Archive for maio \31\+00:00 2009

Secret of Googlenomics: Data-Fueled Recipe Brews Profitability (Segredo do Googlenomics: Receita lubrificada de dados alimenta lucratividade)

maio 31, 2009

Eis aqui o Prof. Hal Varian, Chief Economist do Google, fazendo sucesso na revista Wired mais recente!


Wire Magazine: 17.06

Secret of Googlenomics: Data-Fueled Recipe Brews Profitability

By Steven Levy  05.22.09


In the midst of financial apocalypse, the gadflies and gurus of the global marketplace are gathered at the San Francisco Hilton for the annual meeting of the American Economics Association. The mood is similar to a seismologist convention in the wake of the Big One. Yet surprisingly, one of the most popular sessions has nothing to do with toxic assets, derivatives, or unemployment curves.

“I’m going to talk about online auctions,” says Hal Varian, the session’s first speaker. Varian is a lanky 62-year-old professor at UC Berkeley’s Haas School of Business and School of Information, but these days he’s best known as Google’s chief economist. This morning’s crowd hasn’t come for predictions about the credit market; they want to hear about Google’s secret sauce.

Varian is an expert on what may be the most successful business idea in history: AdWords, Google’s unique method for selling online advertising. AdWords analyzes every Google search to determine which advertisers get each of up to 11 “sponsored links” on every results page. It’s the world’s biggest, fastest auction, a never-ending, automated, self-service version of Tokyo’s boisterous Tsukiji fish market, and it takes place, Varian says, “every time you search.” He never mentions how much revenue advertising brings in. But Google is a public company, so anyone can find the number: It was $21 billion last year.

His talk quickly becomes technical. There’s the difference between the Generalized Second Price auction model and the Vickrey-Clark-Groves alternative. Game theory takes a turn; so does the Nash Equilibrium. Terms involving the c-word—as in clicks—get tossed around like beach balls at a summer rock festival. Clickthrough rate. Cost per click. Supply curve of clicks. The audience is enthralled.

During the question-and-answer period, a man wearing a camel-colored corduroy blazer raises his hand. “Let me understand this,” he begins, half skeptical, half unsure. “You say that an auction happens every time a search takes place? That would mean millions of times a day!”

Varian smiles. “Millions,” he says, “is actually quite an understatement.”

Why does Google even need a chief economist? The simplest reason is that the company is an economy unto itself. The ad auction, marinated in that special sauce, is a seething laboratory of fiduciary forensics, with customers ranging from giant multinationals to dorm-room entrepreneurs, all billed by the world’s largest micropayment system.

Google depends on economic principles to hone what has become the search engine of choice for more than 60 percent of all Internet surfers, and the company uses auction theory to grease the skids of its own operations. All these calculations require an army of math geeks, algorithms of Ramanujanian complexity, and a sales force more comfortable with whiteboard markers than fairway irons.

Varian, an upbeat, avuncular presence at the Googleplex in Mountain View, California, serves as the Adam Smith of the new discipline of Googlenomics. His job is to provide a theoretical framework for Google’s business practices while leading a team of quants to enforce bottom-line discipline, reining in the more propellerhead propensities of the company’s dominant engineering culture.

Googlenomics actually comes in two flavors: macro and micro. The macroeconomic side involves some of the company’s seemingly altruistic behavior, which often baffles observers. Why does Google give away products like its browser, its apps, and the Android operating system for mobile phones? Anything that increases Internet use ultimately enriches Google, Varian says. And since using the Web without using Google is like dining at In-N-Out without ordering a hamburger, more eyeballs on the Web lead inexorably to more ad sales for Google.

The microeconomics of Google is more complicated. Selling ads doesn’t generate only profits; it also generates torrents of data about users’ tastes and habits, data that Google then sifts and processes in order to predict future consumer behavior, find ways to improve its products, and sell more ads. This is the heart and soul of Googlenomics. It’s a system of constant self-analysis: a data-fueled feedback loop that defines not only Google’s future but the future of anyone who does business online.

When the American Economics Association meets next year, the financial crisis may still be topic A. But one of the keynote speakers has already been chosen: Googlenomist Hal Varian.

Ironically, economics was a distant focus in the first days of Google. After Larry Page and Sergey Brin founded the company in 1998, they channeled their energy into its free search product and left much of the business planning to a 22-year-old Stanford graduate named Salar Kamangar, Google’s ninth employee. The early assumption was that although ads would be an important source of revenue, licensing search technology and selling servers would be just as lucrative. Page and Brin also believed that ads should be useful and welcome—not annoying intrusions. Kamangar and another early Googler, Eric Veach, set out to implement that ideal. Neither had a background in business or economics. Kamangar had been a biology major, and Veach’s field of study was computer science.


Hal Varian, high priest of Googlenomics.
Photo: Joe Pugliese

Google’s ads were always plain blocks of text relevant to the search query. But at first, there were two kinds. Ads at the top of the page were sold the old-fashioned way, by a crew of human beings headquartered largely in New York City. Salespeople wooed big customers over dinner, explaining what keywords meant and what the prices were. Advertisers were then billed by the number of user views, or impressions, regardless of whether anyone clicked on the ad. Down the right side were other ads that smaller businesses could buy directly online. The first of these, for live mail-order lobsters, was sold in 2000, just minutes after Google deployed a link reading see your ad here.

But as the business grew, Kamangar and Veach decided to price the slots on the side of the page by means of an auction. Not an eBay-style auction that unfolds over days or minutes as bids are raised or abandoned, but a huge marketplace of virtual auctions in which sealed bids are submitted in advance and winners are determined algorithmically in fractions of a second. Google hoped that millions of small and medium companies would take part in the market, so it was essential that the process be self-service. Advertisers bid on search terms, or keywords, but instead of bidding on the price per impression, they were bidding on a price they were willing to pay each time a user clicked on the ad. (The bid would be accompanied by a budget of how many clicks the advertiser was willing to pay for.) The new system was called AdWords Select, while the ads at the top of the page, with prices still set by humans, was renamed AdWords Premium.

One key innovation was that all the sidebar slots on the results page were sold off in a single auction. (Compare that to an early pioneer of auction-driven search ads, Overture, which held a separate auction for each slot.) The problem with an all-at-once auction, however, was that advertisers might be inclined to lowball their bids to avoid the sucker’s trap of paying a huge amount more than the guy just below them on the page. So the Googlers decided that the winner of each auction would pay the amount (plus a penny) of the bid from the advertiser with the next-highest offer. (If Joe bids $10, Alice bids $9, and Sue bids $6, Joe gets the top slot and pays $9.01. Alice gets the next slot for $6.01, and so on.) Since competitors didn’t have to worry about costly overbidding errors, the paradoxical result was that it encouraged higher bids.

“Eric Veach did the math independently,” Kamangar says. “We found out along the way that second-price auctions had existed in other forms in the past and were used at one time in Treasury auctions.” (Another crucial innovation had to do with ad quality, but more on that later.)

Google’s homemade solution to its ad problem impressed even Paul Milgrom, the Stanford economist who is to auction theory what Letitia Baldridge is to etiquette. “I’ve begun to realize that Google somehow stumbled on a level of simplification in ad auctions that was not included before,” he says. And applying a variation on second-price auctions wasn’t just a theoretical advance. “Google immediately started getting higher prices for advertising than Overture was getting.”

Google hired Varian in May 2002, a few months after implementing the auction- based version of AdWords. The offer came about when Google’s then-new CEO, Eric Schmidt, ran into Varian at the Aspen Institute and they struck up a conversation about Internet issues. Schmidt was with Larry Page, who was pushing his own notions about how some of the big problems in business and science could be solved by using computation and analysis on an unprecedented scale. Varian remembers thinking, “Why did Eric bring his high-school nephew?”

Schmidt, whose father was an economist, invited Varian to spend a day or two a week at Google. On his first visit, Varian asked Schmidt what he should do. “Why don’t you take a look at the ad auction?” Schmidt said.

Google had already developed the basics of AdWords, but there was still plenty of tweaking to do, and Varian was uniquely qualified to “take a look.” As head of the information school at UC Berkeley and coauthor (with Carl Shapiro) of a popular book called Information Rules: A Strategic Guide to the Network Economy, he was already the go-to economist on ecommerce.

At the time, most online companies were still selling advertising the way it was done in the days of Mad Men. But Varian saw immediately that Google’s ad business was less like buying traditional spots and more like computer dating. “The theory was Google as yenta—matchmaker,” he says. He also realized there was another old idea underlying the new approach: A 1983 paper by Harvard economist Herman Leonard described using marketplace mechanisms to assign job candidates to slots in a corporation, or students to dorm rooms. It was called a two-sided matching market. “The mathematical structure of the Google auction,” Varian says, “is the same as those two-sided matching markets.”

Varian tried to understand the process better by applying game theory. “I think I was the first person to do that,” he says. After just a few weeks at Google, he went back to Schmidt. “It’s amazing!” Varian said. “You’ve managed to design an auction perfectly.”

To Schmidt, who had been at Google barely a year, this was an incredible relief. “Remember, this was when the company had 200 employees and no cash,” he says. “All of a sudden we realized we were in the auction business.”

It wasn’t long before the success of AdWords Select began to dwarf that of its sister system, the more traditional AdWords Premium. Inevitably, Veach and Kamangar argued that all the ad slots should be auctioned off. In search, Google had already used scale, power, and clever algorithms to change the way people accessed information. By turning over its sales process entirely to an auction-based system, the company could similarly upend the world of advertising, removing human guesswork from the equation.

The move was risky. Going ahead with the phaseout—nicknamed Premium Sunset—meant giving up campaigns that were selling for hundreds of thousands of dollars, for the unproven possibility that the auction process would generate even bigger sums. “We were going to erase a huge part of the company’s revenue,” says Tim Armstrong, then head of direct sales in the US. (This March, Armstrong left Google to become AOL’s new chair and CEO.) “Ninety-nine percent of companies would have said, ‘Hold on, don’t make that change.’ But we had Larry, Sergey, and Eric saying, ‘Let’s go for it.'”

News of the switch jacked up the Maalox consumption among Google’s salespeople. Instead of selling to corporate giants, their job would now be to get them to place bids in an auction? “We thought it was a little half-cocked,” says Jeff Levick, an early leader of the Google sales team. The young company wasn’t getting rid of its sales force (though the system certainly helped Google run with far fewer salespeople than a traditional media company) but was asking them to get geekier, helping big customers shape online strategies as opposed to simply selling ad space.

Levick tells a story of visiting three big customers to inform them of the new system: “The guy in California almost threw us out of his office and told us to fuck ourselves. The guy in Chicago said, ‘This is going to be the worst business move you ever made.’ But the guy in Massachusetts said, ‘I trust you.'”

That client knew math, says Levick, whose secret weapon was the numbers. When the data was crunched—and Google worked hard to give clients the tools needed to run the numbers themselves—advertisers saw that the new system paid off for them, too.

AdWords was such a hit that Google went auction-crazy. The company used auctions to place ads on other Web sites (that program was dubbed AdSense). “But the really gutsy move,” Varian says, “was using it in the IPO.” In 2004, Google used a variation of a Dutch auction for its IPO; Brin and Page loved that the process leveled the playing field between small investors and powerful brokerage houses. And in 2008, the company couldn’t resist participating in the FCC’s auction to reallocate portions of the radio spectrum.

Google even uses auctions for internal operations, like allocating servers among its various business units. Since moving a product’s storage and computation to a new data center is disruptive, engineers often put it off. “I suggested we run an auction similar to what the airlines do when they oversell a flight. They keep offering bigger vouchers until enough customers give up their seats,” Varian says. “In our case, we offer more machines in exchange for moving to new servers. One group might do it for 50 new ones, another for 100, and another won’t move unless we give them 300. So we give them to the lowest bidder—they get their extra capacity, and we get computation shifted to the new data center.”

The transition to an all-auction sales model was a milestone for Google, ensuring that its entire revenue engine would run with the same computer-science fervor as its search operation. Now, when Google recruits alpha geeks, it is just as likely to have them focus on AdWords as on search or apps.

The across-the-board emphasis on engineering, mathematical formulas, and data-mining has made Google a new kind of company. But to fully understand why, you have to go back and look under AdWords’ hood.

Most people think of the Google ad auction as a straightforward affair. In fact, there’s a key component that few users know about and even sophisticated advertisers don’t fully understand. The bids themselves are only a part of what ultimately determines the auction winners. The other major determinant is something called the quality score. This metric strives to ensure that the ads Google shows on its results page are true, high-caliber matches for what users are querying. If they aren’t, the whole system suffers and Google makes less money.

Google determines quality scores by calculating multiple factors, including the relevance of the ad to the specific keyword or keywords, the quality of the landing page the ad is linked to, and, above all, the percentage of times users actually click on a given ad when it appears on a results page. (Other factors, Google won’t even discuss.) There’s also a penalty invoked when the ad quality is too low—in such cases, the company slaps a minimum bid on the advertiser. Google explains that this practice—reviled by many companies affected by it—protects users from being exposed to irrelevant or annoying ads that would sour people on sponsored links in general. Several lawsuits have been filed by would-be advertisers who claim that they are victims of an arbitrary process by a quasi monopoly.

You can argue about fairness, but arbitrary it ain’t. To figure out the quality score, Google needs to estimate in advance how many users will click on an ad. That’s very tricky, especially since we’re talking about billions of auctions. But since the ad model depends on predicting clickthroughs as perfectly as possible, the company must quantify and analyze every twist and turn of the data. Susan Wojcicki, who oversees Google’s advertising, refers to it as “the physics of clicks.”

During Varian’s second summer in Mountain View, when he was still coming in only a day or two a week, he asked a recently hired computer scientist from Stanford named Diane Tang to create the Google equivalent of the Consumer Price Index, called the Keyword Pricing Index. “Instead of a basket of goods like diapers and beer and doughnuts, we have keywords,” says Tang, who is known internally as the Queen of Clicks.

The Keyword Pricing Index is a reality check. It alerts Google to any anomalous price bubbles, a sure sign that an auction isn’t working properly. Categories are ranked by the cost per click that advertisers generally have to pay, weighted by distribution, and then separated into three bundles: high cap, mid cap, and low cap. “The high caps are very competitive keywords, like ‘flowers’ and ‘hotels,'” Tang says. In the mid-cap realm you have keywords that may vary seasonally—the price to place ads alongside results for “snowboarding” skyrockets during the winter. Low caps like “Massachusetts buggy whips” are the stuff of long tails.

Tang’s index is just one example of a much broader effort. As the amount of data at the company’s disposal grows, the opportunities to exploit it multiply, which ends up further extending the range and scope of the Google economy. So it’s utterly essential to calculate correctly the quality scores that prop up AdWords.

“The people working for me are generally econometricians—sort of a cross between statisticians and economists,” says Varian, who moved to Google full-time in 2007 (he’s on leave from Berkeley) and leads two teams, one of them focused on analysis.

“Google needs mathematical types that have a rich tool set for looking for signals in noise,” says statistician Daryl Pregibon, who joined Google in 2003 after 23 years as a top scientist at Bell Labs and AT&T Labs. “The rough rule of thumb is one statistician for every 100 computer scientists.”

Keywords and click rates are their bread and butter. “We are trying to understand the mechanisms behind the metrics,” says Qing Wu, one of Varian’s minions. His specialty is forecasting, so now he predicts patterns of queries based on the season, the climate, international holidays, even the time of day. “We have temperature data, weather data, and queries data, so we can do correlation and statistical modeling,” Wu says. The results all feed into Google’s backend system, helping advertisers devise more-efficient campaigns.

To track and test their predictions, Wu and his colleagues use dozens of onscreen dashboards that continuously stream information, a sort of Bloomberg terminal for the Googlesphere. Wu checks obsessively to see whether reality is matching the forecasts: “With a dashboard, you can monitor the queries, the amount of money you make, how many advertisers you have, how many keywords they’re bidding on, what the rate of return is for each advertiser.”

Wu calls Google “the barometer of the world.” Indeed, studying the clicks is like looking through a window with a panoramic view of everything. You can see the change of seasons—clicks gravitating toward skiing and heavy clothes in winter, bikinis and sunscreen in summer—and you can track who’s up and down in pop culture. Most of us remember news events from television or newspapers; Googlers recall them as spikes in their graphs. “One of the big things a few years ago was the SARS epidemic,” Tang says. Wu didn’t even have to read the papers to know about the financial meltdown—he saw the jump in people Googling for gold. And since prediction and analysis are so crucial to AdWords, every bit of data, no matter how seemingly trivial, has potential value.

Since Google hired Varian, other companies, like Yahoo, have decided that they, too, must have a chief economist heading a division that scrutinizes auctions, dashboards, and econometric models to fine-tune their business plan. In 2007, Harvard economist Susan Athey was surprised to get a summons to Redmond to meet with Steve Ballmer. “That’s a call you take,” she says. Athey spent last year working in Microsoft’s Cambridge, Massachusetts, office.

Can the rest of the world be far behind? Although Eric Schmidt doesn’t think it will happen as quickly as some believe, he does think that Google-style auctions are applicable to all sorts of transactions. The solution to the glut in auto inventory? Put the entire supply of unsold cars up for bid. That’ll clear out the lot. Housing, too: “People use auctions now in cases of distress, like auctioning a house when there are no buyers,” Schmidt says. “But you can imagine a situation in which it was a normal and routine way of doing things.”

Varian believes that a new era is dawning for what you might call the datarati—and it’s all about harnessing supply and demand. “What’s ubiquitous and cheap?” Varian asks. “Data.” And what is scarce? The analytic ability to utilize that data. As a result, he believes that the kind of technical person who once would have wound up working for a hedge fund on Wall Street will now work at a firm whose business hinges on making smart, daring choices—decisions based on surprising results gleaned from algorithmic spelunking and executed with the confidence that comes from really doing the math.

It’s a satisfying development for Varian, a guy whose career as an economist was inspired by a sci-fi novel he read in junior high. “In Isaac Asimov’s first Foundation Trilogy, there was a character who basically constructed mathematical models of society, and I thought this was a really exciting idea. When I went to college, I looked around for that subject. It turned out to be economics.” Varian is telling this story from his pied-è0-Plex, where he sometimes stays during the week to avoid driving the 40-some miles from Google headquarters to his home in the East Bay. It happens to be the ranch-style house, which Google now owns, where Brin and Page started the company.

There’s a wild contrast between this sparsely furnished residence and what it has spawned—dozens of millionaire geeks, billions of auctions, and new ground rules for businesses in a data-driven society that is far weirder than the one Asimov envisioned nearly 60 years ago. What could be more baffling than a capitalist corporation that gives away its best services, doesn’t set the prices for the ads that support it, and turns away customers because their ads don’t measure up to its complex formulas? Varian, of course, knows that his employer’s success is not the result of inspired craziness but of an early recognition that the Internet rewards fanatical focus on scale, speed, data analysis, and customer satisfaction. (A bit of auction theory doesn’t hurt, either.) Today we have a name for those rules: Googlenomics. Learn them, or pay the price.

Senior writer Steven Levy ( wrote about the Kryptos sculpture at CIA headquarters in issue 17.05.

The Future of Manufacturing, GM, and American Workers (Part I) (O Futuro da Indústria de Transformação, GM, e Trabalhores Americanos (Parte I)

maio 31, 2009

O post abaixo veio do blog de Robert Reich, que foi Secretário do Trabalho no Governo do Presidente Bill Clinton!


Friday, May 29, 2009

The Future of Manufacturing, GM, and American Workers (Part I)  What’s the Administration’s specific aim in bailing out GM? I’ll give you my theory later.

For now, though, some background. First and most broadly, it doesn’t make sense for America to try to maintain or enlarge manufacturing as a portion of the economy. Even if the U.S. were to seal its borders and bar any manufactured goods from coming in from abroad–something I don’t recommend–we’d still be losing manufacturing jobs. That’s mainly because of technology.

When we think of manufacturing jobs, we tend to imagine old-time assembly lines populated by millions of blue-collar workers who had well-paying jobs with good benefits. But that picture no longer describes most manufacturing. I recently toured a U.S. factory containing two employees and 400 computerized robots. The two live people sat in front of computer screens and instructed the robots. In a few years this factory won’t have a single employee on site, except for an occasional visiting technician who repairs and upgrades the robots.

Factory jobs are vanishing all over the world. Even China is losing them. The Chinese are doing more manufacturing than ever, but they’re also becoming far more efficient at it. They’ve shuttered most of the old state-run factories. Their new factories are chock full of automated and computerized machines. As a result, they don’t need as many manufacturing workers as before.

Economists at Alliance Capital Management took a look at employment trends in twenty large economies and found that between 1995 and 2002–before the asset bubble and subsequent bust–twenty-two million manufacturing jobs disappeared. The United States wasn’t even the biggest loser. We lost about 11% of our manufacturing jobs in that period, but the Japanese lost 16% of theirs. Even developing nations lost factory jobs: Brazil suffered a 20% decline, and China had a 15% drop.

What happened to manufacturing? In two words, higher productivity. As productivity rises, employment falls because fewer people are needed. In this, manufacturing is following the same trend as agriculture. A century ago, almost 30% of adult Americans worked on a farm. Nowadays, fewer than 5% do. That doesn’t mean the U.S. failed at agriculture. Quite the opposite. American agriculture is a huge success story. America can generate far larger crops than a century ago with far fewer people. New technologies, more efficient machines, new methods of fertilizing, better systems of crop rotation, and efficiencies of large scale have all made farming much more productive.

Manufacturing is analogous. In America and elsewhere around the world, it’s a success. Since 1995, even as manufacturing employment has dropped around the world, global industrial output has risen more than 30%.

We should stop pining after the days when millions of Americans stood along assembly lines and continuously bolted, fit, soldered or clamped what went by. Those days are over. And stop blaming poor nations whose workers get very low wages. Of course their wages are low; these nations are poor. They can become more prosperous only by exporting to rich nations. When America blocks their exports by erecting tariffs and subsidizing our domestic industries, we prevent them from doing better. Helping poorer nations become more prosperous is not only in the interest of humanity but also wise because it lessens global instability.

Want to blame something? Blame new knowledge. Knowledge created the electronic gadgets and software that can now do almost any routine task. This goes well beyond the factory floor. America also used to have lots of elevator operators, telephone operators, bank tellers and service-station attendants. Remember? Most have been replaced by technology. Supermarket check-out clerks are being replaced by automatic scanners. The Internet has taken over the routine tasks of travel agents, real estate brokers, stock brokers and even accountants. With digitization and high-speed data networks a lot of back office work can now be done more cheaply abroad.

Any job that’s even slightly routine is disappearing from the U.S. But this doesn’t mean we are left with fewer jobs. It means only that we have fewer routine jobs, including traditional manufacturing. When the U.S. economy gets back on track, many routine jobs won’t be returning–but new jobs will take their place. A quarter of all Americans now work in jobs that weren’t listed in the Census Bureau’s occupation codes in 1967. Technophobes, neo-Luddites and anti-globalists be warned: You’re on the wrong side of history. You see only the loss of old jobs. You’re overlooking all the new ones.

The reason they’re so easy to overlook is that so much of the new value added is invisible. A growing percent of every consumer dollar goes to people who analyze, manipulate, innovate and create. These people are responsible for research and development, design and engineering. Or for high-level sales, marketing and advertising. They’re composers, writers and producers. They’re lawyers, journalists, doctors and management consultants. I call this “symbolic analytic” work because most of it has to do with analyzing, manipulating and communicating through numbers, shapes, words, ideas.

Symbolic-analytic work can’t be directly touched or held in your hands, as goods that come out of factories can be. In fact, many of these tasks are officially classified as services rather than manufacturing. Yet almost whatever consumers buy these days, they’re paying more for these sorts of tasks than for the physical material or its assemblage. On the back of every iPod is the notice “Designed by Apple in California, Assembled in China.” You can bet iPod’s design garners a bigger share of the iPod’s purchase price than its assembly.

The biggest challenge we face over the long term — beyond the current depression — isn’t how to bring manufacturing back. It’s how to improve the earnings of America’s expanding army of low-wage workers who are doing personal service jobs in hotels, hospitals, big-box retail stores, restaurant chains, and all the other businesses that need bodies but not high skills. More on that to come.


Time Warner to spin off AOL (Time Warner vai separar a AOL)

maio 29, 2009

Eis aí uma notícia que o mercado estava esperando há algum tempo!   E ela veio da!


Time Warner to spin off AOL

May 28, 2009 5:18 AM PDT

by Jon Skillings

Update at 6:54 a.m. PDT: Added Time Warner’s stock movement for Thursday morning, along with background on the history of AOL and Time Warner.

Media giant Time Warner announced Thursday morning that it will cast off its AOL division to become a standalone company. 

Before that separation can take place, Time Warner will buy the 5 percent of AOL owned by Google so that it will have 100 percent control of AOL. Time Warner expects the transaction with Google to take place in the third quarter and the final AOL spinoff around the end of the year.

As an independent, publicly traded company, AOL will focus on growing its Web brands and services, as well as its advertising business, according to Time Warner.

Time Warner CEO Jeff Bewkes said in a statement:


We believe that a separation will be the best outcome for both Time Warner and AOL. The separation will be another critical step in the reshaping of Time Warner that we started at the beginning of last year, enabling us to focus to an even greater degree on our core content businesses. The separation will also provide both companies with greater operational and strategic flexibility. We believe AOL will then have a better opportunity to achieve its full potential as a leading independent Internet company.


The separation of AOL from Time Warner will put a final nail in the coffin of one of the emblematic mergers of the dot-com boom and bust. Brimming with the abundant funds and hyper-optimism of the Web’s go-go years, AOL–then known as America Online–acquired Time Warner in January 2001 to create the world’s largest media company. Within a year, however, it was already apparent that the union of new and old media was not as “supercharged” as its backers had promised.

By September 2003, things had gone so poorly with the merger, and specifically with AOL’s dwindling dominance as an online portal, that AOL Time Warner dropped “AOL” from its name, and became just Time Warner.

In subsequent years, AOL has continued to struggle. Consumers have dropped their dial-up subscriptions in droves as broadband access became more widely available and they became more comfortable navigating the Internet on their own.

The first quarter of 2009, like other quarters before it, showed just how much of a burden AOL had become on Time Warner. For that three-month period, AOL’s revenue dropped 23 percent from the year-earlier quarter, and Time Warner CEO Jeff Bewkes emphasized at the time that the company was seeking “the right ownership structure for AOL.”

In March, several weeks ahead of that earnings announcement, Time Warner appointed a new chief executive for AOL, Tim Armstrong, calling him “the right executive to move AOL into the next phase of its evolution.” Armstrong had previously been a leader of Google’s advertising sales operations.

Time Warner shares began trading up slightly Thursday morning following the AOL news, starting the day at $23.34 after closing Wednesday at $23.

Jonathan Skillings is managing editor of CNET News, based in the Boston bureau. He’s been with CNET since 2000, after a decade in tech journalism at the IDG News Service, PC Week, and an AS/400 magazine. He’s also been a soldier and a schoolteacher. Email Jon.

Gartner Highlights Five Questions Every CIO Should Ask to Ensure Value From Enterprise Architecture (Gartner aponta cinco questões que todo CIO deveria perguntar para assegurar valor a partir de Arquitetura Empresarial)

maio 28, 2009

Eis-me aqui novamente revendo notícias sobre Arquitetura Empresarial.  Vejam o que o blog  reportou sobre um trabalho do Gartner (que ainda não vi)!


Gartner Highlights Five Questions Every CIO Should Ask to Ensure Value From Enterprise Architecture


In the current economic climate, an effective enterprise architecture (EA) program is a necessity, not a luxury, so Gartner, Inc. has identified five questions that will help CIOs ensure that their EA initiative is on track to deliver business value.

Gartner defines EA as the process of translating business vision and strategy into effective enterprise change. A business-driven EA plan will help identify cost optimization opportunities and ensure a rational approach to investment by balancing the needs of today with tomorrow’s growth opportunities.

“CIOs seem to intuitively realize the value that EA can deliver, but we find that many organizations continue to struggle with it,” said Anne Lapkin, research vice president at Gartner. “Often EA is not well understood elsewhere in the organization, and the EA teams are not doing a good enough job of demonstrating or articulating its value. Further complicating matters is the fact that the increased importance of cost optimization efforts means that EA teams often need to recast their initiatives in light of changing enterprise priorities.”

Ms. Lapkin said that ensuring that EA efforts support the changing priorities of the business and focus on short- as well as long-term value is critical. To this end, Gartner has devised five key questions that the CIO can ask to ensure that the EA program is on track.

Is the value proposition of the EA initiative specific to the enterprise and articulated in business terms? Business leaders are interested in achieving the business goals that are defined for the company, and it is the architect’s ability to express how the architecture will contribute to these efforts that will make the difference between support for the architecture and tolerance (or indeed, indifference). A corollary to this question is, “Is it written down?” Too often, chief architects rely on the idea that the value proposition is well understood to the enterprise, forgetting that anything that is not made explicit is open to interpretation by different stakeholders.

Has the value proposition been refocused as enterprise priorities have changed? Clearly, the current climate of economic uncertainty has changed business priorities. It is important not to forget that EA is an iterative process. The EA team should re-evaluate its priorities periodically as part of that process. Not every enterprise is drastically cutting expenses because it’s the only way to survive. Some are using the current environment to expand into new markets. EA teams should take the opportunity to refine their value propositions to reflect the current business priorities and to publicly reaffirm their commitment to achieving business goals to demonstrate that it is in tune with this business overall.

Do the architects emphasize the value of the process rather than the value of the deliverables? In many organizations, there is an inappropriate focus by the architecture team on the production of “artifacts” rather than the facilitative process of EA. Instead, the focus needs to be on enabling enterprise change — EA is the process that articulates strategic drivers for change, defines vision of the future state to support those strategic drivers, and provides the road map for achieving the future state and creative constraints that should be followed when executing on the road map. This should be a collaborative process, facilitated by the architects, with the real benefit to the enterprise coming from going through the process and not in any particular work that is produced.

Are performance metrics being used, and are they business-focused? In an attempt to measure the value of EA, organizations often mistakenly resort to metrics that are focused on the EA team and outputs or technical results. Often a company will measure conformance to the architecture, such as the number of waivers granted or the percentage of projects that undergo architectural review. However, Gartner maintains that measures of architectural effectiveness are no substitute for measuring business value. If the EA initiative is not delivering the business results that the enterprise needs, something will have to change. Appropriate measures might instead include improved time to market for new products or reduced costs as a percentage of revenue.

Is effective governance in place to ensure that the architecture vision is being realized? Governance and architecture go hand in hand. EA identifies high-priority business changes, and governance ensures these changes are funded and occur. If architecture guidance is not implemented, then EA deliverables count for little more than books gathering dust on the shelf. To achieve true value, the processes for using the architecture to make investment and implementation decisions must be developed at the same time that the process for creating and maintaining the architecture is defined.

“Business-driven EA represents a valuable tool for CIOs as they contend with shifting priorities, tightened budgets and increased demands for business alignment,” said Ms. Lapkin. “CIOs can ensure that their EA efforts are on track to provide business value and alignment when these questions are answered.”

Additional information is available in the Gartner report “Five Questions the CIO Should be Asking About Enterprise Architecture.” The report is available on Gartner’s Web site at

Ms. Lapkin will discuss the current state of EA, as well as the future for the market during the Gartner SOA & Application Development and Integration Summit that is being held June 24-25 at the Royal Lancaster Hotel in London. The Summit gives a complete view of service oriented architecture (SOA), application development, application integration, and emerging trends. Additional details about the Summit are available at Members of the media can register by contacting Holly Stevens at

What VCs Are Investing In (Em que o Venture Capital está investindo)

maio 26, 2009

Matéria de hoje da revista Technology Review, do MIT, EUA!

What VCs Are Investing In

Virtualization and data-management companies still show potential.

Tuesday, May 26, 2009

By Erica Naone

Credit: Technology Review

Financial conditions for technology startups have been cool, to say the least, since the economic crisis began. But now, buoyed by two recent public offerings, venture capitalists are showing a renewed interest in fledgling technology firms.

The fourth quarter of 2007 saw 17 venture-backed technology companies go public, one of the biggest spikes in stock offerings in a single quarter since the height of the dot-com boom in mid-2000. In contrast, the last quarter of 2008 and the first of 2009 saw no venture-backed public offerings in any industry, according to the National Venture Capital Association. Mergers and acquisitions have also slowed, and the number of venture funds raising money at the beginning of the year was smaller than at any time since 2003.

Two recent public stock offerings have, however, lifted the industry’s spirits. SolarWinds, a network-management software company based in Austin, TX, went public Wednesday of last week. Online restaurant reservation company OpenTable, based in San Francisco, went public the following day.

Venture capitalists attending Venture Summit East last week in Boston saw these developments as a positive sign for the industry as a whole, and they were optimistic about the potential for other startups.

“SolarWinds is evidence that software-as-a-service works,” says Sunil Dhaliwal, a general partner at Battery Ventures. Instead of selling physical software, the company’s products are distributed and maintained via the Internet. Some observers have questioned whether this approach to selling software is economically viable, especially when faced with competition from more established companies. The answer, Dhaliwal says, is “yes across the board.”

Enthusiasm for Web startups has, however, clearly changed since the height of the Web 2.0 boom. This is due partly to tighter economic constraints, but also to plummeting costs of starting Web businesses as cloud-computing infrastructure has spread. Since less capital is required to start a company, there is less need to turn to outside investors.

“I think most [Web] startup companies should not take venture-capital money,” said Jeff Fagnan, a partner at Atlas Venture, during a panel discussion. He cited, in particular, companies building lightweight Web applications or software for portable devices like the iPhone. In some cases, Fagnan said, venture capital may damage a startup by creating conditions that push the company to aim too high from the outset.

Another impact of cloud computing is that many venture capitalists are now wary of companies aiming to build large infrastructure empires themselves. Michael Skok, a general partner at North Bridge Venture Partners, says he wouldn’t touch a business looking to compete with the cloud-computing giants like Amazon and Google. “It’s impossible for a startup,” he says.

What tech startups can do, according to Skok, is fill an important research-and-development role, and he sees opportunities across the industry. For example, Skok says, for every dollar spent to store a piece of data when it’s created, companies typically spend an additional $11 to $15 backing it up and managing it. So he’s interested in startups developing new technologies that address some of the current inefficiencies in enterprise IT infrastructure. “Software is far from dead,” he says. However, Skok notes that new companies also need a big idea that will sustain them through lean times, and they can no longer bank on being acquired.

Larry Cheng, a partner at Fidelity Ventures, echoes Skok’s interest in infrastructure technology. Virtualization, which makes it possible to run different virtual computers on the same physical machine, is widely recognized as an important technological trend, he says. However, much of the technology behind virtualization products is outdated. “The entire infrastructure supporting virtualization is going to have to change,” says Cheng, who also sees opportunities for startups in security.

The mood among software entrepreneurs and investors may be colored by having suffered through the first Web bubble. But Cheng is optimistic about the current environment, partly because it’s now less frenetic. He says that interest in clean-tech startups reminds him of the Internet bubble, and he’s glad to be on the other side of it.

“Enterprises still spend billions on IT, and are always looking for ways to cut costs,” Cheng says. “IT is very steady.”

Microsoft Plans Major Ad Push Around New Search Engine; Is The Name Bing? (Microsoft planeja grande ofensiva de propaganda online em torno de novo engenho de busca; o nome é Bing?)

maio 25, 2009

Post do que saiu ontem no sobre nova ofensiva da Microsoft na área de busca.


Microsoft Plans Major Ad Push Around New Search Engine; Is The Name Bing?

Joseph Tartakoff
Sunday, May 24, 2009; 8:07 PM

Details are starting to trickle out about how Microsoft (NSDQ: MSFT) plans to position a revamp of its search engine against competitors. AdAge which previously reported that Microsoft was planning a major ad campaign to promote a relaunch of Live Search this spring now says that Microsoft will spend between $80 and $100 million on advertising, almost double the amount typically spent on the launch of a consumer product. The campaign which will span TV, print, and online is so large that ad firm JWT is actually hiring while most of its competitors are shedding employees. AdAge says the campaign will “focus on planting the idea that today’s search engines don’t work as well as consumers previously thought by asking them whether search (aka Google) really solves their problems.” However, it will not refer to Google (NSDQ: GOOG) or Yahoo (NSDQ: YHOO) by name.

Microsoft is reportedly set to announce details surrounding an overhaul of its search engine later this week at the AllThingsD Conference in Carlsbad, Calif. The company is also expected to announce a new name for the search engine. AdAge refers repeatedly to the new search engine as being called Bing, one of several names that has been rumored to be under consideration, although it does not explicitly say that will be the new brand. One possible problem if Microsoft does plan to use Bing: reported Friday that the U.S. Patent and Trademark Office rejected Microsoft’s request to trademark Bing, citing “likelihood of confusion,” due to the existing use of the brand by a mobile company. A Microsoft spokeswoman did not immediately return an e-mail seeking comment.

Why Business Intelligence Matters Now More Than Ever (Porque Inteligência de Negócios-BI importa mais do que nunca)

maio 23, 2009

Mais notícias interessantes do blog!


Why Business Intelligence Matters Now More Than Ever

by Loraine Lawson, IT Business Edge
May 21, 2009 11:22:34 AM

If you’re tempted to postpone integration projects related to business intelligence or BI projects in general – don’t, warns Sarah Burnett, a senior research analyst with European IT research firm The Butler Group. Burnett explained to IT Business Edge’s Loraine Lawson how both BI and CPM software can help companies navigate the current economic climate, and the integration challenges you’ll have to overcome first.


Lawson: In a recent report on Business Intelligence (BI) and Corporate Performance Management (CPM), the Butler Group concluded that holding off on business intelligence and CPM projects isn’t a viable option. Why not?
Burnett: BI allows organizations to get a more accurate and detailed picture of what is going on in terms of business and customers. It can do this in different ways – accurate view of costs, liabilities, risks, customer buying patterns, supplier cost-effectiveness, etc.


BI can bring visibility into the organization at granular levels and help link different aspects together. Take budgets, for example. It is all too easy for organizations to think that they have more money than they do and over-commit on budgets, only to find themselves later with liabilities that they cannot afford.


Whilst rolled up financial reports from spreadsheets can show expenditure and commitments against cost center codes, it is important to be able to drill into the figures, see where they have come from and to have some context around the expenditure. The expenditure, for example, can be viewed against progress and what should be expected next, and so enable organizations to forecast and be ready for invoices that are to come.


This is particularly important in a recession when organizations need to minimize outgoings and risks. It also enables them to plan savings in an informed and effective way.


“Now that consumer confidence is returning, it is also important for organizations to be ready to do sales and promotion campaigns at the right time. BI can help with that …”


Sarah Burnett
The Butler Group

Lawson: What’s the connection between CPM and BI applications?
Burnett: Now that consumer confidence is returning, it is also important for organizations to be ready to do sales and promotion campaigns at the right time. BI can help with that too – allow organizations to review customer behavior so that they can time special offers to get customers back before their competitors do. In this way, you can use Business Intelligence to optimize business and improve corporate performance – develop a full cycle of CPM right from the planning and budgeting stage through to implementing, monitoring and – very importantly – improving.


Lawson: With all the tools available now, why are poor data quality and lack of integration still major barriers to effective CPM and BI?
Burnett: BI relies on data collected from other systems, so the quality of the data is very important to BI. The intelligence provided by BI has to be trusted enough to be acted upon.


There are many data quality software tools that can be used to improve the quality of data that comes into the BI or CPM system. But there is more to data quality than just tools.


Data quality should be built into processes so that data is correctly captured and stored, that errors are not introduced in other processes that use the data, and that the data is integrated, i.e., brought together from different systems so that the information that it provides can be compared and contrasted to provide intelligence: Where is Widget X selling the best? Is it actually selling at a profit?


Lawson: What steps do companies need to take to address the integration problems undermining CPM and BI?
Burnett: Which aspect of integration did you want to discuss: Between BI and CPM software, or embedding BI in other apps, or data integration for BI and CPM?


Lawson: Could you explain all of them?
Burnett: As far as BI and CPM integration, many vendors offer their own integrated BI/CPM packages or integrate with software from third parties. Many of the leading software apps have support for standards. Some support other third-party tools or connectors to different pieces of BI/CPM software.


Following the spate of acquisitions in 2007, vendors have integrated some of the acquired products, for example, recently acquired OLAP engines, but there is still work going on in this area. It is advisable for organizations to ensure that their preferred software apps work together. In Butler Group’s Features Matrix, part of our recently published BI (CPM) report, we list the interoperability capabilities of leading CPM software from: Actuate, InformationBuilders, IBM Cognos, Microsoft, Oracle, SAP and SAS.


Vendors are addressing embedded BI in different ways, e.g., through SOA, operational BI or real-time BI. The latter is a different kind of BI that collects data from message queues or real-time events and builds process or event profiles to use for reference and comparisons. Deviations from the norm can then be spotted in real time and alerts generated or even other processes activated automatically. There are also BI software tools with open APIs and support for Flash, etc. that allow BI output to be used in composite applications (mashups).


The last, data integration, is very important and also benefits from years of development in this market. There are many ETL tools and other data integration apps that can interface to a myriad of BI applications and vice versa. The sort of problems experienced in this area include different data definitions, or reference data used in different parts of the organization and in different data sources, making it difficult to consolidate data, and compare and contrast info in different business contexts. Other problems include increasing volumes of data that can lead to data warehousing performance and scalability issues.


Data quality is very important, too. If you put garbage in, you will get garbage out. Data quality is important for business efficiency anyway and it is essential for gaining trusted business intelligence.

AOL’s Ad Platform Retains Top Ranking (A Plataform A, de propaganda online, da AOL retém o topo do ranking)

maio 23, 2009

Retomando o tema da propaganda online, eis aqui uma notícia de uma das redes de propaganda que vem ganhando destaque!  O post veio do blog


AOL’s Ad Platform Retains Top Ranking
Posted by Susan Hall May 22, 2009 10:09:21 AM
America Online’s Platform-A came out on top in April in comScore’s ranking of U.S. ad networks.Yahoo’s Ad Network placed second and Google’s Ad Network third, reports The story says AOL’s platform has held the top spot for quite some time.

The networks were ranked on their reach among U.S. Internet users during the month. The ad networks connect advertisers with sites based on a specific interest, such as sports, helping those advertisers reach a specific audience rather than that of a big general site. 

According to a separate story, however, growth in online display advertising has slowed dramatically. And rather than being sold in cost-per-thousand impressions, advertisers want to tie prices to the number of times viewers take action, such as clicking on an ad, signing up for a promotion or buying something.


Decoupling 2.0 (Descolamento 2.0)

maio 22, 2009

A tese do descolamento das economias emergentes daquelas do muito “já emergido” volta à tona. Desta vez é a nova revista The Economist que levanta a peteca! 

Decoupling 2.0

Emerging Economies

May 21st 2009
From The Economist print edition

The biggest emerging economies will recover faster than America



REMEMBER the debate about decoupling? A year ago, many commentators—including this newspaper—argued that emerging economies had become more resilient to an American recession, thanks to their strong domestic markets and prudent macroeconomic policies. Naysayers claimed America’s weakness would fell the emerging world. Over the past six months the global slump seemed to prove the sceptics right. Emerging economies reeled and decoupling was ridiculed.

Yet perhaps the idea was dismissed too soon. Even if America’s output remains weak, there are signs that some of the larger emerging economies could see a decent rebound. China is exhibit A of this new decoupling: its economy began to accelerate again in the first four months of this year. Fixed investment is growing at its fastest pace since 2006 and consumption is holding up well. Despite debate over the accuracy of China’s GDP figures (see article), most economists agree that output will grow faster than seemed plausible only a few months ago. Growth this year could be close to 8%. Such optimism has fuelled commodity prices which have, in turn, brightened the outlook for Brazil and other commodity exporters.

That said, even the best performing countries will grow more slowly than they did between 2004 and 2007. Nor will the resilience be universal: eastern Europe’s indebted economies will suffer as global banks cut back, and emerging economies intertwined with America, such as Mexico, will continue to be hit hard. So will smaller, more trade-dependent countries. Decoupling 2.0 is a narrower phenomenon, confined to a few of the biggest, and least indebted, emerging economies.

It is based on two under-appreciated facts: the biggest emerging economies are less dependent on American spending than commonly believed; and they have proven more able and willing to respond to economic weakness than many feared. Economies such as China or Brazil were walloped late last year not only, or even mainly, because American demand plunged. (Over half of China’s exports go to other emerging economies, and China recently overtook the United States as Brazil’s biggest export market.) They were hit hard by the near-collapse of global credit markets and the dramatic destocking by shell-shocked firms. In addition, many emerging countries had been aggressively tightening monetary policy to fight inflation just before these shocks hit. The result was that domestic demand slumped even as exports fell.


Not such a bad idea after all

But the global shocks are now abating. Firms cannot slash stocks for ever. And as investors’ panic recedes, so credit markets are beginning to function. This will not be enough to spur a vibrant recovery in America, where households must painfully rebuild their balance-sheets. But it removes a drag on big emerging economies—all the more so because their governments have dramatically loosened the fiscal and monetary reins. China’s stimulus is the most spectacular, but Brazil has also been able to cut interest rates and boost spending.

Government activism helps explain why the creditworthy big emerging economies can recover more quickly. But it cannot create long-term resilience. China’s rebound will only be sustained if the economy shifts further from state-sponsored investment to private consumption. That will require tough structural changes, from forcing state-owned firms to pay fatter dividends to a stronger social safety net. Other countries, notably India, must calibrate their government finances even more carefully (see article). The idea of decoupling lives on, but that does not mean sustained prosperity in the big emerging economies is a foregone conclusion.

What Does Global Expansion of Higher Education Mean for the US? (O Quê a Expansão Global da Educação Superior significa para os EUA?)

maio 20, 2009

Outro interessante artigo que saiu neste mês no National Bureau of Economic Research-NBER, dos EUA.  Desta feita o autor é o Prof. Richard B. Freeman.  O paper pode ser baixado aqui!.


What Does Global Expansion of Higher Education Mean for the US?


This study documents the rapid spread of higher education around the world and the consequent reduced share of the US in the world’s university students and graduates. It shows that the proportion of young persons who go to college has risen in many advanced countries to exceed that in the US while human capital leapfrogging in the huge populous developing countries has produced massive increases in their university educated work forces. One result of the expansion of higher education overseas is that the US has come to rely extensively on the immigration of highly educated persons to maintain a lead position in science and technology. International students make up roughly half of university graduate immigrants to the US, which makes policies toward those students a key determinant in the country’s success in attracting immigrant talent.

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