Thursday, May 31, 2012

Sal Khan's 'Academy' sparks a tech revolution in education

Marco R. della Cava, USA TODAY, May 30, 2012
  
MOUNTAIN VIEW, Calif. – Most people don't wake up in the morning thinking about how to best explain the financial collapse of the Thai baht in the 1990s. But most people aren't Sal Khan. 
      "When I started, you wouldn't have imagined that some crazy dude in a closet making videos would help lead this charge," says Salman Khan. "But my mission is to have every precocious 13-year-old in the world have access to every bit of information they could ever want."
"When I started, you wouldn't have imagined that some crazy dude in a closet making videos would help lead this charge," says Salman Khan. "But my mission is to have every precocious 13-year-old in the world have access to every bit of information they could ever want." 

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It's not much past 9 a.m., and Khan, 36, founder of the online educational non-profit Khan Academy, gets set to record his 3,081st video lecture in a small office with a view of air conditioning ducts.
"In 1997, you see, there was a devaluation of the Thai currency," Khan says into a beefy microphone as he makes crude sketches on his monitor. 

If you want gleaming high-tech, go down the road to Google's campus. If you're looking for a revolution, this is the right address.

Ever since quitting his job as a successful hedge-fund analyst two years ago to dedicate himself full time to this labor of love, Khan has managed to win fans worldwide and goad skeptical educators.

His simply narrated, faceless home videos on everything from algebra to French history have been viewed half a billion times. Last year, a number of schools began "flipping" their classrooms, having students study Khan videos by night and do homework with teachers by day.

In the process, Khan has fueled the debate over tech's growing influence on education while garnering the support of powerful friends.

"At 3,000 lessons online, Sal's personal ability as a teacher is remarkable," says Bill Gates, whose mention of Khan Academy at a conference in Aspen, Colo., in 2010 put the website on the map. "Bringing this kind of creativity and new assessment tools for teachers could make a profoundly positive difference in education."

Gates' enthusiasm is shared by Silicon Valley philanthropist and Academy backer Ann Doerr, wife of renowned tech investor John Doerr. "Sal imparts a sense of dignity; he assumes each (viewer) is intelligent," says Doerr, who adds that providing free access to knowledge that "allows students to become empowered, drive their own education and test their mettle just seems right."

Khan is taking that praise and sprinting with it.
Where he once was an army of one, the staff has been ramped up to 32, including the recent high-profile addition of Google's first hired employee, programming ace Craig Silverstein. The staff's immediate mission is to further broaden the site's content and improve assessment and feedback features so the Khan Academy experience becomes more interactive.

"We have 6 million visitors a month, so we think that students helping each other is the future," Khan says. "That community can become as popular as the videos themselves. It'll be like having free private tutors in the cloud."

Next up: Camp Khan
Khan's plans are no less ambitious on the ground. This summer, he'll launch the first Khan Academy Discovery Lab in Palo Alto, Calif., a small, project-based summer camp "that's like a lab for us, so we can learn more about how kids learn," he says. If it's a hit, the labs will expand nationwide next year.

And after, perhaps a bricks-and-mortar Khan Academy. "I wouldn't want to be the headmaster of such a place per se, because I want to work on stuff that scales," he says. "But it's a cool idea. A place where teachers make what an engineer would make, where the ideas we have can be on display."

Those ideas have caused friction in the education community. Though critiques vary, most hinge on the inference that classroom-based teachers aren't as important as we thought.

"If a teacher is just lecturing like a computer might, maybe that teacher should be replaced. But the truth is, most teachers don't just drone on, they educate," says Frank Noschese, a physics teacher at John Jay High in Cross River, N.Y., whose personal website dedicates a tab to Khan Academy criticism.

"Some teachers are getting pressure from their administrators to flip their classrooms, but it might not be the right thing for them," Noschese says. "When I first learned about the site, I eagerly e-mailed it around, saying it could be a useful tool. But now all of sudden it's supposed to be reforming education?"

That bandwagon phenomenon is at the root of the grumbling, says Kevin Bushweller, executive editor of Education Week Digital Directions.

"Khan's timing is perfect, because students and parents are living in the age of YouTube, where video watching is routine," he says. "Certainly schools need to evaluate what's best for their kids and curriculum. That said, technology is here, and doing the same old thing just won't work."

Suney Park agrees. The sixth-grade math teacher at Eastside College Prep in nearby East Palo Alto recently flipped her classroom using Khan Academy videos, and now feels liberated. "I had my doubts, but now I feel like the conductor of an orchestra, and if I have to tell the violins to go on with their stuff while I help the brass catch up, I can do it," Park says. "I couldn't go back to the regular way of teaching."

Voice behind the videos
Love it or hate it, Khan Academy is part of a looming tech-education iceberg, says Victor Hu, head of education technology and services for Goldman Sachs. He says that from 2002 to 2006, venture capital firms put $300 million into about 50 tech-ed deals; since 2007, $2 billion has gone into 230 deals.

"Technology is doing to education what it's done to countless other industries: disrupting it," Hu says. "Where education once was static, bound to a textbook, now it's moving to a global, interdisciplinary model."

But even Hu is impressed by Khan's rapid and improbable success. "To go from making videos for your cousins to promoting a new model for education in a few short years is amazing," he says. "But it's also the best thing that can happen to this space. It needs more smart people who care."

By any yardstick, Khan is both. Raised by a single mother in suburban New Orleans, Khan attended a local public school, where "we had smart kids, and we had metal detectors."

He was one of the smart ones, eventually not only captaining the math team but also taking graduate-level mathematics courses at the University of New Orleans. Massachusetts Institute of Technology offered him a scholarship, and four years later, he graduated with two bachelor's degrees and one master's in computer science and math.

"Being a bit crazy intellectually is normal here, and that characterizes Sal," says Anant Agarwal, an early Khan mentor at MIT who now is president of edX, a newly formed non-profit online-education partnership between MIT and Harvard.

"Sal and I have our debates, but we agree that adding a tech dimension to education will have a huge impact worldwide," says Agarwal, whose edX project is echoed by news that Stanford, Princeton, the University of Michigan and the University of Pennsylvania have partnered with Coursera.org to offer classes online.
"What makes Sal's videos particularly engaging is just his personality," Agarwal says. "It's his voice. You feel like he's there next to you, explaining things."

But Khan's almost laid-back delivery belies a fierce drive.

"Besides having a great sense of humor, Sal is also very intense and dedicated," says Shantanu Sinha, who met Khan in high school, roomed with him at MIT and now is Khan Academy's president and COO.
Sinha recalls walking miles in the snow with Khan so the two could tutor gifted children nearby. "He became that focused with his videos," he says. "Heck, he was that focused when he went looking for a wife."

Khan found one, rheumatologist Umaima Marvi, during his time at Harvard Business School, which he attended after an ill-fated foray into the dot-com space at the turn of the millennium. The couple have two children, a 3-year-old boy and a 10-month-old girl.

If Khan is pushing his academy for anyone, it would seem to be for that next generation. He got off a hedge-fund track that was promising high-seven-figure payoffs so he could pilot this startup, one he is particularly thrilled offers no lure of stock options and instant riches.

"I saw firsthand what that did to people (during the dot-com years), and it was ugly," he says. "Sometimes people in Silicon Valley are a bit confused, 'Oh, you're not for profit?' But the core ethos of the valley isn't to buy a Bentley. It's to innovate, do well, and then help the next generation innovate. So we're just part of that."

Khan says he has had no problems luring top talent, and Silverstein's decision to come on board last fall is a case in point. "At first I just thought I'd donate some money, but then I thought it'd be better to donate my time," says Silverstein, whose job is to help other new engineers make the site's user experience more intuitive, something he pioneered at Google.

"Search was a bit like where we're at with Khan Academy. There was a lot out there, and it was just a matter of helping people find what they needed fast," he says. "Who knows where this will go? But helping people learn things is a nice challenge to have."

An argument with oomph
Back in his small office, Khan is holding forth — OK, call it lecturing — on why a college degree may not mean what it used to.

It's full of reasoned examples ("College must give you a job? Well, no, a computer science degree from Berkeley will give you a job, but you go some random place, it's not clear what will happen to you") and makes a simple point (create globally recognized tests that define mastery, which ignores whether the student had come upon that knowledge at Oxford or by sitting in his basement watching videos).

Yet what's most fun is watching Khan make his argument. As with his faceless videos, he is engaging for some indefinable yet undeniable reason. But what if Khan stops making those videos? Will folks keep watching?

"We're letting others in now," says Khan, noting that Stanford will soon put medical classes on his website. "But hopefully they realize from what I've done that it can't be a professor at a whiteboard with PowerPoint. It has to be bite-size and conversational, and no faces."

Khan swivels in his desk chair, then grins broadly.

"When I started, you wouldn't have imagined that some crazy dude in a closet making videos would help lead this charge. But my mission is to have every precocious 13-year-old in the world have access to every bit of information they could ever want."

In other words, his target is the 13-year-old version of Salman Amin "Sal" Khan.

From Student to Teacher
Key steps on Sal Khan’s road from proud math geek to passionate online educator:

·       At Grace King High School in Metairie, La., (other famous alums: Ellen DeGeneres and Donna Brazile), Khan — valedictorian in 1994 — takes advanced-placement math classes. School rules compel all AP students to join the math club, where Khan finds he enjoys mentoring younger students.

·       After graduating from MIT in 1998, Khan uses his computer science degrees to join the start-up meVC as its chief technology officer. He’s on the road to riches when the bubble bursts. He attends Harvard Business School, where he falls in love with Umaima Marvi and the study of capital markets.

·       Rejected by most hedge funds for lack of experience, Khan finds a mentor in Daniel Wohl, who brings him on board Wohl Capital Management in Boston, which soon moves to Palo Alto, Calif. In 2004, Khan help his 13-old-cousin, Nadia, with math problems by making videos in his free time.

·       In 2008, Wohl closes out his fund. He encourages Khan to start his own hedge fund, but the market collapse looms. In 2009, Khan ends his hedge fund work and uses a small donation from philanthropist Ann Doerr to create Khan Academy. A year later, Bill Gates cites Khan’s videos at a conference, and donations flow.

In late 2010, a few Bay Area schools agree to “flip” their curriculums, using Khan videos as homework. Khan accelerates the growth of his non-profit, hiring engineers, management consultants and former teachers. But his biggest coup comes in late 2011 when he lands Craig Silverstein, the man who created Google’s tech architecture as its first employee.

Wednesday, May 30, 2012

Mary Meeker's Annual Internet Trends Report

The Wall Street Journal, May 30, 2012

Mary Meeker, the former Internet analyst known during the dot-com boom as the “Queen of the Net,” has released her annual (and always massive) slide deck on the latest Internet trends.

The 112 slides are heavily focused on rapid mobile adoption and runs through a number of examples of business models that are being re-invented thanks to fast-evolving devices, better connectivity and new interfaces.

Meeker, who joined venture-capital firm Kleiner Perkins from Morgan Stanley last year, cuts to the hear of Kleiner Perkins’ push into what it calls the “third wave of innovation,” combining social networking, mobile and e-commerce.

TechAmerica Foundation Announces Leadership for "Big Data" Commission

SmartData Collective, May 30, 2012

Will Lead Group of 22 Industry Experts and Academics 

WASHINGTON, DC, May 30, 2012 (MARKETWIRE via COMTEX) -- The TechAmerica Foundation announced today that some of the foremost thinkers in "Big Data" will lead a commission of 22 experts and academics to examine the issue and provide guidance on how to leverage "Big Data" to address the most pressing issues facing government as well as drive U.S. innovation and competitiveness. 

Chairing the commission will be Steve Mills, Senior Vice President and Group Executive at IBM and Steve Lucas, Global Executive Vice President and General Manager, Database & Technology at SAP. Serving as vice chairs of the commission are Teresa Carlson, Vice President Global Public Sector, Amazon Web Services and Bill Perlowitz, Chief Technology Officer, Science, Technology and Engineering Group, Wyle. 

"The problem today is not gathering data, but rather making intelligent actionable decisions based on the volume, velocity and variety of data we are receiving. I am excited about launching this Commission and hope that we can leverage the best and brightest industry, academic and government minds to determine how to use big data to drive innovation, efficiencies and effectiveness in the public sector," said Jennifer Kerber, President of the TechAmerica Foundation. 

Big Data is a hot topic for the technology community and is becoming a focal point for government with the Administration recently announcing a $200 Million Big Data Research and Development Initiative. 

"We are entering a new era of computing where information is growing at a record pace. The winners and losers will be those who can innovate faster based on strategic insights drawn from the variety and velocity of new forms of big data being generated every day," said Steve Mills. "By gaining deeper insights into this vast new natural resource, the opportunities to accelerate the pace of discovery in science and engineering and develop information-intensive curriculum is unlimited. 

"The world's data is doubling every 18 months, presenting government and industry with new opportunities to transform information into insight," said Steve Lucas. "New database technologies and applications, coupled with real time analysis of big data, will help business and government run better and ultimately improve the well-being of customers and citizens. By bringing private sector innovation to the public sector, the Big Data Commission will help leaders address some of the biggest questions facing government today." 

The Commission expects to also take up such issues as: what capabilities are required to succeed? How do you use Big Data to make intelligent decisions? How will agencies effectively govern and secure huge volumes of information, while protecting privacy and civil liberties? And perhaps most importantly, how do we use big data to transform how the government delivers services? 

"The federal government is under increasing pressure to innovate and do more with less," said Teresa Carlson. "The U.S federal government has been among the most forward leaning in taking advantage of the benefits of the cloud and Big Data. We're pleased to be a part of TechAmerica Foundation's Commission to further advance the work the federal government is doing on behalf of U.S citizens." 

"Big Data has the potential to increase efficiency, improve the speed and accuracy of decisions, forecast the future, identify savings, increase transparency, create jobs, and provide insight into our agencies and citizenry; this is a hugely disruptive force occurring during challenging economic times. To transform hindsight to insight and remain competitive, we must immediately address the technical, cultural, organizational, and policy challenges data poses and embrace the relentless increase in available data," said Bill Perlowitz. 

The commission membership is made up of leading experts on big data and represent both industry and academia -- the full list of members can be found here. 

To learn more about the TechAmerica Foundation's Big Data Commission: http://www.techamericafoundation.org/big-data-commission
 
About TechAmerica Foundation TechAmerica Foundation educates industry executives, policy makers and opinion leaders on the promise of technological innovation to advance prosperity, security and the general welfare. Launched in 1981, the Foundation is a 501c(3) non-profit, non-partisan affiliate of TechAmerica, which is the leading voice and resource for the U.S. technology industry. The Foundation disseminates award-winning industry, policy and market research covering topics such as U.S. competitiveness in a global economy, innovation in government, and other areas of national interest. It also organizes conferences and seminars to explore pertinent issues with government and industry representatives and to share the Foundation's findings. Learn more about TechAmerica Foundation at www.techamericafoundation.org .
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MIT's 'Big Data' Proposal Wins National Competition to Be Newest Intel Science and Technology Center

Intel Newsroom, May 30, 2012,

SANTA CLARA, Calif., May 30, 2012 (BUSINESS WIRE) -- --The Intel Science and Technology Center (ISTC) for Big Data will be located at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and five additional collaborating universities.
--Research at the ISTC will focus on ways to analyze big datasets to develop solutions for such diverse fields as government, financial services, healthcare and life sciences, manufacturing and retail.
 
Intel Corporation announced today that the Massachusetts Institute of Technology (MIT) has won a national search to become the newest Intel Science and Technology Center (ISTC) for Big Data and will be hosted by MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL).
 
The new research center will seek to accelerate the pace of scientific and medical discoveries, enterprise and industrial computing and encourage innovation in the design of new data-intensive end-user experiences.
 
By creating and funding an ecosystem of leading researchers in the field and providing a collaborative center such as the ISTC for Big Data, Intel and its partners' goal is to explore new computational solutions for how people share, store and manipulate large amounts of data. This will enable the extraction of the right information to gain new additional insights.

"We are witnessing unprecedented growth in unstructured digital data and this will only accelerate further through the rapid increase of mobile Internet devices such as phones, cars and signs, and the projected development of the 'Internet of Things,' which will be constantly sensing the world around us," said Justin Rattner, Intel's chief technology officer. "For this massive amount of what is called 'Big Data' to be useful, it has to be analyzed to be made understandable. Our goal is to innovate and guide this work across multiple fields, from medical to media, to extract meaning from large amounts of data."
 
"At MIT, we are honored that Intel chose CSAIL as the hub of its Intel Science and Technology Center for Big Data; the selection represents a vote of confidence in our ability to work together to accelerate breakthrough research," said Susan Hockfield, MIT president. "One of MIT's defining strengths is our commitment to working with industry allies like Intel."
 
The new ISTC for Big Data is Intel's sixth research center to open since January 2011. Intel is operating five other U.S.-based ISTCs in the fields of visual computing, secure computing, cloud computing, embedded computing and pervasive computing.
 
Big Data Research
 
Research at the new ISTC for Big Data will center on exploring the challenges and opportunities associated with the massive, unstructured and dynamic nature of Big Data. These Big Data attributes are often a poor fit for relational models of conventional database systems and end-user needs for complex, real-time decision-making stay unmet.
 
Specific research will examine designing and prototyping hardware and software for storing, managing, processing, understanding and visualizing data; discovering novel algorithms and scalable, co-designed architectural alternatives; and innovative ways of optimizing modern processor technology trends such as multicore, manycore and emerging non-volatile memory technologies. The research could impact several areas:
 
-- Economic analysis: Research at the center will make it possible to visualize and better understand factors driving the global economy. It will become possible for individuals, traders, corporate strategists and government policymakers to combine and analyze streams of real-time data representing financial transactions, social network effects, climate and political developments for faster and better decisions.
 
-- Healthcare and Life Sciences: Huge amounts of data need to be processed across the medical community. For example, in the complex area of genetics and gene expression, Big Data analytics could bring the hope of creating more customized treatments for people by making sense of the petabytes of information.
 
-- Retail: Today's retail business is a real-time information-driven enterprise. Every customer interaction and movement of a product through a distribution network is measured and used to refine pricing strategies, update inventory decisions and tailor customer incentives. The center's research will focus on how it can help retailers access information that didn't exist before or were too costly or complex, so they are able to delight customers with outstanding selection, service and intelligent personalized offers.
 
-- Government: Safety and security professionals need actionable intelligence that can help predict scenarios that can be utilized in the fields of disaster preparedness, intelligence gathering and even crowd management. The ISTC for Big Data will explore solutions that can help turn their gold mine of data into high-quality information that can increase efficiencies and inform decisions.
 
With MIT as its hub school, the ISTC for Big Data will include faculty research collaborators from six U.S. universities, the others being the University of California at Santa Barbara, Portland State University, Brown University, University of Washington and Stanford University. The ISTC for Big Data is expected to complement and benefit from the synergistic research in related fields of cloud and visual computing at the other ISTCs. The ISTC for Big Data is co-led by MIT professors Michael Stonebreaker and Sam Madden as the university principal investigators, and Intel Fellow Pradeep Dubey as the industry principal investigator. Intel's Jeff Parkhurst will serve as the center's program director.
 
Intel INTC +0.15% is a world leader in computing innovation. The company designs and builds the essential technologies that serve as the foundation for the world's computing devices. Additional information about Intel is available at newsroom.intel.com and blogs.intel.com.
 
Intel and the Intel logo are trademarks of Intel Corporation in the United States and other countries.
 
SOURCE: Intel Corporation

New MIT Book: Networked, The New Social Operating System

Rainie, Lee and Barry Wellman. Networked: The New Social Operating System. MIT Press, May 2012.


Daily life is connected life, its rhythms driven by endless email pings and responses, the chimes and beeps of continually arriving text messages, tweets and retweets, Facebook updates, pictures and videos to post and discuss. Our perpetual connectedness gives us endless opportunities to be part of the give-and-take of networking.

Some worry that this new environment makes us isolated and lonely. But in
Networked, Lee Rainie and Barry Wellman show how the large, loosely knit social circles of networked individuals expand opportunities for learning, problem solving, decision making, and personal interaction. The new social operating system of “networked individualism” liberates us from the restrictions of tightly knit groups; it also requires us to develop networking skills and strategies, work on maintaining ties, and balance multiple overlapping networks.

Rainie and Wellman outline the “triple revolution” that has brought on this transformation: the rise of social networking, the capacity of the Internet to empower individuals, and the always-on connectivity of mobile devices. Drawing on extensive evidence, they examine how the move to networked individualism has expanded personal relationships beyond households and neighborhoods; transformed work into less hierarchical, more team-driven enterprises; encouraged individuals to create and share content; and changed the way people obtain information. Rainie and Wellman guide us through the challenges and opportunities of living in the evolving world of networked individuals.


About the Authors


Lee Rainie is Director of the Pew Research Center’s Internet & American Life Project and former managing editor of
U.S. News and World Report.

Barry Wellman is the S. D. Clark Professor of Sociology at the University of Toronto, where he directs NetLab.

Reviews
“It’s easy to find rigorous science, and it’s easy to find topical stuff, but it’s not easy to find both at the same time!”
—Shankar Vedantam, NPR Science
       
Endorsements
"Lee Rainie and Barry Wellman have combined forces to become the new Marshall McLuhan! They draw on years of observation to weave the threads of the online and offline worlds into a deeply colored tapestry. We can see emergent social norms arising from their moving stories and insightful analyses."
—Vint Cerf, Internet Pioneer
“Lee Rainie and Barry Wellman have woven three enormous changes in the ways we connect--the spread of the internet, mobile tools, and social media--into a single clarifying story of our present and future life in the 21st century.”
—Clay Shirky, author of Cognitive Surplus and Here Comes Everybody
“Just as I would not let my child loose in traffic before I taught her to look both ways, if it were up to me, nobody would be let loose online until they read Networked. From the stories of real people whose lives have been changed, often for the better, by their interactions with contemporary online social networks, to the sociological and psychological theories that explain how life is really changing in the age of ‘networked individualism,’ this is a must-read manual for life online today.”
—Howard Rheingold, critic and author of Net Smart, Tools for Thought, The Virtual Community, and Smart Mobs
       
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Saturday, May 26, 2012

Robert Litan: Big Data Can Save Health Care-But at What Cost to Privacy?

Robert E. Litan, The Atlantic, May 2012


Medical research,  would benefit greatly from massive, publicly shared sets of patient information.

Last month, 30 experts from various backgrounds convened by the Kauffman Foundation issued a report, "Valuing Health Care," that offered some familiar and some not-so-familiar recommendations for improving health-care outcomes and cost-effectiveness. Several of the lesser-known ones focused on how to best collect, aggregate, and share more and better patient health data.

The data at issue includes: patients' medical records, which are now typically held by multiple doctors and hospitals; information that only patients know (e.g., behaviors and life and job histories); genetic information that is only just becoming cheaply available; and health data generated by researchers, pharmaceutical companies, and insurers.

Although health data are highly sensitive and thus require protection, they are also a public good. The more data that researchers are able to analyze, the better chances they have for detecting patterns that can lead to fewer wasteful (and often painful) procedures and tests, and for finding new causes, treatments, and even cures for diseases.

Today, however, the best data analyzers tend to work for financial firms, Google, Facebook, and other high-tech companies, because that's where both the money and the data are. An imperative both for cutting costs and improving treatments in health care is to find creative ways to encourage individuals to divulge data about themselves on a depersonalized basis to databases that can be easily accessed by health care researchers. Once the data are available, they will attract the analytical talent much like honey attracts bees.

Ironically, patient medical records are of only limited value to researchers. Apart from lab and test results, which can be useful, physicians' notes are collected episodically and prepared largely with an eye toward getting paid. Making these records "electronic" won't change their fundamental nature. What researchers could most profit from are databases that link genetic information, medical histories, areas where patients have lived and worked, and how patients behave.

The best way to collect such information is simply to ask people for it. That is exactly what Dr. Susan Love is doing through her "Army of Women," which has so far compiled data on more than 365,000 women concerned about breast cancer. Importantly, just 30 percent of those who have signed up actually have had the disease -- so by following the histories of the recruits to "Love's Army," researchers can compare individuals who do and not have breast cancer, making it easier to isolate the causal factors.

More philanthropies and other organizations battling disease should follow Dr. Love's lead and begin to ask patients for data, too. Employers, for their part, should encourage employees to sign up with organizations of their choice. Further, the organizations initially collecting the data should ask patients who sign up to let the organizations share their data with other research groups. Eventually there may be multiple databases, some of whose members have consented to share their data with other organizations and others who have not. But collectively the organizations should have millions of volunteers whose histories can be tracked by researchers who look for clues to disease origins and possible cures.

Still, at an Atlantic-sponsored event held on April 19 at which the Kauffman report was unveiled, some audience members raised legitimate concerns about the security of these data and whether they could be truly depersonalized. John Wilbanks, a member of the Kauffman Task Force on Cost-Effective Health Care Innovation, which prepared the report, and one of the nation's leading thinkers about data-mining and its risks and benefits, said that there are plenty of gifted hackers who, with the limited public information currently available, can identify each of us with a high degree of likelihood already. He implied that there is a large generational divide between younger people who essentially know or suspect this and just live with it -- look at what's exchanged on Facebook every day -- and older people who are deeply concerned about their privacy and fearful of putting their sensitive health data in any database, however seemingly trustworthy.

My view is that depersonalization at least should be tried, and even if it always doesn't work, many people who want faster cures will be willing to provide their data anyhow.

Would-be or actual patients are not the only ones who should be asked to share their data. A more aggressive mandate to share should exist for researchers themselves, especially when they are funded by taxpayers. The National Institutes of Health (NIH) currently requires research grantees receiving at least $500,000 in federal support to file plans for making their data available to other researchers. This minimalist requirement should not only be more effectively enforced, but NIH should also adopt policies requiring grantees to actually share their data.

Of course, there are many other fixes required to make the health care market look like the markets for other goods and services in our economy -- like transparent pricing and better information about quality of services. But enabling researchers to have access to data about patients -- much as Google, Facebook, Amazon, and many other retailers have data about our buying habits -- is a critically important, but so far overlooked, early step. Lives and money are at stake.

Wednesday, May 23, 2012

MIT Sloan CIO Symposium takes on mobile, data, clouds

This year's symposium highlights the challenges of dealing with legacy while embracing new computing approaches.

Gordon Haff,  MIT Sloan, May 23, 2012
 
CAMBRIDGE, Mass.--Most discussions about where computing is and where it's going end up touching on three big themes: mobility, big data, and cloud computing. Yesterday's MIT Sloan CIO Symposium was no exception, whether those precise terms were used or not.

Perhaps the most striking example of just how rapidly mobile devices are forcing IT organizations to adapt came from Scott Griffith, the CEO of Zipcar, who said that "60 percent of interactions are now through an Android or an iPhone." He also noted that essentially BlackBerry's entire share had shifted to Android over a period of about two and a half years.

Griffith also emphasized the importance of data mining to his company. As with airlines, Zipcar is largely a fixed-cost business in the short term; it therefore makes sense to provide incentives for people to use vehicles at times of low demand. Data mining is the basis for a wide range of price promotions; it's also used for marketing programs such as referrals.

The symposium also provided examples of just how far "Bring Your Own Device" (BYOD) has progressed within some organizations. For example, Frank Modruson, CIO of Accenture, said that roughly 70 percent of the mobile devices at his company are employee purchased. They "put security down on the device" but they provide no support. (They also offer the option of a standard IT-supported device for those who want one.) As large and very distributed IT services organization, Accenture is typical of the sort of company I see moving to BYOD most aggressively.

Perhaps the biggest theme of the day, however, was delivering business innovation through information technology.

Part of this involves simply not doing certain things and moving them to a public cloud provider of some sort. Steven John, the CIO of Workday, said that IT organizations should concentrate on: "What are the things that others can't do? What are the things that only we can do? Focus on business technology knowledge." John went on to observe that public cloud computing provided a certain degree of order through the constraints it places, whereas, historically, customization has caused many IT failures.

It's not always possible to move to new IT structures overnight though. An academic panel moderated by Jason Pontin, the editor in chief of Technology Review, and consisting of Prof. Anant Agarwal, director MIT CSAIL; Prof. Erik Brynjolfsson, MIT Sloan; and Joichi Ito, director of MIT Media Lab, discussed ways to accelerate productivity and change. Advice included making a lot of small bets and shifting towards more data-driven and distributed decision making. The overall consensus was, in the words of Ito "You don't need to spend money any longer. Let people try everything."

However, the CIO panel that followed brought a somewhat different perspective. For example, Thomas Sanzone, SVP of Booz Allen Hamilton, asked "Why are we embarrassed by legacy? What if we have a legacy of success?" He went on to note that the challenge of anything legacy is that the cost of maintenance is far smaller than the cost of development. 

I've heard similar sentiments from many CIOs. Legacy may be a dirty word but it often applies to systems that have been successfully running the business for many years. The challenge is often not so much how to get rid of these legacy systems but how to introduce innovation while dealing with the legacies in whatever way is appropriate.

Accenture's Modruson also observed that IT shouldn't be just about making small bets and failing fast. He said that "If you're a CIO, you want to try new things and not spend much money but if there are big returns to be had with big investments, we'll do those too." He went on to note that planning or lack of planning really depends on what you're doing. "If you're building a highway, it's kind of important to plan."

Former IBMer Irving Wladawsky-Berger suggested to me that some of this apparent disconnect between the academic view and the CIO view really comes down to platforms vs. applications. CIOs are responsible for creating a solid IT infrastructure on which the business can be run and on which innovation can happen. I think, at some level, the panelists who were more focused on the innovation side of the equation were taking the availability of robust infrastructure as something of a given (and, to be sure, perhaps largely ignoring the reality of legacy systems.)

This is the balancing act that we see time and time again with cloud computing. One one side of the scale is the speed, the agility, the low friction of "the cloud" -- whatever the precise form it takes. On the other side are the very real concerns of, not just IT organizations, but the business -- reducing risk, protecting data, and providing reliable service.

The Big Data Value Continuum

Scott Jarr, VoltDB, April 26, 2012
                      
Technology markets are challenging enough to understand but, when you throw in the added noise that typically accompanies early markets, gaining real insights can be next to impossible.  It is not unusual to have ten or more vendors in a particular segment and adjacent segments, and countless products attempting to solve similar customer problems. Needless to say, tech markets are rarely clear in the beginning.

Big Data is just such a market today. One of the challenges we face at VoltDB is making sense of this market and understanding where we fit.  This, of course, demands that we grasp the fundamental nature of the market and understand where others fit as well.

The following post is the first of a two part series that provides commentary to a couple of slides we recently built that aim to articulate the current state of the Big Data market. This discussion has already generated some very interesting dialog, and I’d welcome your inputs as well.

Part 1: The New Nature of Data
1.      Data exists on a time continuum.  Data is no longer stationary.  It moves from function to function within an organization.  Gone are the days where a piece of information arrives, gets locked away in an Oracle (or DB2, MSSQL, etc.) system and is left to grow old and cold.  Big Data is all about leveraging information for its total worth, and that means extracting the maximum value from data at various points in its life.
 
2.      The “things” we do with data are strongly correlated to its age. In the graphic below, we have represented time as the horizontal axis. To the far left is the point at which data is created.

Just after data is created, it is highly interactive.  We want to perform high velocity operations on that data at this stage – how fast can we place a trade, or serve an ad, or inspect a record?

Shortly after creation, we are often interested in a specific data instance relative to other data that has also arrived recently – how is my network traffic trending, what is my composite risk by trading desk, what is the current state of my online game leader board?  Queries like these on high velocity data are commonly referred to as
realtime analytics.

As data begins to age, our interest often changes from “hot” analytics to record-level storage and retrieval – store this URL, retrieve this user profile, etc.  Write it to the database and read it back as necessary, but make sure those reads can be serviced very quickly because there’s often a user waiting for that data on the other side of a browser.

Can you detect the beginning of a trend?  As data begins to age, its value does not diminish, but the
nature of that value does begin to change.

Ultimately data becomes useful in a historical context.  Organizations have found countless ways to gain valuable insights – trends, patterns, anomalies – from data over long timelines. Business intelligence, reporting, back testing are all examples of what we do to extract value from historical data.   Additionally, we are now seeing an increasing use of data science – applications that explore data for increasingly deeper insights – not just
observing trends, but discovering them. I call this Exploratory analytics.   
   
3.      The value of data changes from the individual item to the aggregate over this time line. Now that we have an understanding of how we use data over a timeline, let’s use a real world example to demonstrate how the value of that data changes over the same timeline.

When I buy my new iPad, that individual transaction (data item) is highly valuable – to me, to Apple and to American Express.  Over the course of a week, Apple may use that item with other near term items for realtime analytics – inventory control perhaps or regional trending. But the value of that individual item will rapidly decline.  In 6 months, there will be almost no value in knowing that I bought my iPad in the Boston Apple store on March 17th.

However, there will be tremendous value to Apple when my purchase is looked at relative to many others like me over a six-month period – that is, the value of data is high in the
aggregate.  Finding people of a similar cohort, who buy additional products or services, are valuable insights for Apple to use to sell additional products to large consumer populations (something Apple is very good at doing).  Over time, the value of that one data item (my iPad purchase) is now exclusively in its relationship to many other related purchases.            

So, as we’ve talked here in Part 1, data is no longer purely static. It lives on a continuum where different actions are taken on it and value is derived from it in different ways. This is the promise of Big Data.

Clearly, we can’t use a single datastore to service our needs across the entire continuum.  In part 2, I’ll overlay popular database technologies onto this continuum to illustrate which solutions fit best for different kinds of Big Data needs.    

New Report: Leveraging Data Analytics in Federal Organizations

Helena Sims and Steven Sossei, Corporate Partner Advisory Group, May 2012

“Data analytics is a powerful tool that can help government agencies reduce fraud, waste and abuse. The commercial sector has used data analytics for years to improve decision making, achieve better financial outcomes and improve customer service. The use of data analytics is growing at a rapid rate. The International Data Corporation, a provider of market intelligence in the information technology field, estimates that the business analytics market for software, hardware and consulting services is expected to grow at an 8 percent rate worldwide, reaching nearly $33 billion in 2012.

AGA set out to determine how the federal government is using data analytics and what it is doing with the resulting information…. we learned that some federal agencies have embraced data analytics and have demonstrated the benefits of integrating analytics tools into their operations. As a result, the federal government is in a position to build on the analytic advances it has already made. Some organizations are poised to share their capabilities with other federal organizations and possibly with other levels of government that implement federally funded programs. However, there is no clear plan to leverage the government’s investment in data analytics."

Tuesday, May 22, 2012

Reducing healthcare administrative inefficiencies with big data

Roger Foster, Government Health IT, May 22, 2012


While it is true that organizations across all industries experience a certain degree of inefficient administrative processes, the size and the cost of the problem in the US healthcare industry is colossal.

Indeed, administrative system inefficiencies have been estimated in the range of $100-150 billion annually, and the actual costs could be even higher. According to a position paper by the Medical Group Management Association, “simplifying our healthcare system’s administration could reduce annual healthcare costs by almost $300 billion.”
These structural administrative overhead inefficiencies ultimately increase healthcare cost and decrease the overall quality of public health.

Administrative Costs Are Unsustainable
 
Some of the causes are structural and include market fragmentation around multiple providers and the large number of payers. As a result, we have a complicated set of health delivery systems and a correspondingly complicated set of administrative and billing procedures that must be followed by the patient, provider and payers.

Healthcare providers negotiate rates and enter into contract deals with dozens of health and benefit plans in order to be reimbursed for their services, essentially meaning that each health plan supports its own systems for underwriting, claims administration, provider network contracting and broker network management. The implementation of different systems and approaches for medical coding, billing systems and data feeds creates additional cost to the patient.

A 2011 study showed that if US physicians spent a similar amount of time as their Canadian counterparts on administrative work, the savings would amount to $27.6 billion annually. What’s more, healthcare staff in the US (nurses and medical assistants) spend nearly 10 times more hours on administrative support compared to their Canadian counterparts. The average US hospital spends nearly one quarter of its entire budget on billing and administrative costs.

Some additional quantifiable administrative inefficiencies include:
·       American physicians spend nearly eight hours per week on paperwork
·       Nurses and medical assistants spend 20+ hours per week per physician on administrative tasks not including data quality reporting
·       There are about 14 to 35 health insurance employees per 10,000 plan enrollees
·       Estimates on the percentage of clerical personnel in the US healthcare workforce range from 18 to 27 percent
 
A key step provider and payer agencies need to take to reduce healthcare costs and improve quality is to identify these administrative inefficiencies and use big data tools to significantly reduce these non-value added costs.

3 tips to reduce administrative costs
 
Right now, the healthcare administrative systems are a set of clumsy transactional systems that don’t play well together. Three areas associated with administrative healthcare costs that can be addressed by big data include:

1.      Addressing the costs associated with the administrative transactions to automate (and standardize) the medical approval and billing process between participants in the healthcare system.
2.      Enhancing the coding process between clinical diagnosis and the financial billing systems by upgrading to a modern code system.
3.      Improving the logistics of managing devices and supplies within the hospital and clinical environment.

Progress already underway
 
The current healthcare network consists of large chains of hospitals and linked physicians’ practices that have entered into blanket contracts and fee-for-service arrangements with insurance companies and other medical reimbursement providers. Hospitals (provider organizations) are complicated businesses offering a multitude of services to multiple stakeholders.

At the same time, new entrants such as Wal-Mart and CVS’s Minute Clinic are coming into the market and offering convenient basic medical services right where you shop. If you have the flu you can see the nurse right around the corner at your favorite local drug store. These competitors are taking the low-end part of the healthcare market away from hospital and traditional healthcare providers.

These business pressures will have a growing impact on the bottom-line of large regional medical institutions. Wal-Mart and CVS are using their large-scale big data logistics services to market and deliver basic consumer level care at price-points that hospital have considered too low. These new entrants are interfacing directly into the insurance reimbursement systems as a healthcare provider and carefully managing their administrative costs to be more competitive than hospitals and physician-based practices. They also offer the consumer the convenience of easy access to the pharmacy right after seeing the care provider (usually a nurse).

Better Disease Coding
 
Right now the US healthcare system is planning on moving ICD-9 to the ICD-10 coding system. Standardized codes are used to improve consistency in recording patient symptoms and diagnoses across the healthcare system. The new ICD-10 system updates medical coding from approximately 13,000 to 68,000 codes. This will increase the specificity of the information conveyed in the codes and enable more fidelity for use in health IT systems for both financial and clinical decision support. However, HHS has currently proposed delaying ICD-10 compliance by one year until October 1, 2014.


Improving the fidelity of the medical coding system and shifting healthcare claims processing from a batch model to a near real-time model will be a step in the right direction. This has been successfully done before. Wall Street moved from 7 days to settle trades, to hourly settlements. Big data processing can address the large amount of automation needed between the systems and back-office processes for healthcare administrative data.

Using big data to track assets and supplies
 
Another area of hospital administrative costs that big data can help support is tracking assets and supplies within the hospital environment. This ranges from ensuring the logistical supply chain is in place to ensuring medical supplies are properly stocked and ready for use. It also includes knowing where supplies are or will be needed in the case of emergency response to large-scale disasters. Hospitals including the Veterans Administration’s more than 150 hospital centers are looking at how to use Radio Frequency Identification (RFID) tagging to track assets and supplies.

Assets in the hospital need to be tracked on a real-time basis to know where supplies and equipment are and ensure efficient utilization rates. Some supplies like catheters need to be linked and tracked on a per-patient basis to ensure that they are not inadvertently forgotten on a patient and result in further medical complications. Tracking these medical supplies and linking with the patient’s record will significantly increase the administrative big data challenge within hospitals.


Lots of healthcare administrative systems need to be integrated (interoperate) to make the health care claims and billing system work. Data from multiple sources needs to be integrated. While standardization will help, the reality is that there will always be some variety in the different systems. Big data integration can address the “mesh” of interoperability to support integration across the systems.

In the next article, I will address how big data can be used to reduce healthcare provider inefficiencies and improve the coordination of care across multiple healthcare delivery organizations.
 
Roger Foster is a Senior Director at DRC’s High Performance Technologies Group and advisory board member of the Technology Management program at George Mason University. He has over 20 years of leadership experience in strategy, technology management and operations support for government agencies and commercial businesses. He has worked big data problems for scientific computing in fields ranging from large astrophysical data sets to health information technology. He has a master’s degree in Management of Technology from the Massachusetts Institute of Technology and a doctorate in Astronomy from the University of California, Berkeley. He can be reached at rfoster@drc.com, and followed on Twitter at @foster_roger.

Troves of Personal Data, Forbidden to Researchers


John Markoff, The New York Times, May 21, 2012

PALO ALTO, Calif. — When scientists publish their research, they also make the underlying data available so the results can be verified by other scientists. 

At least that is how the system is supposed to work. But lately social scientists have come up against an exception that is, true to its name, huge. 

It is “big data,” the vast sets of information gathered by researchers at companies like Facebook, Google and Microsoft from patterns of cellphone calls, text messages and Internet clicks by millions of users around the world. Companies often refuse to make such information public, sometimes for competitive reasons and sometimes to protect customers’ privacy. But to many scientists, the practice is an invitation to bad science, secrecy and even potential fraud. 

The issue came to a boil last month at a scientific conference in Lyon, France, when three scientists from Google and the University of Cambridge declined to release data they had compiled for a paper on the popularity of YouTube videos in different countries. 
The chairman of the conference panel — Bernardo A. Huberman, a physicist who directs the social computing group at HP Labs here — responded angrily. In the future, he said, the conference should not accept papers from authors who did not make their data public. He was greeted by applause from the audience. 

In February, Dr. Huberman had published a letter in the journal Nature warning that privately held data was threatening the very basis of scientific research. “If another set of data does not validate results obtained with private data,” he asked, “how do we know if it is because they are not universal or the authors made a mistake?” 

He added that corporate control of data could give preferential access to an elite group of scientists at the largest corporations. “If this trend continues,” he wrote, “we’ll see a small group of scientists with access to private data repositories enjoy an unfair amount of attention in the community at the expense of equally talented researchers whose only flaw is the lack of right ‘connections’ to private data.” 

Facebook and Microsoft declined to comment on the issue. Hal Varian, Google’s chief economist, said he sympathized with the idea of open data but added that the privacy issues were significant. 

“This is one of the reasons the general pattern at Google is to try to release data to everyone or no one,” he said. “I have been working to get companies to release more data about their industries. The idea is that you can provide proprietary data aggregated in a way that poses no threats to privacy.” 

The debate will only intensify as large companies with deep pockets do more research about their users. “In the Internet era,” said Andreas Weigend, a physicist and former chief scientist at Amazon, “research has moved out of the universities to the Googles, Amazons and Facebooks of the world.” 

But while social and data scientists agree on the importance of replicating experimental results, there is less consensus on what should be done and how to deal with concerns about privacy. 

At leading social science journals, there are few clear guidelines on data sharing. “The American Journal of Sociology does not at present have a formal position on proprietary data,” its editor, Andrew Abbott, a sociologist at the University of Chicago, wrote in an e-mail. “Nor does it at present have formal policies enforcing the sharing of data.” 

The problem is not limited to the social sciences. A recent review found that 44 of 50 leading scientific journals instructed their authors on sharing data but that fewer than 30 percent of the papers they published fully adhered to the instructions. A 2008 review of sharing requirements for genetics data found that 40 of 70 journals surveyed had policies, and that 17 of those were “weak.” 

The data-sharing policy of the journal Science says, “All data necessary to understand, assess and extend the conclusions of the manuscript must be available to any reader of Science.” But in the case of a 2010 article based on data from cellphone patterns, a legal agreement with the data provider prevented the researchers from even disclosing the country of origin. 

Ginger Pinholster, a spokeswoman for the American Association for the Advancement of Science, which publishes the journal, acknowledged that on “rare occasions” Science does allow exceptions to its publication guidelines to protect privacy. “Information about movements in particular locations” could provide personal information, she said, “and the authors also had to promise privacy in order to get the information from the phone company.”
The journal did not note the policy exception when it published the article. 

Similarly, an April 2011 article in the journal PLoS One stated that the research was “based on the records of 72.4 million calls and 17.1 million text messages accumulated over a one-month period,” but did not identify the provider of the information. 

A founder of PLoS, Michael Eisen, a cell biologist at the University of California, Berkeley, who is a a forceful advocate for “open science,” sounded rueful about that paper in an e-mail message. “It’s antithetical to the basic norms of science to make claims that cannot be validated because the necessary data are proprietary,” he wrote. 

The issue was foreshadowed in a 2009 essay in Science whose authors included Albert-Laszlo Barabasi, a physicist at Northeastern University who was also an author of the controversial papers in Science and PLoS One. 

“Perhaps the thorniest challenges exist on the data side, with respect to access and privacy,” they wrote. They warned that even anonymizing data sets could be imperfect, and they called for new models for collaboration between industry and academia to aid research and safeguard privacy. 

Last year the National Science Foundation said that researchers who receive its funds would be “expected” to share data with other researchers. 

Many scientists agree that this is as it should be. 

 e access to data,” said Alex Pentland, director of the Human Dynamics Laboratory at M.I.T. “That is beginning to happen as governments and industry realize that they need to better understand the promise and limits of big data; for instance, we will be announcing a huge, multicountry release of phone data soon.”
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