Wednesday, May 16, 2012

Social media is reinventing how business is done

Tim Mullaney, USA TODAY, May 14, 2012


When Red Robin Gourmet Burgers introduced its new Tavern Double burger line last month, the company had to get everything right. So it turned to social media.

The 460-restaurant chain used an internal social network that resembles Facebook to teach its managers everything from the recipes to the best, fastest way to make them. Instead of mailing out spiral-bound books, getting feedback during executives' sporadic store visits and taking six months to act on advice from the trenches, the network's freewheeling discussion and video produced results in days. Red Robin is already kitchen-testing recipe tweaks based on customer feedback — and the four new sandwiches just hit the table April 30.

Facebook's initial public offering Friday — the largest by a technology company — is a watershed moment for the consumer side of the Web, but social networking's real economic impact might be ahead as companies learn how to harness "social business" tools. 

Beyond advertising on Facebook or Twitter, companies are using social networks to build teams that solve problems faster, share information better among their employees and partners, bring customer ideas for new product designs to market earlier, and redesign all kinds of corporate software in Facebook's easy-to-learn style.

"At a very basic level, Facebook is the most popular application ever, with a billion people who know how to use it," said Marc Benioff, chief executive of salesforce.com, whose Chatter social-networking tools are used by 150,000 companies. "The ability to access information is much better because it's easier to get to it."
After a slow start, Big Business is embracing social media in a big way. Forrester Research says the sales of software to run corporate social networks will grow 61% a year and be a $6.4 billion business by 2016.

Two-thirds of big companies surveyed now use Web 2.0 tools such as social networks or blogs, with use of internal social networks up 50% since 2008, according to a survey by McKinsey & Co. Nearly 90% said they have reaped at least one measurable business benefit, though most say the improvements have been modest.

Heavy use of social tools has a statistically significant correlation to profitability, said Michael Chui, senior fellow at the McKinsey Global Institute. But it's early: Only about 3% of respondents used social business tools for all three major uses — reaching customers, connecting employees and coordinating with suppliers, McKinsey said.

The Social Web seems to be doing a different job in Corporate America than the first-generation Web. In the late 1990s, companies such as Wal-Mart used the Internet to streamline supply chains and better manage inventories to hold down prices. Banks used the new technology to cut the cost of processing mortgages by as much as two-thirds, by eliminating clerical workers and substituting e-mail for expensive overnight deliveries. If Web 1.0 automated routine processes and warehouses, Web 2.0 is about organizing design work and creativity, said Andrew McAfee, professor of technology and operations management at Harvard Business School.

"We asked ourselves where would social networking go once everyone had a Facebook account?" said David Sacks, president of San Francisco-based Yammer, whose software runs Red Robin's internal social network. "Big ideas always move from the consumer market into the enterprise market."

"Innovation is a two-way street," said Chris Laping, Red Robin's senior vice president for business transformation. "When people see things, they feel things. And when they feel things, they change."

Making connections
Using social networks to foster connections lets companies match the skills of people working all over the world who wouldn't easily find each other, said Eric Lesser, a research director at IBM's Institute for Business Value. It's especially valuable for companies built by acquisition, whose managers in different divisions often don't know each other, he said.

Take SuperValu, a collection of supermarket chains ranging from Shaw's in Boston to Albertsons in California. SuperValu last year used Yammer to build a network to connect 11,000 executives and store managers, chief information officer Wayne Shurts said. They've organized themselves into more than 1,000 groups to talk about specific challenges. 

For example, 182 managers from different chains joined a group to mull common problems of running markets in college towns. Another 153 banded together to talk about running stores in beach communities, where business is seasonal. Those didn't replace any other process, because there was no way to do it before: The managers couldn't all be pulled from their stores for retreats or meetings, and the cost of getting them together would have been prohibitive, Shurts said.

One result: A promotion at college-oriented stores that sold 8,000 $99 mini-refrigerators last fall, each stuffed with $99 worth of coupons to bring the customers back for food. Another discussion led to college-town "beer pong" displays packaging ping-pong balls, red Solo cups and brewskis to fill them up. Both ideas were floated last spring and ready by August, he said.
"You've got to let the conversations happen, even if you might not like all of that conversation," Shurts said. "It's going to happen around the water cooler anyway."

Listening to customers
Companies can also use blogs and social sites to bring customers into their product-design process, said Barton George, director of the Dell computer division that sells to Internet-based companies. Through its IdeaStorm site, Dell has taken in more than 17,000 ideas for new or improved products, and has adopted nearly 500, including backlit keyboards that are better for working on airplanes. 

Other times, Dell puts its own ideas on IdeaStorm, in what it calls a Storm Session, to get feedback before going ahead. On May 6, Dell posted a plan on IdeaStorm describing a proposed specialty laptop, upgrading an existing machine to target people who write wireless apps and other Web-based software using a variation of the Linux operating system called Ubuntu, George said.

By Monday, customers had posted 83 ideas for refinements to the machine on IdeaStorm, covering specific software bugs to broader issues such as whether the screen should be shiny or not. In addition, 35,000 people visited George's Web posting about the new laptop — 10 times more than any other posting he's ever made, he said. The laptop is due on the market by year's end. Dell says the process produces more detailed feedback than traditional focus groups, and builds links to an important group of customers.

So far, the social Web hasn't boosted U.S. productivity growth the way the first-generation Internet did in the late 1990s. But economists such as MIT's Erik Brynjolfsson say it takes about five years for a new technology to show its full impact on companies that deploy it. Social networking is about two or three years in at most companies, McAfee said. 

Companies are tinkering with the technology and their own business processes, trying to find ways to match them up to get the most impact and learn how to interpret all the unorganized data users disclose about themselves on the sites, Lesser said.

In the meantime, the trend has already generated one IPO for a smaller company, Jive Software, that sells social-networking tools to companies. Jive went public at $12 a share in December and now trades around $19.50, achieving a $1.2 billion market value, though it's not yet profitable.

Facebook hasn't actively pursued the social business market. It let companies such as Yammer and Jive mimic its look and feel, because making Facebook-like features an industry standard helped cement Facebook's leadership in consumer social networks, Yammer's Sacks said.

Social media has the potential to be as important to the broader economy as more obviously business-related information technologies such as mobile phones and cloud computing, said Stacey Bishop, a venture capitalist at Scale Venture Partners, in Foster City, Calif.

"I'd put the cloud first, but they're all important and they're all related," Bishop said. "Mobile is an extension of the cloud, because it lets you get your data wherever you are. And social is the layer on top of that, making it easier to cross-communicate."

New WEF report: Rethinking Personal Data: Strengthening Trust

Rethinking Personal Data: Strengthening Trust examines how the appropriate use of personal data can create enormous value for governments, organizations and individuals. 

Produced in collaboration with The Boston Consulting Group, the report provides a multistakeholder perspective on how the potential value of personal data can be unlocked.
The report aims to foster dialogue around some of the key questions that need to be resolved to ensure long-term and sustainable value creation. Who owns personal data? How can privacy be protected? What is the role of context in setting permissions? How can organizations be held accountable? What is the role of regulators?  It outlines concrete steps that stakeholders can take, focusing on three areas: upgrading protection and security, agreeing on rights and responsibilities for using data based on context, and driving accountability and enforcement. The report concludes with a call for leaders to work together to achieve a coordinated yet decentralized approach to this global challenge.

Big data is worth nothing without big science

As with gold or oil, data has no intrinsic value, writes Webtrends CEO Alex Yoder. Big science, which bridges the gap between knowledge and insight, is where the real value is.

Alex Yoder,  CNET News, May 15, 2012 

We are living in "the age of big data," according to The World Economic Forum. Renowned futurist Ray Kurzweil agrees. I do too. 

As the likes of Google, Facebook, Adobe Systems, and IBM embrace big data with gusto, startups are also popping up with the promise to help companies discover what one of the most valuable assets in the world can accomplish for them. No industry is untouched by big data, which is notably transforming the way social networks work today. However, the key factor that will determine success for companies in this age is not simply big data, but big science. 

The World Economic Forum's report on data equated it with an asset such as gold. Others have declared that data is "the new oil." But, as with gold or oil, data has no intrinsic value. 

Gold requires mining and processing before it finds its way into our jewelry, electronics, and even the Fort Knox vault. Oil requires extraction and refinement before it becomes the gasoline that fuels our vehicles. Likewise, data requires collection, mining and, finally, analysis before we can realize its true value for businesses, governments, and individuals alike. 

Gold requires mining and processing before it finds its way into our jewelry, electronics, and even the Fort Knox vault. Oil requires extraction and refinement before it becomes the gasoline that fuels our vehicles. Likewise, data requires collection, mining and, finally, analysis before we can realize its true value for businesses, governments, and individuals alike. 

According to IDC, the amount of data that companies are wrestling with is growing at 50 percent per year -- or more than doubling every two years. Many organizations are rich in data but poor in insight. That's where big science comes in. 

The collection and mining of massive amounts of digital data currently defines the term big data. Those are processes that businesses largely handle. However, the analysis of that data -- that magic ingredient of algorithms and advanced mathematics that bridges the gap between knowledge and insight -- is big science. It is where the value is. It is the future. 

Put simply, the analysis that big science brings to the table makes big data relevant. I envision big science combining with big data to create big opportunities in three significant ways: real-time relevant content, data visualization, and predictive analytics. Although I think that these trends will be especially important for my industry, digital marketing and analytics, I have no doubt that they will impact all industries, as chief marketing officers are inevitably drawn closer to chief information officers in an effort to tame and harness big data. 

Getting right message to the right customer at the right time is the promise of relevant, real-time marketing. Big science, not big data, will bring this to life. 

Marketers, using analytics to collect massive amounts of digital information, have been working with big data for years now. In fact, they are flooded with geographic, demographic, and ethnographic data about their customers. The big science of processing and analyzing this data, through human expertise and machine intelligence, will empower marketers to identify and segment their customers, tailor and target the most relevant content to them, and deliver these experiences in real time across a range of digital channels and devices. 

As was stated, human intelligence is a part of the big science that will help to deliver relevant content in real time. The human intelligence of big science will be fueled by data visualization. 

Visualizations of Web traffic have been around for years. These are relatively simplistic, however, and they typically visualize data that is historical. Big science will take the raw potential of big data and make it digestible for the human mind in real time. Imagine a retailer being able to visualize and track both the shipments of new goods and the intake of returned or unused items in real time through a bright, simple, and dynamic user interface. The opportunities for optimizing business processes, and revenue in just this one scenario are endless. 

But what if you could use your big data to see not just what's happening now, but also to model what you could be doing to optimize outcomes for the future? Enter big science fueling predictive analytics. 

The big science of predictive analytics will take advantage of the historical patterns ingrained in big data to unlock insights to inform current and future strategies. Should you change the image in an advertisement from a black-and-white graphic to a color photo? If you did, what sorts of results could you expect? Big science can help show you the way. 

It takes complex algorithms, powerful computing and, perhaps most of all, human analysts to build and administer the big science that turns the "then and now" nature of big data into "when." Last year, the McKinsey Global Institute projected that the United States alone needs 140,000 to 190,000 more workers with "deep analytical expertise." 

Those who become experts in the science behind the big-data phenomenon will become the next wave of digital and corporate geniuses. One potential genius, Gilad Elbaz, the influential investor and inventor behind big-data startup Factual, recently told The New York Times, "I have been thinking that we need to get more personal data. I want to get people to figure out a way to get people to leave their data to science."

Tuesday, May 8, 2012

Federal Facebook could outdo conferences, former CIO says

Aliya Sternstein,  Next Gov,  May 7, 2012

The federal government needs social networks -- not more conferences -- to connect colleagues in far-flung places, former federal Chief Information Officer Vivek Kundra said Monday at a conference hosted by Nextgov’s parent organization Government Executive Media Group.

“Why is it that you don’t have an employee social network?” pondered the recent hire at cloud services firm Salesforce.com. “This notion of creating an employee social network is so important.”

Kundra suggested that agencies -- many of which are “multinational” with foreign offices -- establish online communities where U.S.-based staff, overseas co-workers and their customers can informally connect anytime, anywhere.

“I would argue that it’s better than a point-in-time conference to build up these social networks because they persist across time,” he said. “It’s going to destroy this notion of distance.”

During the past month, government conferences organized by the General Services Administration and National Oceanic and Atmospheric Administration have garnered charges of costly extravagance.

Kundra’s new job as Salesforce’s executive vice president of emerging markets involves applying social tools to make governments more efficient, transparent and collaborative. But big data generated by the increasing use of social technologies and the Internet can overload people’s circuit breakers if it’s not analyzed properly, he noted.

Big data -- the huge of amount information now available for fact-finding and analysis -- has changed the meaning of the term sensors, Kundra said. People usually think of sensors as motion and speed detectors on streets that discern traffic patterns. “But what we haven’t really talked a lot about yet is people in their everyday lives are instrumenting the entire planet” by taking photos on their mobile phones or Tweeting about the weather, he said.

All the transactions are happening in milliseconds so parsing them in real time could yield immediate insights, unlike monthly or even daily reports. “The problem of separating noise from signal is critical” when sensors are everywhere, Kundra added.

Thursday, May 3, 2012

Big Data, Big Biology, and the 'Tipping Point' in Quantified Health:Takeaways from Xconomy’s On-the-Record Dinner

FYI - “P4 Medicine”—health care that is predictive, preventive, personalized, and participatory


Two of the biggest trends in technology innovation are converging—and as they come together, there is a chance to accomplish something rare in San Diego. Something exponential.

One of these forces is “big data,” the ever-increasing capabilities of computers and analytic software to move from gigabytes to terabytes, petabytes, and beyond. The other is “big biology,” which encompasses a breathtaking array of fundamental breakthroughs in DNA sequencing, molecular diagnostics, genome biology, proteomics, and other “omics” technologies.

Each is an irresistible force in its own right, and they are coming together like tributaries at the confluence of “quantified health,” a new field with the potential to fundamentally change health care. What makes this combination so powerful is the sheer magnitude of ways that “health” can now be measured—and in the use of data analytics to mine information that was previously unattainable.


The new tools of molecular biology are making it much more practical to analyze a patient’s genome—to determine if a patient has a genetic predisposition, say, for diabetes or heart disease. Advances in molecular diagnostics are also making it easier to regularly measure the hundreds of thousands of molecule-size constituents that are produced through genetic activity. With data analytics, it’s feasible to compare and chart these millions of data points over time to detect early signs of disease long before any symptoms appear—or to compare the data from one patient with the data from millions of other patients to see how they compare on the bell curve of “normal.”

This combination of big biology with big data has already begun—and leads to what Lee Hood of the Seattle-based Institute for Systems Biology calls “P4 Medicine”—health care that is predictive, preventive, personalized, and participatory. It is one reason why Larry Smarr, founding director of the California Institute for Telecommunications and Information Technology (Calit2), has become a de facto evangelist for quantified health.

“Never in human history has something that is critical to human health gone down in cost by a factor of 1 million in a decade the way genomics has,” says Smarr, who is also a San Diego Xconomist. Combine this megatrend with advances in sensors and related technologies that are embedded in current-generation smart phones—-and the plummeting costs of data storage and cloud computing—and you have all the needed ingredients for a technology revolution in health care.

 
With Smarr’s help, Xconomy brought together some of the best minds in business, life sciences, and information technology for an “on-the-record” dinner discussion about the implications of quantified health, and what it might take to harness the power of this new river of information.

“The decade we are now moving into is going to be radically different than [anything] in the history of health and medicine because of these exponential changes we’re going through,” Smarr says.

As we’ve reported previously, Smarr has used some of these new diagnostic tools to calibrate his own health, along with a variety of innovative devices that measure his physical activity, caloric burn, and sleep efficiency. Along the way, he’s become something of a poster child for quantified health (also known as “quantified self”), and he sometimes talks as if he’s getting more speaking requests than he can count.

That’s important, though, is that Smarr contends that San Diego already has all the pieces needed to become “one of the real leaders in quantified health.” He sees plenty of expertise here in genomics, molecular diagnostics, high-performance computing, wireless technologies, health IT, and data analytics. Yet these are only the ingredients. They still need to fit together, and San Diego still needs to muster the business and technology leadership to put the pieces together.


So what would it take to accomplish that here? This was one of the primary themes of our discussion.

“We are 3 million people,” says Diego Miralles, who heads Janssen Healthcare Innovation, an entrepreneurial initiative in San Diego that is part of Johnson & Johnson’s pharmaceutical business. “We have four or five medical systems. It would be great if we started working with those medical systems to make San Diego the city of the medical future, if we could really come together.”

The experts quickly identified a number of hurdles that must be overcome to realize this vision, including:

—Scalability. Smarr sees a challenge in developing a bioinformatics network that could expand from a pilot project to serve the health needs of a population as big as San Diego.

—Trustability. As a founding member of Google Health, which operated from 2008 to 2011, Missy Krasner said one issue that became a problem was who owns the data—and who would be the trusted custodian of the data? Krasner, who is now an entrepreneur-in-residence at Morgenthaler Ventures in Mountain View, CA, also asked why anyone would want to participate. “What are the incentives that get the average consumer and the average provider at Sharp or Scripps or anywhere else to actually want to receive that data and do something with it?”

—Profitability. “How do you make money?” asked Lisa Suennen, a co-founder and managing member of the Psilos Group, a healthcare-focused venture capital firm in Corte Madera, CA. “If there’s not a clear path to that, it’s a barrier.”

—Engagability. Generating meaningful feedback for patients will be a challenge, said Ernesto Ramirez, who is working at UCSD’s Center for Wireless and Population Health Systems on a doctorate in health behavior. “To me the issue is not the statistical methods that tell me whether I’m going to have a heart attack. It’s the layering on of all the behavioral methods, the visualization, and all the other ways that you can help me understand the data.”

Despite the thicket of difficulties, though, there are still some things that could be done—which was another recurring theme of the discussion.

“You just start,” said Mark Stevenson, the president and chief operating officer of Life Technologies (NASDAQ: LIFE), the global biotechnology company based in Carlsbad, CA. If Larry Smarr’s 10-year experiment in quantified health represents what Stevenson calls an “n of 1”—a single case study—then it’s simply a matter of trying to extrapolate the technology from there to get to an “n of 300,” or an “n of 3 million.”

Rather than trying to address everything, Stevenson said there are practical ways to collaborate by taking on tasks that could be more easily accomplished. For example, Stevenson said Life Technologies has provided its Ion Torrent genetic diagnostic tools to help develop individualized treatment regimens for a small group of 18 women in Phoenix, AZ, who were diagnosed with so-called “triple negative” breast cancer.

“For me, the tipping point starts in critical disease areas,” Stevenson said. “So, oncology patients, newborn children with neurological disorders, and infectious disease.”

Laura Shawver, an ovarian cancer survivor who founded the Clearity Foundation so molecular diagnostics could be used to help develop more personalized therapies for other ovarian cancer patients, embraced the idea.

“San Diego is very entrepreneurial,” said Shawver, who also is the CEO of San Francisco-based Cleave Biosciences. “We all live it. We all do it. The thing that really bothers me is when I hear somebody say personalized medicine is the way of the future. No! It’s here and now.” For cancer patients in particular, she added, “When your life is on the line, you’ll do whatever it takes.”

Several participants agreed that getting most patients to “buy in” to a quantified health program would likely be problematic—and so is patient compliance.

“I have patients and I tell them to use a Fitbit or a BodyMedia armband or whatever, and they use it for three months and after that it just kind of fizzles,” said Eric Topol, the prominent San Diego cardiologist and director of the Scripps Translational Science Institute.

“It is going to require behavioral change, but we can’t fundamentally change behavior anytime soon,” said Rick Valencia, who oversees a host of wireless health initiatives as vice president and general manager of Qualcomm Life, a Qualcomm (NASDAQ: QCOM) subsidiary. “We have to offer experiences that people embrace, and I think the way you do that is by starting really, really simple.”

Topol also raised another key issue—cost—saying technology innovations almost invariably increase the cost of health care.

“I had a really interesting dinner last week with Bill Gates,” Topol said, “and I was shocked because he was questioning all this stuff. He was saying, ‘Show me where it’s going to cut costs.”

“The only thing that’s ever proven to save costs is something that’s been preventive—so things like vaccines and pap smears,” said Drew Senyei of San Diego’s Enterprise Partners Venture Capital. “Prevention is the only way we’re going to bring costs down.”

“The cost of being able to match a [person’s disease] to a drug is much less than the actual cost of the drug itself,” said Shawver. “And the most expensive drug is the one that doesn’t work. Often times people go from drug to drug to drug in a trial-and-error approach that adds a lot to costs. But it the past, the ability to gain [personalized genetic] information has not been cost-effective.”

The cost issue is important, but a pilot program that demonstrates the potential of quantified health could open the way for more ambitious efforts, said Peter Ellsworth, president of the Legler Benbough Foundation, a $35 million fund that awards grants to improve the quality of life of in San Diego. Ellsworth, who retired in 1996 after a 10-year reign as the CEO of Sharp Healthcare, says a successful demonstration also could help lower the competitive tensions between San Diego’s three rival health systems: Sharp, Scripps Health, and the UC San Diego Health System.

“If somebody is doing something and people like it, and there’s some publicity, pretty soon, the others will come along,” Ellsworth said. “I think the technology really does offer us something that we haven’t had before, because we’re going to be able to demonstrate things we were never able to do before.”

Rivalries between doctors and healthcare systems also could become less relevant as people generate more of their own health data from devices like Fitbit and BodyMedia, said Smarr. He estimates that 25 percent of all medical test data now resides outside the confines of health care systems, and that it is only going to increase. So now is the time to start thinking about the scale of quantified health and how it’s going to work.

“We’ve got to find a way to make your own data transportable, and we’ve got to have the legal reforms so that if your body generates the data, then you own it,” Smarr said. “You might want to license it to somebody else, but you own it. That’s not where we are today.

“I’ve seen so many of these digital transformations over and over again,” Smarr continued. “You go from a data-poor world to a data-rich world, and in the data-rich world your solutions are completely different than in the data-poor world and there’s no way to predict what it’s going to be like in the data-rich world or who’s going to be on first. That’s the way it works.

“Nobody would have thought that Steve Jobs would come up with a way to work file-sharing with the record industry. But he did. And now Apple is worth more than any corporation in the world. But you can’t predict that.

“The point is to start thinking,” Smarr said. “Go to the end of the rainbow. Assume that everybody has data like I do. I’ve got a disk drive from the Craig Venter Institute in Maryland with 35 billion bytes of information, which is the sequencing of all the bacteria in my gut. I sent a little vial of stool to them on dry ice and I got back 35 billion numbers. So start thinking. Everybody has got 35 billion numbers like this, which by the way is more than the human genome.

“I spend all my time thinking, ‘How do I work in a world of rich numbers?’—not ‘How do I keep it from happening?’ So get your mind around the fact that that’s what the whole population is going to be like that, and start looking for business solutions, and legal and ethical solutions in that data-rich world. Because by the time you can actually do anything, that will be the reality for everybody. For some of us, it already is.”

That’s Smarr’s vision of the world of quantified health. From his work as an astronomer to director of a supercomputer center to the Internet and CalIT2, Smarr says his career has always been built around exponentials.

In attendance at the Xconomy dinner—which was sponsored by Alexandria Real Estate Equities, the Latham & Watkins law firm, Ernst & Young, and the
TriNet human resources firm—were John Blume of Applied Proteomics, Jon Cohen of Science magazine, David Nelson of Epic Sciences, Larry Smarr of CalIT2, Eric Topol of Scripps Health, Lisa Suennen of the Psilos Group, Drew Senyei of Enterprise Partners, Missy Krasner of Morgenthaler Ventures, Rick Valencia of Qualcomm Life, Peter Ellsworth of the Legler Benbough Foundation, Tom Watlington of Sotera Wireless, Mark Stevenson of Life Technologies, Diego Miralles of Janssen Healthcare Innovation, Laura Shawver of the Clearity Foundation and Cleave Biosciences, Ernsto Ramirez of UCSD’s Center for Wireless and Population Health Systems, Jason Moorhead of Alexandria Real Estate Equities, partners Steven Chinowsky and Barry Clarkson of Latham & Watkins, Doug Regnier and John Clift of Ernst & Young, Shannon Conway and Anthony Pedrotti of TriNet. Xconomy Associate Publisher Jim Edwards and Xconomy San Diego Editor Bruce V. Bigelow also were there.

Wednesday, May 2, 2012

'Big Data' Could Remake Science -- And Government

Joseph Marks, National Journal, May 2, 2012

"Big data" has the power to change scientific research from a hypothesis-driven field to one that’s data-driven, Farnam Jahanian, chief of the National Science Foundation’s Computer and Information Science and Engineering Directorate, said on Wednesday.

Reaching that point, however, will require upfront investment from government and the private sector to build infrastructure for data analysis and new collaboration tools, Jahanian said. He was speaking at a  "big data" briefing for congressional staff hosted by the industry group TechAmerica.

The term "big data" refers generally to the mass of new information created by the Internet and by scientific tools such as the Hubble Telescope and the Large Hadron Collider. The emerging field of big-data analysis is aimed at sorting through the massive volume of that data -- whether it’s social media posts, video clips, satellite feeds, or the reaction of accelerated particles -- to gather intelligence and spot new patterns.
Federal officials announced in March that the government will invest $200 million in research grants and infrastructure building for big data.

The investment was spawned by a June 2011 report from the President's Council of Advisors on Science and Technology, which found a gap in the private sector's investment in basic research and development for big data.

The research firm Gartner predicted in December 2011 that 85 percent of Fortune 500 firms will be unprepared to leverage big data for a competitive advantage by 2015.
Big-data analytics also has the potential to improve government efficiency, panelists at the TechAmerica event said.

The Centers for Medicare and Medicaid Services, for example, could pull data from insurance reports and hospital forms and anonymized data from electronic medical records to get a much better understanding of which medications and procedures are most effective, said Caron Kogan, a strategic planning director at Lockheed Martin Corp.

In addition, the Defense Department could gather better data on the expected life cycle of its equipment so that it could replace equipment before it fails, drastically cutting down its supply-chain costs.

“[Some of] these are old concepts,” Kogan said, “but now you have more data so predictability is increased. You’re not working with a sample size, you’re working with all this data to assess which parts might fail.”

Big data also has the potential to point out new patterns or opportunities for efficiency that officials may never have imagined, said Bill Perlowitz, chief technology officer of Wyle science, technology, and engineering group.

“In hypothetical science, you propose a hypothesis, you go out and gather data, and you see if your hypothesis is supported,” Perlowitz said. “That limits your exploration to what you can imagine. It also limits the number of relationships you can explore because the human mind can only go so far.

“The shift with data-driven science and big data,” he said, “is that first we collect the data and then we see what it tells us. We don’t have a pretense that we understand what those relationships are, or what information we may find.”

Big Data: California Chosen as Home for Computing Institute



John Markoff, The New York Times, April 30, 2012

BERKELEY, Calif. — The Simons Foundation, which specializes in science and math research, has chosen the University of California, Berkeley, as host for an ambitious new center for computer science, the university plans to announce on Tuesday. 

The foundation’s $60 million grant to establish the center, to be called the Simons Institute for the Theory of Computing at U.C. Berkeley, underscores the growing influence of computer science on the physical and social sciences. An interdisciplinary array of scientists will explore the mathematical foundations of computer science and attack problems in fields as diverse as health care, astrophysics, genetics and economics. 

“We’ve been talking to astronomers, climate scientists, fluid mechanics people, quantum physicists and cognitive scientists,” said Richard M. Karp, a Berkeley computer scientist who will be the institute’s director. 
Part science and part engineering, computer science has long been viewed warily by scientists in other disciplines. But that is changing, not only because the computer has become the standard scientific instrument but also because “computational thinking” offers new ways to analyze the vast amounts of data now accessible to scientists. This new approach — what researchers call the “algorithmic” or “computational” lens — is transforming science in much the way the microscope and telescope did. When computer scientists train their sights on other disciplines, said Christos H. Papadimitriou, a Berkeley computer scientist who will help manage the institute, “truths come out that wouldn’t have come out otherwise.” 

Moreover, the flood of experimental results generated by inexpensive sensors, combined with the Internet’s ubiquitous connectivity, is threatening to drown scientists in vast data sets often called “big data.” 

“I do think there is this idea that big data is happening,” said Peter Norvig, Google’s director of research. “Now there is more acceptance pencil and paper alone won’t solve all of our problems.” 

Tuesday’s announcement is part of a broader trend toward expanding support for research in computational theory. The Rafik B. Hariri Institute for Computing and Computational Science and Engineering was created last fall at Boston University to turn a computational lens on an array of disciplines. (It is named for the former Lebanese prime minister who was assassinated in 2005; he was also a former trustee of the university.) 

“It’s analytics with big data, it’s the ability to compute and analyze in massive parallel architectures,” said Jeannette M. Wing, head of the computer science department at Carnegie Mellon University. “All the science and engineering disciplines realize this is part of the future.” 

Dr. Karp, the Simons Institute’s new director, cited the example of the Berkeley cosmologist Joshua Bloom, who has automated the process of identifying interesting events in the night sky. Each evening the system must choose from millions of options. 

“What should we track tomorrow?” Dr. Karp said. “There is a continually changing decision process. It’s an online computation process par excellence.” 

The institute will not be a scientific computing center, said Alistair Sinclair, a Berkeley computer scientist who will serve as its associate director. “An astronomer won’t come along with data and we all sit down and create algorithms to process his data,” he said. “It goes deeper than that. The astronomer will come and talk about the nature of his problems.”

The institute will have about 70 visiting researchers at any one time, including faculty members, postdoctoral researchers and graduate students. It will begin operating later this year and be fully operational in 2013. 

The Simons Foundation, created by the hedge fund billionaire and philanthropist James H. Simons, has given hundreds of millions of dollars for research in autism, math and physical sciences, and life sciences — and, last year, $150 million from both the organization and its founders to Stony Brook University on Long Island. 

Dr. Simons, who earned his doctorate in mathematics at Berkeley, was chairman of the math department at Stony Brook before creating Renaissance Technologies, a private investment firm. Forbes magazine estimates his current worth at $10.6 billion.

The Perfect Milk Machine: How Big Data Transformed the Dairy Industry

Alexis Madrigal, The Atlantic, May 1, 2012

Dairy scientists are the Gregor Mendels of the genomics age, developing new methods for understanding the link between genes and living things, all while quadrupling the average cow's milk production since your parents were born.

While there are more than 8 million Holstein dairy cows in the United States, there is exactly one bull that has been scientifically calculated to be the very best in the land. He goes by the name of Badger-Bluff Fanny Freddie.

Already, Badger-Bluff Fanny Freddie has 346 daughters who are on the books and thousands more that will be added to his progeny count when they start producing milk. This is quite a career for a young animal: He was only born in 2004.

There is a reason, of course, that the semen that Badger-Bluff Fanny Freddie produces has become such a hot commodity in what one artificial-insemination company calls "today's fast paced cattle semen market." In January of 2009, before he had a single daughter producing milk, the United States Department of Agriculture took a look at his lineage and more than 50,000 markers on his genome and declared him the best bull in the land. And, three years and 346 milk- and data-providing daughters later, it turns out that they were right.

"When Freddie [as he is known] had no daughter records our equations predicted from his DNA that he would be the best bull," USDA research geneticist Paul VanRaden emailed me with a detectable hint of pride. "Now he is the best progeny tested bull (as predicted)."

Data-driven predictions are responsible for a massive transformation of America's dairy cows. While other industries are just catching on to this whole "big data" thing, the animal sciences -- and dairy breeding in particular -- have been using large amounts of data since long before VanRaden was calculating the outsized genetic impact of the most sought-after bulls with a pencil and paper in the 1980s.

Dairy breeding is perfect for quantitative analysis. Pedigree records have been assiduously kept; relatively easy artificial insemination has helped centralized genetic information in a small number of key bulls since the 1960s; there are a relatively small and easily measurable number of traits -- milk production, fat in the milk, protein in the milk, longevity, udder quality -- that breeders want to optimize; each cow works for three or four years, which means that farmers invest thousands of dollars into each animal, so it's worth it to get the best semen money can buy. The economics push breeders to use the genetics.

The bull market (heh) can be reduced to one key statistic, lifetime net merit, though there are many nuances that the single number cannot capture. Net merit denotes the likely additive value of a bull's genetics. The number is actually denominated in dollars because it is an estimate of how much a bull's genetic material will likely improve the revenue from a given cow. A very complicated equation weights all of the factors that go into dairy breeding and -- voila -- you come out with this single number. For example, a bull that could help a cow make an extra 1000 pounds of milk over her lifetime only gets an increase of $1 in net merit while a bull who will help that same cow produce a pound more protein will get $3.41 more in net merit. An increase of a single month of predicted productive life yields $35 more.

When you add it all up, Badger-Fluff Fanny Freddie has a net merit of $792. No other proven sire ranks above $750 and only seven bulls in the country rank above $700. One might assume that this is largely because the bull can help the cows make more milk, but it's not! While breeders used to select for greater milk production, that's no longer considered the most important trait. For example, the number three bull in America is named Ensenada Taboo Planet-Et. His predicted transmitting ability for milk production is +2323, more than 1100 pounds greater than Freddie. His offspring's milk will likely containmore protein and fat as well. But his daughters' productive life would be shorter and their pregnancy rate is lower. And these factors, as well as some traits related to the hypothetical daughters' size and udder quality, trump Planet's impressive production stats.

One reason for the change in breeding emphasis is that our cows already produce tremendous amounts of milk relative to their forbears. In 1942, when my father was born, the average dairy cow produced less than 5,000 pounds of milk in its lifetime. Now, the average cow produces over 21,000 pounds of milk. At the same time, the number of dairy cows has decreased from a high of 25 million around the end of World War II to fewer than nine million today. This is an indisputable environmental win as fewer cows create less methane, a potent greenhouse gas, and require less land.

At the same time, it turns out that cow genomes are more complex than we thought: as milk production amps up, fertility drops. There's an art to balancing all the traits that go into optimizing a herd.

While we may worry about the use of antibiotics to stimulate animal growth or the use of hormones to increase milk production by up to 25 percent, most of the increase in the pounds of milk an animal puts out over the pastoral days of yore come from the genetic changes that we've wrought within these animals. It doesn't matter how the cow is raised -- in an idyllic pasture or a feedlot -- either way, the animal of 2012 is not the animal of 1940 or 1980 or even 2000. A group of USDA and University of Minnesota scientists calculated that 22 percent of the genome of Holstein cattle has been altered by human selection over the last 40 years.

In a sense that's very real, information itself has transformed these animals. The information did not accomplish this feat on its own, of course. All of this technological and scientific change is occurring within the social context of American capitalism. Over the last few decades, the number of dairies has collapsed and the size of herds has increased. These larger operations are factory farms that are built to squeeze inefficiencies out of the system to generate profits. They benefit from economies of scale that allow them to bring in genomic specialists and use more expensive bull semen.

No matter how you apportion the praise or blame, the net effect is the same. Thousands of years of qualitative breeding on family-run farms begat cows producing a few thousand pounds of milk in their lifetimes; a mere 70 years of quantitative breeding optimized to suit corporate imperatives quadrupled what all previous civilization had accomplished. And the crazy thing is, we're at the cusp of a new era in which genomic data starts to compress the cycle of trait improvement, accelerating our path towards the perfect milk-production machine, also known as the Holstein dairy cow.

There are no more famous experiments in genetics than the ones undertaken by the Austrian monk Gregor Mendel on five acres in what is now the Czech Republic from 1856 to 1863. Mendel bred 29,000 pea plants and discovered the most basic rules of genetics without any knowledge of the underlying biochemical mechanics.

Smack dab in the middle of Mendel's experiments, Charles Darwin's Origin of Species was published, but we don't have any record of intellectual mingling between the two men. Even the idea of a gene as an irreducible unit of inheritance wasn't presented until 30 years after Mendel began his experiments. The term and field of genetics would not be fleshed out until William Bateson and company came along in the early 1900s. And its form, DNA, would not be proposed by James Watson and Francis Crick with indispensable help from Rosalind Franklin until 90 years after his last pea plant died. All this to say: Mendel was ahead of his time.

What he had going for him was a dedication to data, to quantification. His fundamental insight was statistical.

Here's the simple version of what he did. Mendel took pea plants that reliably produced purple or white flowers when they self-pollinated. Then he crossbred them, carefully controlling how the plants reproduced. Now, one might expect that if you breed a pea plant with a purple flower and a pea plant with a white flower, you'd get progeny that were sort of mauve, a mix of the two colors. But what Mendel found instead is that you either got purple flowers or white flowers. Even more amazingly, sometimes breeding two purple flowers would yield a white flower. Among the first generation of crossbreeds, the mix of flower colors occurred at a roughly constant ratio of about 3:1, purple to white. If the traits of two plants were being mixed to generate the next generation, how could two purple flowers yield a white flower? And why would this ratio arise?

Mendel took a conceptual leap and hypothesized that the plants had two possible copies of its plans (i.e. genes) to make flower color (or any of six other traits he analyzed). If the plant received two of the dominant plan (purple), the flowers would, of course, be purple. If it received one of each, the dominant plan would still reign. But if the plant received two recessive plans, then the flowers of that pea would be white.

The monk turned out to be right. For traits controlled by a single gene, things really do work as he predicted. Mendel's insights became part of the central dogma of genetics. You can use the statistical method he used to calculate how likely someone is to get sickle cell anemia from her parents. In most genetics classes, Mendel is where it all starts and for good reason.

But it turns out that Mendel's version of things doesn't actually give a very clear picture of the kinds of things we care about most. "Mendel studied a few traits that happened to be controlled by a single gene, making the probabilities easier to figure out," the USDA's VanRaden said. "Animal breeders for many decades have used models that assume most traits are influenced by thousands of genes with very small effects. Some [individual] genes do have detectable effects, but many studies of plant and animal traits conclude that most of the genetic variation is from many little effects."

For dairy cows -- or humans, for that matter -- it's just not as simple as the dominant-recessive single-gene paradigm that Mendel created. In fact, Mendel picked his model organism well. Its simplicity allowed him to focus in on the simplest possible genetic model and figure it out. He could easily manipulate the plant breeding; he could observe key traits of the plant; and these traits happened to be controlled by a single gene, so the math lay within human computational range. Pea plants were perfect for studying the basics of genetics.

With that in mind, allow me to suggest, then, that the dairy farmers of America, and the geneticists who work with them, are the Mendels of the genomic age. That makes the dairy cow the pea plant of this exciting new time in biology. Last week in the Proceedings of the National Academy of Science, two of the most successful bulls of all time had their genomes published.

This is a landmark in dairy herd genomics, but it's most significant as a sign that while genomics remains mostly a curiosity for humans, it's already coming of age when it comes to cattle. It's telling that the cutting-edge genomics company Illumina has precisely one applied market: animal science. They make a chip that measures 50,000 markers on the cow genome for attributes that control the economically important functions of those animals.


Mendel may have worked with plants, the rules he revealed turned out to be universal for all living things. The same could be true of the statistical rules that dairy scientists are learning about how to match up genomic data with the physical attributes they generate. The statistical rules that reflect the way dozens or hundreds of genes come together to make a cow likely to develop mastitis, say, may be formally similar to the rules that govern what makes people susceptible to schizophrenia or prone to living for a long time. Researchers like the University of Queensland's Peter Visscher are bringing the lessons of animal science to bear on our favorite animal, ourselves.

Want to live for a very long time? Well, we hope to discover the group of genes that are responsible for longevity. The problem is that you have genomic data over here and you have phenotypic data, i.e. how things actually are, over there. What you need, then, is some way of translating between these two realms. And it's that matrix, that series of transformations, that animal scientists have been working on for the past decade.

It turned out they were in the perfect spot to look for statistical rules. They had databases of old and new bull semen. They had old and new production data. In essence, it wasn't that difficult to generate rules for transforming genomic data into real-world predictions. Despite -- or because of -- the effectiveness of traditional breeding techniques, molecular biology has been applied in the field for years in different ways. Given that breeders were trying to discover bulls' hidden genetic profiles by evaluating the traits in their offspring that could be measured, it just made sense to start generating direct data about the animals' genomes.

"Each of the bulls on the sire list, we have 50,000 genetic markers. Most of those, we have 700,000," the USDA's VanRaden said. "Every month we get another 12,000 new calves, the DNA readings come in and we send the predictions out. We have a total of 200,000 animals with DNA analysis. That's why it's been so easy. We had such a good phenotype file and we had DNA stored on all these bulls."

They had all that information because for decades, scientists have been taking data from cows to figure out which bulls produced the best offspring. Typically, a bull with a promising pedigree would reach sexual maturity and his semen would be used to impregnate a selection of about 50 test cows. Those daughters would grow up and start producing milk a few years later. The data from those cows would be used to calculate the value of that now "proven" bull. People called the process "progeny testing" and it did not require that breeders knew the exact genetic makeup of a bull. Instead, scientists and breeders could simply say: We do not know the underlying constellations of genes that make this bull so valuable, but we do know how much milk his kids will produce. They learned to use that data to predict who the best bulls were.

That meant that some bulls became incredibly sought after. The number two bull of the last century, Pawnee Farm Arlinda Chief, had more than 16,000 daughters, 500,000 granddaughers, and 2 million great granddaughters. He's responsible for about 14 percent of all the genetic material in all Holsteins, USDA scientists estimate.

"[In the past], we combined performance data -- milk yield, protein yield, confirmation data -- with pedigree information, and ran it through a fairly sophisticated computing gobbledygook," another USDA scientist Curt Van Tassel told a group of dairy farmers. "It spit out at the other end predicted transmitting ability, predicted genetic values of whatever sort. Now what we're trying to do is tweak that black box by introducing genomic data."

There are many different ways you could model the mapping of 50,000 genetic markers onto a dozen performance traits, especially when you have to consider all kinds of environmental factors. So the dairy breeders have been developing and testing statistical models to take all this stuff into account and spit out good predictions of which bulls herd managers should ultimately select.The real promise is not that genomic data will actually be better than the ground-truth information generated from real offspring (though it might be), but rather that the estimates will be close enough to real but save 3 to 4 years per generation. If you don't have to wait for daughters to start cranking out milk, then you can shave those years off the improvement cycle, speeding it up several times.

Nowadays breeders can choose between "genomic bulls," which have been evaluated based purely on their genes and "proven bulls," for which real world data is available. Discussions among dairy breeders show that many are beginning to mix in younger bulls with good-looking genomic data into the breeding regimens. How well has it gone? The first of the bulls who were bred from their genetic profiles alone, are receiving their initial production data. So far, it seems as if the genomic estimates were a little high, but more accurate than traditional methods alone.

The unique dataset and success of dairy breeders now has other scientists sniffing around their findings. Leonid Kruglyak, a genomics professor at Princeton, told me that "a lot of the statistical techniques and methodology" that connect phenotype and genotype were developed by animal breeders. In a sense, they are like codebreakers. If you know the rules of encoding. it's not difficult to put information in one end and have it pop out the other as a code. But if you're starting with the code, that's a brutally difficult problem. And it's the one that diary geneticists have been working on.

Their work could reach outside the medical realm to help us understand human's evolution as well. For example, Kruglyak said, human population geneticists want to figure out how to explain the remarkable lack of genetic variance between human beings. "The typical [genetic] variation among humans is one change in a thousand," he said. "Chimps, though they obviously have a much smaller population now, have several fold higher genetic diversity." How could this be? Researchers hypothesize that human beings once went through a bottleneck where there were very few humans relative both to the current human population and the chimp population. Few humans meant that the gene pool was limited at some point in the pre-historical but fairly recent past. We've never recovered the diversity we might have had.


It might seem that Badger-Bluff Fanny Freddie is the pinnacle of the Holstein bull. He's been the top bull since the day his genetic markers showed up in the USDA database and his real-world performance has backed up his genome's claims. But he's far from the best bull that science can imagine.

John Cole, yet another USDA animal improvement scientist, generated an estimate of the perfect bull by choosing the optimal observed genetic sequences and hypothetically combining them. He found that the optimal bull would have a net merit value of $7,515, which absolutely blows any current bull out of the water. In other words, we're nowhere near creating the perfect milk machine.

The problem, of course, is that genomes cannot really be cut and pasted together from the best bits. "When you go extremely far for one trait, you're going to upset some of the other traits," Vanraden said. Breeding is a messy (i.e. biological) process, no matter how technologically sophisticated the front end. After decades of breeding cows for milk production, people realized (to their dismay) that the ability to generate milk and the ability to have babies were negatively correlated. The more milk you tried to order up, the less babies your herd was likely to have. While we're nowhere near the hypothetical limit for Holstein bull value, we do now know that nature is not so easily transformed without some deleterious effects. We may have factory farms, but these machines are still flesh and blood.

Except for Badger-Fluff Fanny Freddie and his fellow bulls, that is. Freddie is a disembodied creature, an animal that is more important as data than as meat or muscle. Though he's been mentioned in thousands of web pages and dozens of trade industry articles, no one mentions where he was born or where the animal currently lives. He is, for all intents and purposes except for his own, genetic material that comes in the handy form of semen. His thousands of daughters will never smell him and his physical location doesn't matter to anyone. He will be replaced very soon by the next top bull, as subject to the pressures of our economic system as the last version of the iPhone.

Tuesday, May 1, 2012

"Big Data" U.S. Congressional Staff Briefing


May 02, 2012 10:00 AM - 11:30 AM ET | U.S. Capitol Washington, DC

Industry experts will discuss the innovations and benefits derived from data collection and analysis and provide insight into what the term “Big Data” means.

FEATURING OPENING REMARKS BY:
FARNAM JAHANIAN , Assistant Director, National Science Foundation

AND A DISTINGUISHED PANEL OF INDUSTRY EXPERTS:

Nuala O'Connor, Senior Counsel, Information Governance and Chief Privacy Leader, General Electric Co.

Bill Perlowitz, CTO,Science Technology & Engineering Group, WYLE

Caron Kogan , Strategic Planning Director, Lockheed Martin

Nick Combs, Federal CTO, EMC

Flavio Villanustre, VP of Technology, LexisNexis Risk Solutions

Location: Capitol Visitor’s Center (SVC-209)

Collaborative Innovation Not Optional In Today's Economy

Dan Keldsen, InformationWeek,  May 01, 2012
 

Given the global economic changes that began in 2008, we've been forced into a new way of working. Is your company burying its head in the sand, or taking the best that people have to give?
 
There are times when you, as a manager or executive, can take the time to architect a change-management process, much as I described in my previous column. Often, change management, even when intended to make changes easy to understand and transition, is bungled. And other times, change simply happens to us, and the test of our mettle comes with how we react to that change. Bury our heads in the sand and hope it blows over? Or figure out how we're going to survive it, then embrace it and move forward? 

My firm is wrapping up research on collaborative innovation, and we highlighted our findings in a recent webinar. I led that webinar with the context that we're now almost four years after the "econolypse" that smashed the world economy, an economy that's slowly being rebuilt--sometimes successfully, sometimes not. 

The new reality is that there are fewer people in the workplace and there's far more work to be done. (Fear of over-hiring is a serious problem.) Companies with employees who worked ONLY as standalone, departmentalized "cube workers" can no longer afford to work that way.
But as someone who has been involved in both Enterprise 2.0 and innovation management since well before 2008, I've been amazed at how many companies I work with that have seen this massive economic shock as a wakeup call. 
 
One glass-half-empty commenter on our survey sees the econolypse creating a two-fold effect: "First, there are fewer people to source or to do sourcing [for collaborative innovation]. Second, the elimination of jobs has reduced morale and forced all employees to work even harder. The focus now is on things with direct results, which makes less direct things like brainstorming [or collaboration] a tough sell."

Another survey respondent's more positive comment better sums up the attitude of most of my clients: "Pre-2008, [collaborative innovation] was not on my personal radar. Now I have the opportunity to observe how team members work together in an environment that is outside of their normal work routines. This creates stronger organizational bonds across departments and functions. In sum, it builds a stronger and more resilient firm. Ultimately, I have stronger and better managers and teams to rely on."


Creating a "stronger and more resilient firm" is a necessity, as there are no guarantees that the economic shockwaves have stopped.


As you head into the rest of 2012 and the years to follow, consider how you worked pre-2008 versus post-2008, and where you are now. Have you changed enough to weather the next storm? Are you actually getting the best you can out of your workforce?


This isn't just about better collaboration and knowledge-management tools. Another survey respondent summed things up well: "The industry is more accepting now of collaboration and shared intelligence. Tools are better, but so, too, are attitudes."


I've held many technology roles over the years, and much of my work involves finding appropriate technologies to drive collaborative innovation at scale. But if you're waiting for the "perfect" or "best available" technology to power your company out of the econolypse, I'll let another respondent have the final word on collaborative innovation: "If you don't work this way, you miss the best people have to give."
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