have on voters? How exactly do they use it? What rights, if any, do voters have over this data, which may detail their online browsing habits, consumer purchases and social media footprints?

How did Mr. Obama win? The message and the candidate matter, of course; it’s easier to persuade voters if your policies are more popular and your candidate more appealing. But a modern winning campaign requires more. As Mr. Messina explained, his campaign made an “unparalleled” $100 million investment in technology, demanded “data on everything,” “measured everything” and ran 66,000 computer simulations every day. In contrast, Mitt Romney’s campaign’s data operations were lagging, buggy and nowhere as sophisticated. A senior Romney aide described the shock he experienced in seeing the Obama campaign turn out “voters they never even knew existed.” And that kind of ability matters: while Mr. Obama did win decisively, the size of his lead in four states that determined the outcome, Florida, Ohio, Virginia and Colorado, was about 400,000 votes — or about 1.2 percent of the eligible voters.

The confluence of marketing and politics goes back a long way. A blizzard of direct mail engineered by political consultants is credited with defeating President Harry S. Truman’s national health care proposal after World War II. The new methods, however, are not just better direct mail. Noxious TV ads and slick mailers are like machetes compared with the scalpels of social-science-based big-data. The crude methods may still work to soften the ground and drown out other voices, but in the end they are still very big sticks. Sometimes they kill the patient — just ask swing-state voters about the TV ads they were bombarded with.

The scalpels, on the other hand, can be precise and effective in a quiet, un-public way. They take persuasion into a private, invisible realm. Misleading TV ads can be countered and fact-checked. A misleading message sent in just the kind of e-mail you will open or ad you will click on remains hidden from challenge by the other campaign or the media. Or someone who visits evangelical Web sites might be carefully shielded from messages about gay rights, and someone who has hostile views toward environmentalism may receive messages stroking that sentiment even if the broader campaign woos the green vote elsewhere.

What I really worry about, though, is that these new methods are more effective in manipulating people. Social scientists increasingly understand that much of our decision making is irrational and emotional. For example, the Obama campaign used pictures of the president’s family at every opportunity. This was no accident. The campaign field-tested this as early as 2007 through a rigorous randomized experiment, the kind used in clinical trials for medical drugs, and settled on the winning combination of image, message and button placement. I agree that his family is wonderful and his daughters are cute. But an increasing role of “likability” factors, which we now understand better how to manipulate, is not good for democracy.

These methods will also end up empowering better-financed campaigns. The databases are expensive, the algorithms are proprietary, the results of experiments by campaigns are secret, and the analytics require special expertise. The Democrats have an early advantage partly because academics and data analysts tend to be Democrats. Money will solve that problem. This will shift power in both parties even more toward the richer campaigns and may well be the final nail in the coffin of public financing for presidential campaigns.
What is to be done? Campaigns should make public every outreach message so we at least know what they are saying. These messages can be placed in a public database like campaign contributions so the other side can be aware of, and have the right to respond to, false claims. Political access to proprietary databases should be regulated to provide an even playing field.

I’m not claiming that the Obama campaign used these methods to mislead. However, the fact that the winning campaign’s “chief data scientist” was previously employed to “maximize the efficiency of supermarket sales promotions” does not thrill me. You should be worried even if your candidate is — for the moment — better at these methods. Democracy should not just be about how to persuade people to vote for one candidate over another by any means necessary.

Zeynep Tufekci is a fellow at the Center for Information Technology Policy at Princeton University.

Obama's Approach to Big Data: Do As I Say, Not As I Do

Politicians' Policy Decisions May Stymie Tools That Got Them Elected

Kate Kaye, Ad Age, November 16, 2012


One of the keys to success for President Barack Obama's reelection bid was its masterful use of data. But lost in the hype is this: The administration supports a browser-based do not track system that, if pervasive, would throw a wrench into the data-collection tactics that empowered the campaign.

Even today BarackObama.com features data-tracking cookies from several online ad and analytics firms.
The Mitt Romney and Obama campaigns spent hundreds of thousands of dollars in 2012 on data and related services to enhance their own voter contact information, inform their online and offline messaging and target ads. At the same time, Congress is inspecting the practices of firms that buy, sell and filter consumer data for corporate marketers.

"The Obama administration and the GOP should confront head-on the privacy issues raised by [their] far-reaching use of digital profiling and targeting data," argued privacy advocate Jeffrey Chester, founder of the Center for Digital Democracy. "It would be unfortunate for the administration's work to advance Do Not Track and other key safeguards if they failed to tackle the use of powerful data targeting technologies by political campaigns."

Industry and privacy wonks actually agree
It's a rare occurrence, but both Mr. Chester and the ad industry are in agreement on one thing: They both appreciate the attention the Obama data machine is getting. Privacy groups want to raise awareness of data collection and usage in the hopes of generating public support for curbing what they see as an increasingly infiltrative violation of personal privacy by marketers and the mushrooming data industry.

"Protecting the privacy of consumers and citizens should require policymakers from both sides to confront the civil liberties implications of what has been unleashed," added Mr. Chester, noting that the 2012 campaigns should divulge what data they collected, how they targeted ads and what will happen to the information now that the election is over.

Industry players, especially their Capitol Hill lobbyists, aim to convince legislators that the very data practices some of them criticize are helping them and their colleagues win races.

"Big data isn't going to help Todd Aken," said Mike Zaneis, general counsel of the Interactive Advertising Bureau, referring to the disgraced Congressman from Missouri who lost his Senate campaign after claiming women can ward off pregnancy resulting from "legitimate rape." Continued Mr. Zaneis, "But the Obama campaign used a lot of online data and a tremendous amount of offline data to go precinct-by-precinct to get-out-the-vote."

Third-party tags
More than a week after the election, BarackObama.com houses an array of third-party tags that track users for ad targeting and campaign and site analytics. Yesterday, around fifteen ad company tags were surfaced by Evidon's Ghostery software, including tags from BlueKai, which calls itself a "big data activation solution," and Appnexus, which among other things allows advertisers to use a variety of user behavioral data to target ads to those users on Facebook.

Both the Obama and Romney campaigns used social-media-widget and data provider ShareThis to target fundraising ads and identify issues and trends swing state voters were interested in, according to ShareThis CEO Kurt Abrahamson. The company tracks when people visit web pages and share them on Twitter, Facebook, LinkedIn or other popular social sites and allows advertisers to target ads using that anonymized information.

Clashing goals of campaigning and governing
Data tracking tools and techniques that have helped legislators on both sides of the aisle build supporter lists, generate donations and get out the vote could be stymied by a do-not-track browser standard or restrictive privacy legislation.

In February, the Federal Trade Commission and the ad industry announced they'd work together with browser companies to develop a DNT standard. At the same time, the U.S. Commerce Department introduced a consumer privacy bill of rights that guided companies to provide individual control over data collection, better data security measures, and transparency of data use, and also called for "a reasonable amount of data collection by companies." Secretary of Commerce John Bryson said at the time the department would work with Congress to implement the privacy bill of righs -- which some deem to be supportive of industry's self-regulatory approach -- through legislation.

The Digital Advertising Alliance, a large coalition of ad industry trade groups, has conducted an "ongoing dialogue with the FTC as recently as yesterday to figure out how to implement the [DNT] standard," said Stu Ingis, counsel to the DAA, on Wednesday. The DAA oversees the industry's Ad Choices program, which allows people to opt-out from online ad targeting through display ads that include the group's small triangular symbol. It's not entirely clear whether the FTC is confident that the DAA's self-regulatory program is enough to protect consumer privacy.

As reported by Politico earlier this month, FTC Chairman Jon Leibowitz said, "If by the end of the year or early next year, we haven't seen a real Do Not Track option for consumers, I suspect the commission will go back and think about whether we want to endorse legislation." Mr. Leibowitz is expected by beltway insiders to step down at the end of the year, and some believe his goal to finalize a DNT standard before he leaves is pressurizing the situation.

A free pass for political data?
Enter the Bipartisan Congressional Privacy Caucus. The group recently received responses to inquiries into several data firms that manage and analyze, and in some cases buy and sell, online and offline consumer data. Nine firms -- Acxiom, Epsilon, Equifax, Experian, Harte-Hanks, Intelius, Fair Isaac, Merkle, and Meredith Corp. -- submitted lengthy and often vague answers to a series of questions about their data businesses and practices.

"Many questions about how these data brokers operate have been left unanswered, particularly how they analyze personal information to categorize and rate consumers," said lawmakers in a joint statement regarding the companies' responses.

Absent from the list of data firms questioned were similar companies that deal mainly in voter file and political information that is often enhanced with consumer demographic, shopping and other data. For instance, NGP Van, the Democratic data powerhouse favored by the Obama team was not part of the inquiry. The Obama campaign and DNC spent hundreds of thousands of dollars with NGP Van this election cycle alone. The firm matches its voter data with data from TargetSmart, which offers "the richest set of consumer and interest data, allowing the most sophisticated targeting," according to the NGP Van site.

Other political data firms left out of the inquiry include Catalist, another Democratic data firm; Campaign Grid, which offers Republican data and online ad targeting; and Aristotle, a well-established non-partisan political data company. People involved with the congressional inquiry deny that political data firms were left off the list for any strategic reason.

In a press release about the data broker responses, the Privacy Caucus stated it "will push for whatever steps are necessary to make sure Americans know how this industry operates and are granted control over their own information."

Rep. Ed Markey, a Democrat from Massachusetts and Caucus co-chair, has sponsored a Do Not Track Kids Act and a mobile privacy bill.

Observers don't expect a privacy bill to be passed anytime soon; if that does happen, it may not apply to political campaigns or groups anyway. For instance, political messages are exempt from CAN-SPAM laws, and political organizations are not restricted by the Do Not Call Registry.

"Often when data laws are being proposed and put forward, the politicians exempt themselves," said Don Hinman, senior VP for data strategy at Epsilon, which gets some of its data from political advertisers but mainly is a purveyor of consumer information.

Mr. Ingis considers it exemption for political messages to be a first amendment issue. "It would be very hard for such a limitation on political messages to be restricted. . . . and I think that would have been true in the context of Do Not Call if they would have gone there," he said.

How 'Social Intelligence' Can Guide Decisions

By offering decision makers rich real-time data, social media is giving some companies fresh strategic insight.

Martin Harrysson, Estelle Metayer, and Hugo Sarrazin, McKinsey Quarterly, November 2012

In many companies, marketers have been first movers in social media, tapping into it for insights on how consumers think and behave. As social technologies mature and organizations become convinced of their power, we believe they will take on a broader role: informing competitive strategy. In particular, social media should help companies overcome some limits of old-school intelligence gathering, which typically involves collecting information from a range of public and propriety sources, distilling insights using time-tested analytic methods, and creating reports for internal company “clients” often “siloed” by function or business unit.

Today, many people who have expert knowledge and shape perceptions about markets are freely exchanging data and viewpoints through social platforms. By identifying and engaging these players, employing potent Web-focused analytics to draw strategic meaning from social-media data, and channeling this information to people within the organization who need and want it, companies can develop a “social intelligence” that is forward looking, global in scope, and capable of playing out in real time.

This isn’t to suggest that “social” will entirely displace current methods of intelligence gathering. But it should emerge as a strong complement. As it does, social-intelligence literacy will become a critical asset for C-level executives and board members seeking the best possible basis for their decisions.

In this article, we explore four distinct ways social technologies can augment the intelligence-gathering approaches of companies. As Exhibit 1 makes clear, social media has little effect on some aspects of the intelligence cycle—in particular, the need to identify priorities for exploration and decision making over the next 6 to 12 months, as well as the use of assembled information to make unbiased decisions. But social technologies can play a surprisingly central role in how information is sourced, collected, analyzed, and distributed.

Andrew McAfee : Let the Crowd Fix Your Product's Bugs

Andrew McAfee, Harvard Business Review Blog, November 6, 2012

I'm starting to come to the conclusion that of all the myths businesses and their leaders tell themselves, one of the most harmful is that they know where the expertise is. The more I learn about the results from crowdsourcing and open innovation efforts, the more I believe that the smart strategy is to expose your problems and challenges to as many people as possible and let them show you what they can do. Here's my most recent example of the power of this approach.

The online startup Kaggle assembles a diverse group of people from around the world to work on tough problems submitted by organizations. The company runs data science competitions, where the goal is to arrive at a better prediction than the submitting organization's starting 'baseline' prediction. Results from these contests are striking in a couple ways. For one thing, improvements over the baseline are usually substantial. In one case, Allstate submitted a dataset of vehicle characteristics and asked the Kaggle community to predict which of them would have later personal liability claims filed against them. The contest lasted approximately three months, and drew in more than 100 contestants. The winning prediction was more than 270% better than the insurance company's baseline.

Another interesting fact is that the majority of Kaggle contests are won by people who are marginal to the domain of the challenge — who, for example, made the best prediction about hospital readmission rates despite having no experience in health care — and so would not have been consulted as part of any traditional search for solutions. In many cases, these demonstrably capable and successful data scientists acquired their expertise in new and decidedly digital ways.

Between February and September of 2012 Kaggle hosted two competitions sponsored by the Hewlett Foundation about computer grading of student essays. Improvements in this area are important because essays are better at capturing student learning than multiple choice questions, but much more expensive to grade when human raters are used. So automatic grading of written answers would both improve the quality of testing and lower its cost. Kaggle and Hewlett worked with many education experts to set up the competitions, and as they were preparing to launch some of these people were worried.

The first contest was to consist of two rounds. Eleven established educational testing companies would compete against each other in the first, with members of Kaggle's community of data scientists invited to join in, individually or in teams, in the second. The experts were worried that the Kaggle crowd would simply not be competitive. After all, each of the testing companies had been working on automatic grading for some time, and had devoted substantial resources to the problem. Their hundreds of man years of accumulated experience and expertise seemed like an insurmountable advantage over a bunch of novices.

They needn't have worried. Many of the 'novices' drawn to the challenge outperformed all of the testing companies in the essay competition, and came closer to the consensus score of the human graders than did any of the humans themselves. The surprises continued when Kaggle investigated who the top performers were. In both competitions, none of the top three finishers had any previous significant experience with either essay grading or natural language processing. And in the second competition, none of the top three finishers had any formal training in artificial intelligence beyond a free online course offered by Stanford AI faculty and open to anyone in the world who wanted to take it. And people all over the world did, and learned a lot from it. The top three individual finishers were from, respectively, America, Slovenia, and Singapore.

Businesses certainly know where a lot of the relevant expertise is in any situation, but results like those from Kaggle show me that they certainly don't know where all of it is. As the open source software advocate Eric Raymond famously observed, with enough eyeballs all bugs are shallow. So why not expose your tough problems to as many eyeballs as possible?

How Big Data Could Determine the Winner of Today's Election

Tarun Wadhwa, Forbes, November 6, 2012

If your favorite soda is Diet Dr. Pepper, the chances are that you’ll be supporting Mitt Romney.  Pepsi drinker? You’re most likely voting for Barack Obama. If you drink Mountain Dew, you probably don’t care either way.

These types of conclusions may seem simplistic and superficial, but both campaigns are betting that they will be the key to deciding who the next President of the United States is.

It’s more than what you drink, what you shop for, who your friends are, what websites you visit: all reveal clues to your political leanings. Campaigns have entered the era of “Big Data”—they target voters based on scraps of information they gather from unlikely places.

Thanks to the rise of mobile technology and social media, the number of records collected by data brokers on voter behavior has tripled—from 300 pieces in 2004 to more than 900 pieces today.

Campaigns care about your personal life
Voters used to be the ones obsessing over details of a candidate’s personal life.  Now the tables have turned. Campaigns research the personal lives of the voter.

Micro-targeting, a technique that delivers ads based on the personal traits of a voter, was once considered impossible.  But in 2004, it was recognized for helping George W. Bush defeat John Kerry.  Now it is used by almost every campaign.

Because of the intricacies of our electoral system, a relatively small group of people ends up deciding the outcome of elections.  In the 2000 Presidential campaign, hundreds of millions of dollars was spent on reaching just 7 percent of voters—fewer than 8 million people.  Even a small advantage in mobilizing potential voters in a swing state can determine the difference between a win and a loss.

In this election cycle, more than $3 billion dollars has been spent on broadcast-television advertising, which has remained the dominant form of political communication for the last fifty years.  But times are changing. Television purchases are no longer as effective as they used to be.  A study showed that 88% of voters with DVRs skip ads and that 45% use something other than live TV as their primary mode for viewing videos. These proportions are even higher in younger demographics.

The next frontier: digital behavioral advertising
Just as television advertising revolutionized the field in the 1960s, this election will likely mark digital-behavioral advertising as the next frontier in voter outreach.

As a nation, we are already divided along partisan lines.  We access different media, each with its own messaging and focus. Now we will receive different messages depending on who we are.  Zac Moffatt, digital director for Mitt Romney’s campaign, said to The New York Times that “two people in the same house could get different messages,” and that “not only would the message change, the type of content would change.”

In an article for Stanford Law Review, Daniel Kreiss, a journalism professor at University of North Carolina, Chapel Hill, explains how this can have negative long-term consequences for democratic participation.  With so much sensitive personal information in so many hands, there are risks of data breaches and unauthorized disclosure.

Citizens may hesitate to engage in political discussion on line for fear of being tagged and put into a marketing database.  And the high cost of political data and consulting activities may make it difficult for less affluent candidates to compete effectively.  Perhaps most worrying, campaigns may “redline” an electorate (by ignoring voters who won’t be sympathetic to their views because a model deems them unworthy of investment).

Political targeting – what’s next?
Sophisticated modeling and targeting will become commonplace at every step of the political process. NGOs, interest groups, and candidates for local office will be the next to adopt these methods.

United in Purpose, an evangelical Christian non-profit, is currently using such technology to assign points to voters based on whether they like NASCAR or fishing, and whether they are on anti-abortion or traditional marriage lists.  If these voters have a score of over 600 points, they are considered “serious about their faith”. They will be contacted if they have not registered to vote.

Many voters would be surprised to learn that their interactions with both campaigns are being recorded and analyzed using technology similar to what Target uses to determine whether  teenage girls are pregnant. When voters do learn what their candidates are doing, as many as 86 percent want this to stop. They regard it as an invasion of privacy.  Yet these types of activities are legally considered political speech, so there are hardly any restrictions in place.

What is most worrisome is that there is no easy way to opt out of these databases, or to limit what information is collected about you, or how it is used.  Sadly, we can’t “de-friend” or “unfollow” the politicians.

Google Now: Behind the Predictive Future of Search

How Google learned to un-fragment itself and create the next big thing

Dieter Bohn, The Verge, October 29, 2012


For decades, visions of the future have played with the magical possibilities of computers: they'll know where you are, what you want, and can access all the world's information with a simple voice prompt. That vision hasn't come to pass, yet, but features like Apple's Siri and Google Now offer a keyhole peek into a near future reality where your phone is more "Personal Assistant" than "Bar bet settler." The difference is that the former actually understands what you need while the latter is a blunt search instrument.\

Google Now is one more baby step in that direction. Introduced this past June with Android 4.1 "Jelly Bean," it's designed to ambiently give you information you might need before you ask for it. To pull off that ambitious goal, Google takes advantage of multiple parts of the company: comprehensive search results, robust speech recognition, and most of all Google's surprisingly deep understanding of who you are and what you want to know.

With Android 4.2, launching alongside the Nexus 4 and Nexus 10 on November 13th, Google has updated the feature with new information cards in new categories. And yet, the amount of engineering effort that makes Google Now possible is out of proportion to what it does — it's a massive, cross-company effort for what seems like a relatively small product. That difference is a clue. Google Now isn't important for what it does, well, "now," but the building blocks are there for a radically different kind of platform in the future.
We sat down with the teams responsible for some of the technology that went into Google Now to find out what makes it tick today and discover some hints about what it could be in the future.

A deeper understanding
You may not be familiar with Google Now, primarily because it's only available on the sliver of Android devices running Jelly Bean (and up) — a situation that sadly won't change with the latest version. It's essentially an app that combines two important functions: voice search and "cards" that bubble up relevant information on a contextual basis.

Actually, Google Now technically only refers to the ambient information part of the equation, a branding kerfuffle that distinguishes it from Apple's Siri product yet still causes confusion. Those cards might contain local restaurants, the traffic on your commute home, or when your flight is about to take off. They appear automatically as Google tries to guess the information you'll need at any given moment.

While it seems like a relatively simple service, it's only really possible because of the massive amount of computational power Google can leverage alongside the massive amount of data Google knows about you thanks to your searches. It's "precisely what Google is best at," Android's director of product management, Hugo Barra, tells us. "It really feels like we’ve been working on Google Now for the past ten years. Because Google Now touches every back-end of Google, every different web service that’s been developed over the last ten years or so is part of this service."


The breadth of that backend and the simple cards it enables is what makes Google Now so intriguing as a product. One of Barra’s favorite examples is a voice search for something that pulls from all those multiple sources and turns it into a comprehensible and useful result. Searching for “Directions to the museum with the William Paley exhibition” causes Google to 1) find that exhibition, 2) understand you care about the museum where it is being shown, 3) know your location, and finally 4) present you with a simple map card to the museum itself along with a button to immediately get directions.

Taking all of that complex data and turning it into a relatively simple and useful interface is a gargantuan undertaking, but Google has started with a somewhat small set of categories for the types of cards it shows. With Jelly Bean, you'd see calendar alerts, weather, flight times, sports scores, transit directions, local restaurants, and a few more categories of information.

Even within that limited set of data, Google has to make choices about which cards to show you and when. It uses a few different signals — location, time, and all of your recent searches figuring prominently among them — to decide what to show you in any given moment. "It’s essentially a ranking problem, and it’s a very complicated one," according to Barra, but Google has perhaps more experience at solving ranking problems than any other company after years of delivering search results.

In my experience, Google is able to get you the "right" information you want a relatively small percentage of the time, but that low hit rate doesn't actually hurt the experience all that much. That's mainly thanks to the fairly small number of categories cards fall into, but also to the fact that when Google Now gets it right, it really feels magical. The sort of thing you might manually search for — like your commute time home — is simply waiting for you.

With the latest update, Google is expanding Now into new categories, increasing the different kinds of information it's able to provide. The new additions aren't radically ambitious, but that's in fitting with the overall feel of Google Now. What it shows you is more about serendipitous information than structured data.
The first category involved Gmail integration. With your permission, Google will keep an eye on your inbox and recognize flight confirmations, hotel reservations, restaurant bookings, event tickets, and package tracking emails. It will take that knowledge and give you a relevant card when appropriate — say, giving you your hotel information when you land in the right city or letting you know when it's time to leave for a concert.

The new features are part of Google’s growing efforts to provide relevant results based on the knowledge it’s accumulated about you. As search gets better, so do people’s expectations for what it provides. “Of course Google’s going to access more than just the public information on the web,” Scott Huffman, Engineering Director for Search Quality at Google tells us, “Google’s going to know when my flight is, whether my package has gotten here yet and where my wife is and how long it’s going to take her to get home this afternoon. [...] Of course, Google knows that stuff.” If you’re willing to opt in to letting Google know so much about you — and increasingly, opting in is the default — then Google wants to return the favor by using that information to your benefit. It requires you to trust Google quite a bit, but the company hopes that your trust will be rewarded.

These new cards are actually similar to a feature that Google added to its web search results this past August, both in content and in style. That's probably not an accident — if you assume Google has already won the battle for search, the next battle is giving you information before you even search for it. When it comes to deciding which data to give you, Barra tells us that Google has "a pipeline [...], possibly in the hundreds of cards” from its many engineering teams. Rather than flood users with all of those new cards, Google is taking a slow and steady approach to adding those new features — if only because right now it can only add those cards with a software update.

Some of the other new categories of cards are relatively minor additions: stocks, news, local concerts, movies, and local attractions. It also has a basic exercise tracking card that utilizes the phone’s accelerometer and location data: every month it will let you know how far you've walked or biked and also tell you how it compared to the month previous. Another new card lets you know that you're near a "photo opportunity," as Product Management Director Baris Gultekin told us. It uses data from Google's Panaramio service, noting when you're close to a place that has a "high density of pictures taken at a spot." You can see photos that were taken at the landmark and, Google hopes, take one yourself.

Neural networks
Just as Google Now's ambient information is backed by a massive and unseen engineering effort, Google's voice search is a simple feature that belies the effort that goes behind it. Huffman points out that getting voice search right actually involves more than just turning spoken words into textual queries, "speech recognition, natural language understanding, and understanding entities and knowledge in the world [all] really have to come together."

Voice search is the sort of feature that we take for granted on smartphones — Apple’s Siri and even Windows Phone both use the feature to offer up search results that go beyond basic web searches. What used to be a "hey neat" kind of feature is increasingly becoming an expected feature, and Google is well aware of that, "As you make search better, people’s expectations go up." To meet those expectations, Google is attacking all three of the areas Huffman delineated in equal measure.

Speech recognition is a very difficult problem to solve, as anybody who has dealt with voice search knows all too well. Recently, Google has changed its approach to making it work in a fundamental way, replacing a system that was the result of years of effort with a new framework for understanding the spoken word. Google has shifted to using a neural network that's much more effective at understanding speech.

A neural network is a computer system that behaves a bit like the actual neurons in your brain do. 

Essentially, the computer is designed with layers of software-based "neurons" that do the same thing actual neurons do: take input in and "fire" off to other neurons based on the data they receive. Over the summer, the results of research led by Google Fellow Jeff Dean's on neural networks made some waves: Google had taught a computer to recognize cats in videos. The interesting part is that the neural network essentially created the concept of "cat" on its own without direct human intervention.

Here's how it works: The first layer of neurons looks for very simple things, like angled lines or colors. If it sees something that matches, it fires off a signal. There's then a second layer of neurons, which simply pays attention to sets of neurons firing from the first layer. As you add in more and more layers with the same behavior, you essentially add in layers of conceptual abstraction until, at the very top layer, there's a neuron that has trained itself to recognize cats 15.8 percent of the time.

Of course, that doesn't mean that the computer "understands" cats in a conscious way, but the effect of it being able to recognize something like a cat without direct human training is what's important. "With a lot of other machine learning techniques," Dean explains, "you often have to do a lot of work to hand-engineer exactly the right features [...] Whereas with a neural network you can feed in much rawer forms of data."
Google's research scientists took this method and essentially applied it directly to speech recognition, fellow researcher Vincent Vanhoucke told us. "We picked up the kind of work that Jeff’s team was doing and just changed the input of the system." Google used the neural network at a very basic level of speech recognition: understanding and interpreting the basic sounds of speech: phonemes.

The approach "led to about between 20 to 25 percent reduction in the error rate in our system," according to Vanhoucke. The neural network turned out to be exceptionally good at solving what used to be very thorny problems in speech recognition. Accounting for "different environments, [...] different accents, different tones of voice, different pitches, different background noise, different microphones, [...], people talking in the background, different audio conditions" became much easier because the network was able to automatically learn how to account for each situation.

Knowledge Graph
Just understanding the words you've spoken isn't enough, obviously. Just as a neural network trades in increasing layers of abstraction, Google itself needs to move beyond basic web queries. In a very real way, Google is trying to get its computers to actually understand what it is you're asking them. Part of that comes from a relatively new initiative called the "Knowledge Graph," the company's effort to compile a database of "entities" in the world.

Today, Google's servers are aware of 500 million such entities, and "knowing" those things means that the company is able to act on them in interesting ways. For example, if you search for a “Tom Cruise,” Google knows you’re referring to a person instead of a vacation and can then tell you specific facts about him instead of simply crawling the web for related words. In truth, Google only knows those details because it is so adept at crawling the web — but the additional layer of abstraction created by putting that information into the structured Knowledge Graph means that Google can do more with search results. It "allows voice search, in some sense, [to] give me something to talk about," says Huffman. In Tom Cruise example, returning an “entity” instead of just a search result means you can contextually ask for more information, like “what movies has he been in?” or “how tall is he, really?”

Having something to talk about and talking to somebody are two different things, and with regard to the latter Google is again taking a Google-esque approach. As opposed to Apple's Siri, which you could say has a distinct personality, Huffman says that Google has "shied away from the idea of kind of a human persona for search or for the entity that you’re interacting with and instead tried to go for, in some sense, ‘hey, you’re interacting with all of Google.’"

Wrap-up
The Google that you're interacting with in Google Now is very different than the Google you used even a year ago. The company's products have often felt fragmented, serving small niches and launched without feeling fully thought-through — and then in too many cases simply killed off. That may have been a function of the fact that Google is so large and does so much — but Google Now is a sign that all the different parts of Google are finally working together in a cohesive way.


"Google Now actually started as a twenty percent project," Barra told us. Google famously encourages its employees to work on "side projects" for some portion of their time, and what's interesting about Google Now is that although it started two years ago as one of these side projects, it's become a catalyst for integrating so many different parts of Google. Barra tells us that “we literally have dozens of teams working with us right now,” and the achievement with Google Now is that it feels like those teams are integrated, not fragmented.

In a single app, the company has combined its latest technologies: voice search that understands speech like a human brain, knowledge of real-world entities, a (somewhat creepy) understanding of who and where you are, and most of all its expertise at ranking information. Google has taken all of that and turned it into an interesting and sometimes useful feature, but if you look closely you can see that it's more than just a feature, it's a beta test for the future.

Crowdsourcing Medical Treatments

Samahope Crowdsources Simple, Life-Saving Surgeries For The Poor

Ellen McGirt, Fast Company, November 2, 2012

Veteran social entrepreneur Leila Janah of Samasource recently co-launched a new project to crowdfund medical treatment for the very poor. Think of it as Kiva for surgery.


“I just started bawling,” Leila Janah is telling me about a trip she took to Sierra Leone earlier this year. "I’m usually pretty steely as a matter of course. But I’ve been crying a lot more lately.”

Janah, the founder of Samasource, a nonprofit organization that brings paid digital work to very poor women and youth, is no stranger to harsh realities. She studies them for a living.

But a sweet teenaged girl named Tiangay Kaiwo moved Janah to tears. Kaiwo was waiting for surgery at the government hospital in Bo to repair the extensive damage to her body after her teacher brutally raped her. Traditional tribal remedies involving herbs and a bath in boiling water exacerbated her condition. Kaiwo had been living with a painful rectovaginal fistula for over a year, but best as Janah could tell, the rapes began when she was 12 years old. Janah met dozens of girls and women with similar stories, all needing life-changing surgeries that nobody could afford. “Nobody even to hold their hands, to tell them that this wasn’t their fault,” Janah said.

When the slice of the market you are trying to corner is as troubled as the one that Janah is, then tears are clearly a rational response. But after tears, at least if you’re built like Janah, comes action.

Enter her latest project, Samahope, an experiment in crowdfunding medical treatment--like burn care--for the very poor. Think of it as Kiva for surgery. “There are millions of people who need corrective surgeries that we take for granted in the West,” she says. But the very poor often need types of care, like fistula surgery, that are now wholly unfamiliar and largely unnecessary in the developed world. (You can contribute to the development of the site via their Indiegogo campaign here.)

The site launched last month and has already funded a handful of surgeries; there are over 70 profiles on the site. If you believe in the premise of the League of Extraordinary Women, then the business case is clear: If you get one girl back on her feet, she can go to school. If she goes to school, she can get a job. Enough girls join the workforce and a country gets uplifted. But Janah sees another benefit. “They want to say what happened to them, to tell their own stories,” she says.

She has collected so many of these stories--of the poor and the embattled and their search for basic human rights through employment--that she’s writing a book. “I just interviewed a security guard at a hotel in Freetown," Janah said. "He grew up as a rebel and child soldier in the conflict--think about that for a minute--then forced into the diamond mines. His life was so full of conflict, a constant struggle to access basic human resources, that it’s impossible to wrap my mind around.” Recalling young Kaiwo, “For someone like her, being able to tell her story and help other girls not become a victim is a very powerful thing.”

Samasource, which last month closed a $7.5 million round of philanthropic funding led by The MasterCard Foundation, has become the darling of the tech crowd for its deft use of the Internet to match an excess capacity of potential workers with the jobs they need to live in dignity. “But the scale of the problems can seem so great compared to the resources you have to address them,” says Janah--thus, the crowdsourcing project. Unlike her peers in the for-proft tech world, she is not going to be able to turn to her staffers with breathless reports of sky-high valuations, rounds of venture funding, or promises of equity upside.
And yet, Janah is convinced that dignified work can resurrect even the most damaged lives, and that her own business case is sound. “We’ve gotten the microwork model on the agenda of a lot of foundations and government entities. We just have to prove it can scale.”

Janah recalls with fondness the “aha” moment when she knew that Samasource could actually be a business. But the slog of ramping up to achieve a massive goal is largely free of lightbulb moments. “Not a day goes by when I don’t doubt myself or question something,” she says. So instead, she takes the power of microwork and puts it to work for herself and her team. “I had to manage my own psychology around this. So, I’ve trained myself to pause and celebrate each step.”

She rattles off a list of things that sound more startup than do-good: Realign your expectations, hit your goals, stay close to the customer, stay connected to your mission. She ends up sounding more like a Zen master than elevator pitcher. “It’s about looking down and doing what’s in front of you. Truly savor it. Then do the next thing. My job is to make sure we’re all going in the right direction, and at the end of the year, the sum of those steps adds up to something really great.”

Mechanical Turk and the Limits of Big Data

Walter Frick, MIT Technology Review, November 1, 2012


The Internet is transforming how researchers perform experiments across the social sciences.

It’s telling that the most interesting presenter during MIT Technology Review’s EmTech session on big data last week was not really about big data at all. It was about Amazon’s Mechanical Turk, and the experiments it makes possible.

Like many other researchers, sociologist and Microsoft researcher Duncan Watts performs experiments using Mechanical Turk, an online marketplace that allows users to pay others to complete tasks. Used largely to fill in gaps in applications where human intelligence is required, social scientists are increasingly turning to the platform to test their hypotheses.

The point Watts made at EmTech was that, from his perspective, the data revolution has less to do with the amount of data available and more to do with the newly lowered cost of running online experiments.

Compare that to Facebook data scientists Eytan Bakshy and Andrew Fiore, who presented right before Watts. Facebook, of course, generates a massive amount of data, and the two spoke of the experiments they perform to inform the design of its products.

But what might have looked like two competing visions for the future of data and hypothesis testing are really two sides of the big data coin. That’s because data on its own isn’t enough. Even the kind of experiment Bakshy and Fiore discussed—essentially an elaborate A/B test—has its limits.

This is a point political forecaster and author Nate Silver discusses in his recent book The Signal and the Noise. After discussing economic forecasters who simply gather as much data as possible and then make inferences without respect for theory, he writes:

This kind of statement is becoming more common in the age of Big Data. Who needs theory when you have so much information? But this is categorically the wrong attitude to take toward forecasting, especially in a field like economics, where the data is so noisy. Statistical inferences are much stronger when backed up by theory or at least some deeper thinking about their root causes.

Bakshy and Fiore no doubt understand this, as they cited plenty of theory in their presentation. But Silver’s point is an important one. Data on its own won’t spit out answers; theory needs to progress as well. That’s where Watts’s work comes in. 

The Internet is transforming how researchers think of the “lab” and enabling new kinds of experiments across the social sciences. Those experiments will be critical in helping us collectively make sense of the huge amounts of data we’re now generating. And those huge data sets will help inform the direction of Watts’s and others’ experiments.

The value of big data isn’t simply in the answers it provides, but rather in the questions it suggests that we ask.

Big Data and the Democratisation of Decisions (Economist Intelligence Unit

Report of the Economist Intelligence Unit, 2012

Too much important and relevant data – including new sources of Big Data – remains out of reach from those in your organization who can turn it into value. 
Download Big data and the democratisation of decisions to learn:
·       Why 77% of executives said more employees need access to Big Data;
·       What the two biggest opportunities for Big Data to deliver business value are;
·       What other kinds of data can provide better context for new sources of Big Data;

Economist: Cities Are Turning into Vast Data Factories

The Economist, October 27, 2012

Cheap and easy electronic communication has probably helped rather than hindered this. First, connectivity is usually better in cities than in the countryside, because it is more lucrative to build telecoms networks for dense populations than for sparse ones. Second, electronic chatter may reinforce rather than replace the face-to-face kind. In his 2011 book, “Triumph of the City”, Mr Glaeser theorises that this may be an example of what economists call “Jevons’s paradox”. In the 19th century the invention of more efficient steam engines boosted rather than cut the consumption of coal, because they made energy cheaper across the board. In the same way, cheap electronic communication may have made modern economies more “relationship-intensive”, requiring more contact of all kinds.

Recent research by Carlo Ratti, director of the SENSEable City Laboratory at the Massachusetts Institute of Technology, and colleagues, suggests there is something to this. The study, based on the geographical pattern of 1m mobile-phone calls in Portugal, found that calls between phones far apart (a first contact, perhaps) are often followed by a flurry within a small area (just before a meeting).

Data deluge
A third factor is becoming increasingly important: the production of huge quantities of data by connected devices, including smartphones. These are densely concentrated in cities, because that is where the people, machines, buildings and infrastructures that carry and contain them are packed together. They are turning cities into vast data factories. “That kind of merger between physical and digital environments presents an opportunity for us to think about the city almost like a computer in the open air,” says Assaf Biderman of the SENSEable lab. As those data are collected and analysed, and the results are recycled into urban life, they may turn cities into even more productive and attractive places.

Some of these “open-air computers” are being designed from scratch, most of them in Asia. At Songdo, a South Korean city built on reclaimed land, Cisco has fitted every home and business with video screens and supplied clever systems to manage transport and the use of energy and water. But most cities are stuck with the infrastructure they have, at least in the short term. Exploiting the data they generate gives them a chance to upgrade it. Potholes in Boston, for instance, are reported automatically if the drivers of the cars that hit them have an app called Street Bump on their smartphones. And, particularly in poorer countries, places without a well-planned infrastructure have the chance of a leap forward. Researchers from the SENSEable lab have been working with informal waste-collecting co-operatives in São Paulo whose members sift the city’s rubbish for things to sell or recycle. By attaching tags to the trash, the researchers have been able to help the co-operatives work out the best routes through the city so they can raise more money and save time and expense.

Exploiting data may also mean fewer traffic jams. A few years ago Alexandre Bayen, of the University of California, Berkeley, and his colleagues ran a project (with Nokia, then the leader of the mobile-phone world) to collect signals from participating drivers’ smartphones, showing where the busiest roads were, and feed the information back to the phones, with congested routes glowing red. These days this feature is common on smartphones. Mr Bayen’s group and IBM Research are now moving on to controlling traffic and thus easing jams rather than just telling drivers about them. Within the next three years the team is due to build a prototype traffic-management system for California’s Department of Transportation.

Cleverer cars should help, too, by communicating with each other and warning drivers of unexpected changes in road conditions. Eventually they may not even have drivers at all. And thanks to all those data they may be cleaner, too. At the Fraunhofer FOKUS Institute in Berlin, Ilja Radusch and his colleagues show how hybrid cars can be automatically instructed to switch from petrol to electric power if local air quality is poor, say, or if they are going past a school.

Enforcing the law may also become easier. Andrew Hudson-Smith, director of the Centre for Advanced Spatial Analysis at University College London, thinks that within five years or so police forces will be able to predict and prevent some crimes by watching Twitter and other social media. The thought may give civil libertarians the creeps, but some Londoners, recalling the part played by instant messaging in last year’s riots in their city, may wish the police already had such foresight.

More mundanely, existing data about crime can be analysed more systematically. “Law enforcement’s main problem is the fragmentation of information,” says Mark Cleverley of IBM. Local policemen may know that street robberies are likelier on certain days of the week; combining information like this with other data (such as weather, time of day and so forth) makes crimes easier to prevent. In Memphis predictive-analytics software helped to reduce serious crime by 30% and violent crime by 15% between 2006 and 2010.

However, the real prize, says John Day of IBM Research, lies not in single areas such as traffic or policing but in making whole cities better by drawing on data from multiple sources for multiple purposes. 

Smartphones and cameras, say, can track the flow of people as well as that of cars. A social-media flurry may show that more people than expected are going to turn up at a rock concert, suggesting that traffic should be redirected, more public transport laid on or more police deployed.

Even brilliant technology is not much use if cities are badly run or their politics are dysfunctional. Different departments or local authorities have to work together. In Rio de Janeiro’s “control centre”, officials from several departments watch screens side by side. When a thunderstorm strikes, the airport and schools can be closed and traffic redirected from this single centre. In newly built places institutions have to be designed along with the infrastructure. Simon Giles of Accenture, a consulting firm, explains that when his firm worked on a “creative digital city” in Guadalajara, Mexico, a structure of trusts was created to oversee the development and running of the place, helping to ensure that the benefits of development are shared equally within the community.

Another problem is how to pay for all the analytics and new infrastructure in cities. Private provision is one possibility. For example, German insurers are providing their customers with weather-warning systems developed at FOKUS, says Ulrich Meissen, head of the institute’s electronic-safety department. Cisco’s Mr Elfrink points out that cities themselves could charge for many smart services. Residents might pay a few dollars a month for online medical consultations, alerts that their children have reached school or internet access on the bus.

But quite a lot of things to make city life better can be done inexpensively by residents themselves. Many governments and cities are encouraging this by making public data available. The European Union is sponsoring a project called CitySDK, involving eight cities from Manchester to Istanbul, to give developers data and tools to create digital urban services. One pilot, in Helsinki, is meant to make it easier for citizens to report problems. Another, in Amsterdam, will use real-time traffic data to allow people to find the best way around town and avoid traffic jams. A third, in Lisbon, will guide tourists.

Mr Biderman says there is an even richer seam to be mined as people find and create their own data in real time. For example, air quality can be continually monitored by cyclists and cross-checked with time, place and weather. In fact, just about any object can tell a story. An earlier SENSEable project tracked 3,000 bits of rubbish from Seattle to nearby dumps, to Portland, via Chicago to Florida and California, and to landfills all over America. Information like this may make people think about what they throw away. Some may even do something about it.

Many ideas are brewing in the world’s cities, from grand projects to single apps. Some will be dead ends; others will rely on the enthusiasm of citizens, which will not always be in plentiful supply. But in lots of imperceptible ways, from better traffic management to bins that tweet when they are ready to be emptied, city life is getting better.

Pew: Mobile is the Needle; Social is the Thread

Kathryn Zickuhr, Pew Research Center’s Internet & American Life Project, October 18, 2012

"Examining more than a decade of data on the social impact of technology in America, Pew Internet Research Analyst Kathryn Zickuhr discussed the patterns and trends shaping the new messaging realities of the digital age at the WSU Elliott School of Communications’ annual Comm Week conference."

Building A Culture Around Big Data

Deanna Glick, AOL Government, October 16, 2012

A report released today by the Partnership for Public Service aims to educate federal managers on how agencies can do just that. The report, From Data to Decisions II: Building an Analytics Culture, examines how to best use data – not anecdotes – to base decisions.

Building on an original report released last November that examined how several federal agencies use data, the new report identifies strategies for how to develop and grow an analytics culture within agencies and incorporate it into how federal workers perform the mission. It profiles seven agencies using analytics to achieve better results and the strategies used in a budget-cutting climate.

Both reports were joint efforts between the partnership and the IBM Center for The Business of Government.

"By sharing compelling stories of how agencies are developing, growing and sustaining their analytics and performance-management approaches, we hope to shed light on key steps and processes that are transferable to other agencies," the report states.

To complete the report, the organizations studies how agencies are using analytics; how they got started; what conditions helped to grow their approaches; what challenges arose and why; and what success looks like.

"We found many parallels in approach across agencies and programs," according to the report. "Driven by budget realities and the push for more data-driven actions, agency managers were examining their programs in a disciplined, comprehensive way to determine how they conduct their business."

The report features details of analytics efforts at agencies within the departments of Homeland Security, Health and Human Services, Interior, Defense and Treasury.

A common successful first step in creating a culture around analytics, researchers found, was agencies tying specific activities directly to what they are intended to achieve and linking them to goals. Focusing on these details help agencies employ a data-driven approach to managing programs, identify critical information to gauge progress and results, and ensure that only those activities that are key or essential to meeting desired results are performed.

To improve airport security, for example, a federal security director with the Transportation Security Administration worked with a team to break down the job of a transportation security officer at checkpoint and baggage areas. After analyzing and brainstorming around specific tasks related to the job, his team identified more than 1,300 knowledge areas, values and skills for a transportation security officer. Based on this analysis, they identified vulnerabilities in security screening and uncovered weaknesses in training, procedures or technology. They then pinpointed what could be improved through training and better application of procedures or policy and where technology could support improved performance.

"By instituting these types of systematic processes, agencies start building analytic cultures so they can look critically at what they do and thoroughly understand how their activities can lead to better results," the report states. "The reward for their meticulous appraisal is the enhanced ability to serve the American public cost-effectively and efficiently."
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Gartner Says Big Data Creates Big Jobs: 4.4 Million IT Jobs Globally to Support Big Data By 2015

Analysts Discuss Key Issues Facing the IT Industry During Gartner Symposium/ITxpo 2012, October 21-25, in Orlando

ORLANDO, Fla.--(BUSINESS WIRE)-- Worldwide IT spending is forecast to surpass $3.7 trillion in 2013, a 3.8 percent increase from 2012 projected spending of $3.6 trillion, but it's the outlook for big data that is creating much excitement, according to Gartner, Inc.

"By 2015, 4.4 million IT jobs globally will be created to support big data, generating 1.9 million IT jobs in the United States," said Peter Sondergaard, senior vice president at Gartner and global head of Research. "In addition, every big data-related role in the U.S. will create employment for three people outside of IT, so over the next four years a total of 6 million jobs in the U.S. will be generated by the information economy."

"But there is a challenge. There is not enough talent in the industry. Our public and private education systems are failing us. Therefore, only one-third of the IT jobs will be filled. Data experts will be a scarce, valuable commodity," Mr. Sondergaard said. "IT leaders will need immediate focus on how their organization develops and attracts the skills required. These jobs will be needed to grow your business. These jobs are the future of the new information economy."

Mr. Sondergaard provided the latest outlook for the IT industry today to an audience of more than 8,000 CIOs and IT leaders at Gartner Symposium/ITxpo, which is taking place here through October 25. He said the IT industry is entering the Nexus of Forces, which includes a confluence and integration of cloud, social collaboration, mobile and information.

"This is a time of accelerating change, where your current IT architecture will be rendered obsolete," Mr. Sondergaard said. "You must lead through this change, selectively destroy low impact systems, and aggressively change your IT cost structure. This is the New World of the Nexus, the next age of computing."

Cloud
The cloud is the carrier for the three other Forces: mobile is personal cloud, social media is only possible via the cloud, and big data is the killer app for the cloud. Cloud will be the permanent fixture, the foundation.

"Cloud is not merely about cost-cutting, the end game is not just cheap on-demand services. In fact, 90 percent of these services are still subscription based, not pay-as-you-go," Mr. Sondergaard said. "We are just at the beginning of realizing the cost benefits of cloud, but organizations moving to the cloud are also attracted by the new capabilities they do not get today. It is bringing new approaches to designing applications, specifically for the cloud, and providing more resilience by architecturing failure as a design concept. Cloud also teaches us about services and service levels, and the contrast between what the business wants for outcomes versus IT's old methods of getting there."

Mobile
In 2016, more than 1.6 billion smart mobile devices will be purchased globally. Two-thirds of the mobile workforce will own a smartphone, and 40 percent of the workforce will be mobile. The challenge for IT leaders is determining what to do with this new channel to their customers and employees.

"Mobile is about computing at the right time, in the moment. It is the point of entry for all applications, delivering personalized, contextual experiences," Mr. Sondergaard said. "It means: marketing gets more time with the customer; employees become more productive; and process flows get dramatically cut."

In less than two years, iPads will be more common in business than Blackberries. Mr. Sondergaard said some CIOs are now placing orders for tens of thousands of iPads at a time. Productivity is the driver. Two years from now, 20 percent of sales organizations will use tablets as the primary mobile platform for their field sales force. As a result, by 2018, 70 percent of mobile workers will use a tablet or a hybrid device that has tablet-like characteristics.

Gartner forecasts that in 2016, half of all non-PC devices will be purchased by employees. By the end of the decade, half of all devices in business will be purchased by employees.

Social Computing
In the next three years, the dominant consumer social networks will the limits of their growth. However, social computing will become even more important. Companies are establishing social media as a discipline. Gartner predicts that in three years, 10 organizations will each spend more than $1 billion on social media.

"Social computing is moving from being just on the outside of the organization to being at the core of business operations," Mr. Sondergaard said. "It is changing the fundamentals of management: how you establish a sense of purpose and motivate people to act. Social computing will move organizations from hierarchical structures and defined teams to communities that can cross any organizational boundary."

Big Data
By tapping a continual stream of information from internal and external sources, businesses today have an endless array of new opportunities for: transforming decision-making; discovering new insights; optimizing the business; and innovating their industries.

Big data creates a new layer in the economy which is all about information, turning information, or data, into revenue. This will accelerate growth in the global economy and create jobs.

"Big data is about looking ahead, beyond what everybody else sees," Mr. Sondergaard said. "You need to understand how to deal with hybrid data, meaning the combination of structured and unstructured data, and how you shine a light on 'dark data.' Dark data is the data being collected, but going unused despite its value. Leading organizations of the future will be distinguished by the quality of their predictive algorithms. This is the CIO challenge, and opportunity."

About Gartner Symposium/ITxpo
Gartner Symposium/ITxpo is the world's most important gathering of CIOs and senior IT executives. This event delivers independent and objective content with the authority and weight of the world's leading IT research and advisory organization, and provides access to the latest solutions from key technology providers. Gartner's annual Symposium/ITxpo events are key components of attendees' annual planning efforts. IT executives rely on Gartner Symposium/ITxpo to gain insight into how their organizations can use IT to address business challenges and improve operational efficiency.

Additional information about Gartner Symposium/ITxpo in Orlando, is available at www.gartner.com/symposium/us. Video replays of keynotes and sessions are available on Gartner Events on Demand at www.gartnerondemand.com. Follow news, photos and video coming from Gartner Symposium/ITxpo on Facebook at http://www.facebook.com/GartnerSymposium, and on Twitter at http://twitter.com/Gartner_inc and using #GartnerSym.