The new frontline of modern warfare.
BY STANLEY A. MCCHRYSTAL | MARCH/APRIL 2011
From the outset of my command in Afghanistan, two or three times each week, accompanied by a few aides and often my Afghan counterparts, I would leave the International Security Assistance Force headquarters in Kabul and travel across Afghanistan -- from critical cities like Kandahar to the most remote outposts in violent border regions. Ideally, we left early, traveling light and small, normally using a combination of helicopters and fixed-wing aircraft, to meet with Afghans and their leaders and to connect with our troops on the ground: Brits and Marines rolling back the enemy in Helmand, Afghan National Army troops training in Mazar-e-Sharif, French Foreign Legionnaires patrolling in Kapisa.
But I was not alone: There were other combatants circling the battlefield. Mirroring our movements, competing with us, were insurgent leaders. Connected to, and often directly dispatched by, the Taliban's leadership in Pakistan, they moved through the same areas of Afghanistan. They made shows of public support for Taliban shadow governors, motivated tattered ranks, recruited new troops, distributed funds, reviewed tactics, and updated strategy. And when the sky above became too thick with our drones, their leaders used cell phones and the Internet to issue orders and rally their fighters. They aimed to keep dispersed insurgent cells motivated, strategically wired, and continually informed, all without a rigid -- or targetable -- chain of command.
While a deeply flawed insurgent force in many ways, the Taliban is a uniquely 21st-century threat. Enjoying the traditional insurgent advantage of living amid a population closely tied to them by history and culture, they also leverage sophisticated technology that connects remote valleys and severe mountains instantaneously -- and allows them to project their message worldwide, unhindered by time or filters. They are both deeply embedded in Afghanistan's complex society and impressively agile. And just like their allies in al Qaeda, this new Taliban is more network than army, more a community of interest than a corporate structure.
For the U.S. military that I spent my life in, this was not an easy insight to come by. It was only over the course of years, and with considerable frustrations, that we came to understand how the emerging networks of Islamist insurgents and terrorists are fundamentally different from any enemy the United States has previously known or faced.
In bitter, bloody fights in both Afghanistan and Iraq, it became clear to me and to many others that to defeat a networked enemy we had to become a network ourselves. We had to figure out a way to retain our traditional capabilities of professionalism, technology, and, when needed, overwhelming force, while achieving levels of knowledge, speed, precision, and unity of effort that only a network could provide. We needed to orchestrate a nuanced, population-centric campaign that comprised the ability to almost instantaneously swing a devastating hammer blow against an infiltrating insurgent force or wield a deft scalpel to capture or kill an enemy leader.
WHEN I FIRST WENT TO IRAQ in October 2003 to command a U.S. Joint Special Operations Task Force (JSOTF) that had been tailored down to a relatively small size in the months following the initial invasion, we found a growing threat from multiple sources -- but particularly from al Qaeda in Iraq (AQI). We began a review of our enemy, and of ourselves. Neither was easy to understand.
Like all too many military forces in history, we initially saw our enemy as we viewed ourselves. In a small base outside Baghdad, we started to diagram AQI on white dry-erase boards. Composed largely of foreign mujahideen and with an overall allegiance to Osama bin Laden but controlled inside Iraq by the Jordanian Abu Musab al-Zarqawi, AQI was responsible for an extremely violent campaign of attacks on coalition forces, the Iraqi government, and Iraqi Shiites. Its stated aim was to splinter the new Iraq and ultimately establish an Islamic caliphate. By habit, we started mapping the organization in a traditional military structure, with tiers and rows. At the top was Zarqawi, below him a cascade of lieutenants and foot soldiers.
But the closer we looked, the more the model didn't hold. Al Qaeda in Iraq's lieutenants did not wait for memos from their superiors, much less orders from bin Laden. Decisions were not centralized, but were made quickly and communicated laterally across the organization. Zarqawi's fighters were adapted to the areas they haunted, like Fallujah and Qaim in Iraq's western Anbar province, and yet through modern technology were closely linked to the rest of the province and country. Money, propaganda, and information flowed at alarming rates, allowing for powerful, nimble coordination. We would watch their tactics change (from rocket attacks to suicide bombings, for example) nearly simultaneously in disparate cities. It was a deadly choreography achieved with a constantly changing, often unrecognizable structure.
Over time, it became increasingly clear -- often from intercepted communications or the accounts of insurgents we had captured -- that our enemy was a constellation of fighters organized not by rank but on the basis of relationships and acquaintances, reputation and fame. Who became radicalized in the prisons of Egypt? Who trained together in the pre-9/11 camps in Afghanistan? Who is married to whose sister? Who is making a name for himself, and in doing so burnishing the al Qaeda brand?
All this allowed for flexibility and an impressive ability to grow and to sustain losses. The enemy does not convene promotion boards; the network is self-forming. We would watch a young Iraqi set up in a neighborhood and rise swiftly in importance: After achieving some tactical success, he would market himself, make connections, gain followers, and suddenly a new node of the network would be created and absorbed. The network's energy grew.
In warfare, you make decisions based on indicators. When facing the enemy, you estimate its tactical strength and intuit its planned strategy. This is much simpler when the enemy is a column advancing toward you in plain sight. Our problem in both the Iraq of 2003 and the Afghanistan of today is that indicators popped up everywhere, unevenly and unexpectedly, and often disappeared as quickly as they emerged, flickering in view for only a moment.
We realized we had to have the rapid ability to detect nuanced changes, whether the emergence of new personalities and alliances or sudden changes in tactics. And we had to process that new information in real time -- so we could act on it. A stream of hot cinders was falling everywhere around us, and we had to see them, catch those we could, and react instantly to those we had missed that were starting to set the ground on fire.
SHORTLY AFTER TAKING COMMAND of the JSOTF, I visited one of our teams in Mosul, the largest city in northern Iraq, which was at that time under the able command of then-Maj. Gen. David Petraeus and the troops of the 101st Airborne Division. Although Mosul was still less violent than some other areas of the country, it was clear that al Qaeda was organizing to aggressively contest control of the city -- and, from there, all of northern Iraq.
Our special operations force there was small: about 15 men, supported by a single intelligence analyst. They were set up in a corner of a larger base, operating quietly from a modest white trailer. Although they coordinated with the military forces and civilian (particularly intelligence) agencies on the base, operational security procedures and cultural habits limited the true synergy of their effort against AQI and the fight for the city that lay outside the base's gates.
Moreover, the few antennas that adorned the trailer's roof were unable to pump enough classified information between them and our task force headquarters (or other teams in Iraq) with any timeliness. It wasn't a marooned outpost, thanks to the remarkable team that manned the effort. But it felt like one.
http://www.foreignpolicy.com/files/fp_uploaded_images/110221_815-mcchrystal-hourglass.jpg
That night, on the plane back to Baghdad, I drew an hourglass on a yellow legal pad. The top half of the hourglass represented the team in Mosul; the other represented our task force HQ. They met at just one narrow point. At the top, our team in Mosul was accumulating knowledge and experience, yet lacked both the bandwidth and intelligence manpower to transmit, receive, or digest enough information either to effectively inform, or benefit from, its more robust task force headquarters. All across the country -- in Tikrit, Ramadi, Fallujah, Diyala -- we were waging similarly compartmentalized campaigns. It made our hard fight excruciatingly difficult, and potentially doomed.
The sketch from that evening -- early in a war against an enemy that would only grow more complex, capable, and vicious -- was the first step in what became one of the central missions in our effort: building the network. What was hazy then soon became our mantra: It takes a network to defeat a network.
But fashioning ourselves to counter our enemy's network was easier said than done, especially because it took time to learn what, exactly, made a network different. As we studied, experimented, and adjusted, it became apparent that an effective network involves much more than relaying data. A true network starts with robust communications connectivity, but also leverages physical and cultural proximity, shared purpose, established decision-making processes, personal relationships, and trust. Ultimately, a network is defined by how well it allows its members to see, decide, and effectively act. But transforming a traditional military structure into a truly flexible, empowered network is a difficult process.
Our first attempt at a network was to physically create one. We convinced the agencies partnered with the JSOTF to join us in a big tent at one of our bases so that we could share and process the intelligence in one location. Operators and analysts from multiple units and agencies sat side by side as we sought to fuse our intelligence and operations efforts -- and our cultures -- into a unified effort. This may seem obvious, but at the time it wasn't. Too often, intelligence would travel up the chain in organizational silos -- and return too slowly for those in the fight to take critical action.
It was clear, though, that in this fusion process we had created only a partial network: Each agency or operation had a representative in the tent, but that was not enough. The network needed to expand to include everyone relevant who was operating within the battlespace. Incomplete or unconnected networks can give the illusion of effectiveness, but are like finely crafted gears whose movement drives no other gears.
This insight allowed us to move closer to building a true network by connecting everyone who had a role -- no matter how small, geographically dispersed, or organizationally diverse they might have been -- in a successful counterterrorism operation. We called it, in our shorthand, F3EA: find, fix, finish, exploit, and analyze. The idea was to combine analysts who found the enemy (through intelligence, surveillance, and reconnaissance); drone operators who fixed the target; combat teams who finished the target by capturing or killing him; specialists who exploited the intelligence the raid yielded, such as cell phones, maps, and detainees; and the intelligence analysts who turned this raw information into usable knowledge. By doing this, we speeded up the cycle for a counterterrorism operation, gleaning valuable insights in hours, not days.
But it took a while to get there. The process started as a linear, relatively inefficient chain. Out of habit (and ignorance), each element gave the next group the minimum amount of information needed for it to be able to complete its task. Lacking sufficient shared purpose or situational awareness, each component contributed far less to the outcome than it could or should have.
This made us, in retrospect, painfully slow and uninformed. The linear process created what we called "blinks" -- time delays and missed junctures where information was lost or slowed when filtered down the line. In the early days of the effort, we had multiple experiences where information we captured could not be exploited, analyzed, or reacted to quickly enough -- giving enemy targets time to flee. A blink often meant a missed opportunity in an unforgiving fight.
The key was to reduce the blinks, and we did so by attempting to create a shared consciousness between each level of the counterterrorism teams.
We started by sharing information: Video streamed by the drones was sent to all the participants -- not just the reconnaissance and surveillance analysts controlling them. When an operation was set in motion, information was continuously communicated to and from the combat team, so that intelligence specialists miles away could alert the team on the ground about what they could expect to find of value at the scene and where it might be. Intelligence recovered on the spot was instantly pushed digitally from the target to analysts who could translate it into actionable data while the operators would still be clearing rooms and returning fire. This knowledge was immediately cycled back through the loop to our intelligence and surveillance forces following the results of the raid in real time.
The intelligence recovered on one target in, say, Mosul, might allow for another target to be found, fixed upon, and finished in Baghdad, or even Afghanistan. Sometimes, finding just one initial target could lead to remarkable results: The network sometimes completed this cycle three times in a single night in locations hundreds of miles apart -- all from the results of the first operation. As our operations in Iraq and Afghanistan intensified, the number of operations conducted each day increased tenfold, and both our precision and success rate also rose dramatically.
Although we got our message out differently than did our enemies, both organizations increasingly shared basic attributes that define an effective network. Decisions were decentralized and cut laterally across the organization. Traditional institutional boundaries fell away and diverse cultures meshed. The network expanded to include more groups, including unconventional actors. It valued competency above all else -- including rank. It sought a clear and evolving definition of the problem and constantly self-analyzed, revisiting its structure, aims, and processes, as well as those of the enemy. Most importantly, the network continually grew the capacity to inform itself.
From its birth in Iraq, both the actual network -- and the hard-earned appreciation for that organizational model -- increasingly expanded to Afghanistan, especially as our nation's focus turned toward that theater. When I became the commander there, we set about building a robust communications architecture and worked to establish relationships with key actors, moving frequently around the country to instill the shared consciousness and purpose necessary for a networked modern army. But that was only the first part of the task.
As we learned to build an effective network, we also learned that leading that network -- a diverse collection of organizations, personalities, and cultures -- is a daunting challenge in itself. That struggle remains a vital, untold chapter of the history of a global conflict that is still under way.
Sunday, February 27, 2011
Social Networking's Newest Friend: Genomics
By Emily Singer, Technology Review, MIT, Feb 24, 2011
The first large-scale study to combine genome sequencing and social-network analysis solves a mysterious TB outbreak.
It was the baby's case that first caught people's attention: an infant in a medium-sized community in British Columbia that was diagnosed with tuberculosis in July 2006. When public health workers took a deeper look at the community's medical records, they found a number of additional cases suggestive of an outbreak. By December 2008, 41 cases had been identified, bumping up the region's annual incidence rate by a factor of 10.
Officials at the BC Centre for Disease Control (BCCDC) were faced with the question at the heart of any outbreak: what was the source? Had the bacteria that cause TB mutated to become more infectious? Or was there some change in the community that made the microbes more likely to spread?
The answer would be crucial in focusing public health efforts to stop it. Traditional methods for analyzing transmission patterns created only a hazy picture. Molecular analysis of specimens collected from patients suggested everyone was infected with the same strain. "Based on the information we had, we couldn't really figure out who was giving it to whom," says Patrick Tang, a medical microbiologist at the BCCDC.
So Tang and collaborators combined two tools to create a much clearer picture of the outbreak: social-network analysis, which has become increasingly common in tracking infectious disease over the last decade, and whole-genome sequencing—an analysis of the microbe's entire DNA sequence. The latter, which has been applied to outbreaks in only a few cases to date, allows much more precise tracing of infections than traditional molecular techniques, which look at only a few spots in the genome.
"For the first time, we can paint a really detailed picture of the relationships between people in the community and a really detailed picture of the relationships between the bacteria themselves," says Jennifer Gardy, head of the BCCDC's Genome Research Laboratory and lead author on the study. "We can reconstruct the path an organism took throughout a population."
Researchers sequenced the genomes of 36 bacterial samples collected from patients. They used specialized algorithms to compare individual mutations that arose in the microbes' DNA as they spread. The analysis, published today in the New England Journal of Medicine, revealed that there were actually two different lineages of the microbe, pointing to two different outbreaks spreading independently of one another. These findings suggested that an environmental factor lay at the heart of the outbreak, rather than a genetic one.
In addition to genome sequencing, researchers questioned patients about the people they lived with and worked with, as well as where they spent their time, creating a network diagram of potential interactions. "Instead of just getting a list of names, you're getting names, places, and behaviors, and you can paint a much more detailed picture of the underlying community structure," says Gardy. "Key people and places and certain behaviors that might be contributing to an outbreak's spread become much more apparent, and allow you to adjust your outbreak investigation in real-time as this new information becomes available."
The researchers could overlay the genetic data identifying individual mutations with information from the social network that pinpointed when different people might have interacted with each other. "We could identify super-spreaders of the disease," says Tang.
The researchers ultimately concluded that the outbreak was linked to an increase in crack cocaine use in the community. "That was the most likely trigger, reactivating latent disease and facilitating the spread of disease," says Tang. Using this information, public health agencies could focus their resources on the root of the problem and identify those at the highest risk for reactivation of TB, he says.
"The findings show that it is feasible to combine genetic data and social structure to give an idea of the transmission chain and to distinguish two outbreaks going on at the same time," says Joel Miller, a research fellow in the Center for Communicable Disease Dynamics at the Harvard School of Public Health.
Tang and others predict that this approach will become commonplace in the next few years. "With the cost of whole-genome sequencing coming down—it's a few hundred dollars per organism—a lot of people are interested in using it to address questions worldwide," says Tang. He says sequencing will be especially important in more complex cases, such as tracking the spread of antibiotic-resistant organisms around the world.
The main hurdle now is not the cost of sequencing, but rather the analysis tools, adds Tang. "The limitation for most people is how to make sense of the genomic data that is generated," he says.
The first large-scale study to combine genome sequencing and social-network analysis solves a mysterious TB outbreak.
It was the baby's case that first caught people's attention: an infant in a medium-sized community in British Columbia that was diagnosed with tuberculosis in July 2006. When public health workers took a deeper look at the community's medical records, they found a number of additional cases suggestive of an outbreak. By December 2008, 41 cases had been identified, bumping up the region's annual incidence rate by a factor of 10.
Officials at the BC Centre for Disease Control (BCCDC) were faced with the question at the heart of any outbreak: what was the source? Had the bacteria that cause TB mutated to become more infectious? Or was there some change in the community that made the microbes more likely to spread?
The answer would be crucial in focusing public health efforts to stop it. Traditional methods for analyzing transmission patterns created only a hazy picture. Molecular analysis of specimens collected from patients suggested everyone was infected with the same strain. "Based on the information we had, we couldn't really figure out who was giving it to whom," says Patrick Tang, a medical microbiologist at the BCCDC.
So Tang and collaborators combined two tools to create a much clearer picture of the outbreak: social-network analysis, which has become increasingly common in tracking infectious disease over the last decade, and whole-genome sequencing—an analysis of the microbe's entire DNA sequence. The latter, which has been applied to outbreaks in only a few cases to date, allows much more precise tracing of infections than traditional molecular techniques, which look at only a few spots in the genome.
"For the first time, we can paint a really detailed picture of the relationships between people in the community and a really detailed picture of the relationships between the bacteria themselves," says Jennifer Gardy, head of the BCCDC's Genome Research Laboratory and lead author on the study. "We can reconstruct the path an organism took throughout a population."
Researchers sequenced the genomes of 36 bacterial samples collected from patients. They used specialized algorithms to compare individual mutations that arose in the microbes' DNA as they spread. The analysis, published today in the New England Journal of Medicine, revealed that there were actually two different lineages of the microbe, pointing to two different outbreaks spreading independently of one another. These findings suggested that an environmental factor lay at the heart of the outbreak, rather than a genetic one.
In addition to genome sequencing, researchers questioned patients about the people they lived with and worked with, as well as where they spent their time, creating a network diagram of potential interactions. "Instead of just getting a list of names, you're getting names, places, and behaviors, and you can paint a much more detailed picture of the underlying community structure," says Gardy. "Key people and places and certain behaviors that might be contributing to an outbreak's spread become much more apparent, and allow you to adjust your outbreak investigation in real-time as this new information becomes available."
The researchers could overlay the genetic data identifying individual mutations with information from the social network that pinpointed when different people might have interacted with each other. "We could identify super-spreaders of the disease," says Tang.
The researchers ultimately concluded that the outbreak was linked to an increase in crack cocaine use in the community. "That was the most likely trigger, reactivating latent disease and facilitating the spread of disease," says Tang. Using this information, public health agencies could focus their resources on the root of the problem and identify those at the highest risk for reactivation of TB, he says.
"The findings show that it is feasible to combine genetic data and social structure to give an idea of the transmission chain and to distinguish two outbreaks going on at the same time," says Joel Miller, a research fellow in the Center for Communicable Disease Dynamics at the Harvard School of Public Health.
Tang and others predict that this approach will become commonplace in the next few years. "With the cost of whole-genome sequencing coming down—it's a few hundred dollars per organism—a lot of people are interested in using it to address questions worldwide," says Tang. He says sequencing will be especially important in more complex cases, such as tracking the spread of antibiotic-resistant organisms around the world.
The main hurdle now is not the cost of sequencing, but rather the analysis tools, adds Tang. "The limitation for most people is how to make sense of the genomic data that is generated," he says.
Treat the Patient, Not the CT Scan
By ABRAHAM VERGHESE, NYT, FEBRUARY 26, 2011
Palo Alto, Calif.
The other day as I walked through a wing of my hospital, it occurred to me that Watson, I.B.M.’s supercomputer, would be more at home here than he was on “Jeopardy!” Perhaps it’s good, I thought, that his next challenge, with the aid of the Columbia University Medical Center and the University of Maryland School of Medicine, will be to learn to diagnose illnesses and treat patients.
On our rounds of the wards, Watson would see lots of other computers with humans glued to them like piglets at a sow’s teats. We might visit a patient with a complex illness — one whose second liver transplant has failed, who has a fungal meningitis and now also has kidney failure and bleeding and is on a score of medications.
Watson might help me digest the sheer volume of data that is in the electronic medical record and might see trends in the data that speak of an impending disaster. And since Watson is constantly trolling the Web, he would perhaps bring to my attention a case report published the previous night in a Swedish journal describing a new interaction between two of the drugs my patient is taking.
Better still, if Watson could harness data from all the patients in our hospital and in every other hospital in America, we might be alerted to mini-epidemics taking shape. For example, Watson might recognize that the kidney failure in our patient is linked to kidney failure in a patient in Buffalo and another in San Antonio; all three patients, he might inform me, were taking a “natural” weight loss supplement that contained a Chinese herb, aristolochia, that has been associated with more than 100 cases of kidney failure.
In short, Watson would be a potent and clever companion as we made our rounds. But the complaints I hear from patients, family and friends are never about the dearth of technology but about its excesses. My own experience as a patient in an emergency room in another city helped me see this. My nurse would come in periodically to visit the computer work station in my cubicle, her back to me while she clicked and scrolled away. Over her shoulder she said, “On a scale of one to five how is your ...?”
The electronic record of my three-hour stay would have looked perfect, showing close monitoring, even though to me as a patient it lacked a human dimension. I don’t fault the nurse, because in my hospital, despite my best intentions, I too am spending too much time in front of the computer: the story of my patient’s many past admissions, the details of surgeries undergone, every consultant’s opinion, every drug given over every encounter, thousands of blood tests and so many CT scans, M.R.I.’s and ultrasound images reside in there.
This computer record creates what I call an “iPatient” — and this iPatient threatens to become the real focus of our attention, while the real patient in the bed often feels neglected, a mere placeholder for the virtual record.
Imaging the body has become so easy (and profitable, too, if you own the machine). When I was an intern some 30 years ago, about three million CT scans were performed annually in the United States; now the number is more like 80 million. Imaging tests are now responsible for half of the overall radiation Americans are exposed to, compared with about 15 percent in 1980.
With that radiation exposure comes increasing risk for cancer, but what worries me even more is that this ease of ordering a scan has caused doctors’ most basic skills in examining the body to atrophy. This loss is palpable when American medical trainees go to hospitals and clinics abroad with few resources: it can be quite humbling to see doctors in Africa and South America detect fluid around patients’ lungs not with X-rays but by percussing the chest with their fingers and listening with their stethoscopes.
Of course, we still teach medical students how to properly examine the body. In dedicated physical diagnosis courses in their first and second years, students learn on trained actors, who give them appropriate stories and responses, how to do a complete exam of the body’s systems (circulatory, respiratory, musculoskeletal and the rest). Faculty members stand by to assess that the required maneuvers are performed correctly.
But all that training can be undone the moment the students hit their clinical years. Then, they discover that the currency on the ward seems to be “throughput” — getting tests ordered and getting results, having procedures like colonoscopies done expeditiously, calling in specialists, arranging discharge. And the engine for all of that, indeed the place where the dialogue between doctors and nurses takes place, is the computer.
The consequence of losing both faith and skill in examining the body is that we miss simple things, and we order more tests and subject people to the dangers of radiation unnecessarily. Just a few weeks ago, I heard of a patient who arrived in an E.R. in extremis with seizures and breathing difficulties. After being stabilized and put on a breathing machine, she was taken for a CT scan of the chest, to rule out blood clots to the lung; but when the radiologist looked at the results, she turned out to have tumors in both breasts, along with the secondary spread of cancer all over the body.
In retrospect, though, her cancer should have been discovered long before the radiologist found it; before the emergency, the patient had been seen several times and at different places, for symptoms that were probably related to the cancer. I got to see the CT scan: the tumor masses in each breast were likely visible to the naked eye — and certainly to the hand. Yet they had never been noted.
Too frequently, I hear of (and in a study we are conducting, I am collecting) stories like that from all across the country. They represent a type of error that stems from not making use of basic bedside skills, not asking the patient to fully disrobe. It is a more subtle kind of error than operating on the wrong limb; indeed, this sort of mistake is not always recognized, and yet the consequences can be grave.
IN my experience, being skilled at examining the body has a salutary effect beyond finding important clues that lead to an early diagnosis. It is a ritual that remains important to the patient. Recently my ward team admitted an elderly woman who had been transferred from her nursing home in the night because of a change in her mental status. A CT of the head and all other tests were determined to be normal; the problem had been dehydration, and she was better, ready to go back. But as our team was about to enter the room, my intern warned me that the patient’s lawyer daughter was unhappy with the plan to return her mother to the nursing home, and was waiting impatiently to see me and contest the transfer.
After introducing myself to the patient and to her daughter, I did a thorough but quick neurologic exam. I put the patient through her paces: mental status, cranial nerves, motor and sensory function, used my reflex hammer and pointed out interesting things along the way to my interns and students. I then said to the daughter that her mother seemed back to normal. To our surprise, the daughter seemed comforted, and now had no objection to her mother’s return to the nursing home.
Later, our team discussed what had just happened. We all felt that the daughter witnessing the examination of the patient, that ritual, was the key to earning both their trusts. I find that patients from almost any culture have deep expectations of a ritual when a doctor sees them, and they are quick to perceive when he or she gives those procedures short shrift by, say, placing the stethoscope on top of the gown instead of the skin, doing a cursory prod of the belly and wrapping up in 30 seconds. Rituals are about transformation, the crossing of a threshold, and in the case of the bedside exam, the transformation is the cementing of the doctor-patient relationship, a way of saying: “I will see you through this illness. I will be with you through thick and thin.” It is paramount that doctors not forget the importance of this ritual.
An answer that might have been posed on “Jeopardy!” is, “An emergency treatment that is administered by ear.” I wonder if Watson would have known the question (though he will now, cybertroller that he is), which is, “What are words of comfort?”
Abraham Verghese, a professor at the Stanford University School of Medicine, is the author of the novel “Cutting for Stone.”
Palo Alto, Calif.
The other day as I walked through a wing of my hospital, it occurred to me that Watson, I.B.M.’s supercomputer, would be more at home here than he was on “Jeopardy!” Perhaps it’s good, I thought, that his next challenge, with the aid of the Columbia University Medical Center and the University of Maryland School of Medicine, will be to learn to diagnose illnesses and treat patients.
On our rounds of the wards, Watson would see lots of other computers with humans glued to them like piglets at a sow’s teats. We might visit a patient with a complex illness — one whose second liver transplant has failed, who has a fungal meningitis and now also has kidney failure and bleeding and is on a score of medications.
Watson might help me digest the sheer volume of data that is in the electronic medical record and might see trends in the data that speak of an impending disaster. And since Watson is constantly trolling the Web, he would perhaps bring to my attention a case report published the previous night in a Swedish journal describing a new interaction between two of the drugs my patient is taking.
Better still, if Watson could harness data from all the patients in our hospital and in every other hospital in America, we might be alerted to mini-epidemics taking shape. For example, Watson might recognize that the kidney failure in our patient is linked to kidney failure in a patient in Buffalo and another in San Antonio; all three patients, he might inform me, were taking a “natural” weight loss supplement that contained a Chinese herb, aristolochia, that has been associated with more than 100 cases of kidney failure.
In short, Watson would be a potent and clever companion as we made our rounds. But the complaints I hear from patients, family and friends are never about the dearth of technology but about its excesses. My own experience as a patient in an emergency room in another city helped me see this. My nurse would come in periodically to visit the computer work station in my cubicle, her back to me while she clicked and scrolled away. Over her shoulder she said, “On a scale of one to five how is your ...?”
The electronic record of my three-hour stay would have looked perfect, showing close monitoring, even though to me as a patient it lacked a human dimension. I don’t fault the nurse, because in my hospital, despite my best intentions, I too am spending too much time in front of the computer: the story of my patient’s many past admissions, the details of surgeries undergone, every consultant’s opinion, every drug given over every encounter, thousands of blood tests and so many CT scans, M.R.I.’s and ultrasound images reside in there.
This computer record creates what I call an “iPatient” — and this iPatient threatens to become the real focus of our attention, while the real patient in the bed often feels neglected, a mere placeholder for the virtual record.
Imaging the body has become so easy (and profitable, too, if you own the machine). When I was an intern some 30 years ago, about three million CT scans were performed annually in the United States; now the number is more like 80 million. Imaging tests are now responsible for half of the overall radiation Americans are exposed to, compared with about 15 percent in 1980.
With that radiation exposure comes increasing risk for cancer, but what worries me even more is that this ease of ordering a scan has caused doctors’ most basic skills in examining the body to atrophy. This loss is palpable when American medical trainees go to hospitals and clinics abroad with few resources: it can be quite humbling to see doctors in Africa and South America detect fluid around patients’ lungs not with X-rays but by percussing the chest with their fingers and listening with their stethoscopes.
Of course, we still teach medical students how to properly examine the body. In dedicated physical diagnosis courses in their first and second years, students learn on trained actors, who give them appropriate stories and responses, how to do a complete exam of the body’s systems (circulatory, respiratory, musculoskeletal and the rest). Faculty members stand by to assess that the required maneuvers are performed correctly.
But all that training can be undone the moment the students hit their clinical years. Then, they discover that the currency on the ward seems to be “throughput” — getting tests ordered and getting results, having procedures like colonoscopies done expeditiously, calling in specialists, arranging discharge. And the engine for all of that, indeed the place where the dialogue between doctors and nurses takes place, is the computer.
The consequence of losing both faith and skill in examining the body is that we miss simple things, and we order more tests and subject people to the dangers of radiation unnecessarily. Just a few weeks ago, I heard of a patient who arrived in an E.R. in extremis with seizures and breathing difficulties. After being stabilized and put on a breathing machine, she was taken for a CT scan of the chest, to rule out blood clots to the lung; but when the radiologist looked at the results, she turned out to have tumors in both breasts, along with the secondary spread of cancer all over the body.
In retrospect, though, her cancer should have been discovered long before the radiologist found it; before the emergency, the patient had been seen several times and at different places, for symptoms that were probably related to the cancer. I got to see the CT scan: the tumor masses in each breast were likely visible to the naked eye — and certainly to the hand. Yet they had never been noted.
Too frequently, I hear of (and in a study we are conducting, I am collecting) stories like that from all across the country. They represent a type of error that stems from not making use of basic bedside skills, not asking the patient to fully disrobe. It is a more subtle kind of error than operating on the wrong limb; indeed, this sort of mistake is not always recognized, and yet the consequences can be grave.
IN my experience, being skilled at examining the body has a salutary effect beyond finding important clues that lead to an early diagnosis. It is a ritual that remains important to the patient. Recently my ward team admitted an elderly woman who had been transferred from her nursing home in the night because of a change in her mental status. A CT of the head and all other tests were determined to be normal; the problem had been dehydration, and she was better, ready to go back. But as our team was about to enter the room, my intern warned me that the patient’s lawyer daughter was unhappy with the plan to return her mother to the nursing home, and was waiting impatiently to see me and contest the transfer.
After introducing myself to the patient and to her daughter, I did a thorough but quick neurologic exam. I put the patient through her paces: mental status, cranial nerves, motor and sensory function, used my reflex hammer and pointed out interesting things along the way to my interns and students. I then said to the daughter that her mother seemed back to normal. To our surprise, the daughter seemed comforted, and now had no objection to her mother’s return to the nursing home.
Later, our team discussed what had just happened. We all felt that the daughter witnessing the examination of the patient, that ritual, was the key to earning both their trusts. I find that patients from almost any culture have deep expectations of a ritual when a doctor sees them, and they are quick to perceive when he or she gives those procedures short shrift by, say, placing the stethoscope on top of the gown instead of the skin, doing a cursory prod of the belly and wrapping up in 30 seconds. Rituals are about transformation, the crossing of a threshold, and in the case of the bedside exam, the transformation is the cementing of the doctor-patient relationship, a way of saying: “I will see you through this illness. I will be with you through thick and thin.” It is paramount that doctors not forget the importance of this ritual.
An answer that might have been posed on “Jeopardy!” is, “An emergency treatment that is administered by ear.” I wonder if Watson would have known the question (though he will now, cybertroller that he is), which is, “What are words of comfort?”
Abraham Verghese, a professor at the Stanford University School of Medicine, is the author of the novel “Cutting for Stone.”
Friday, February 18, 2011
Emerging Semantic Web Technology Could Help Intelligence Analysts Spot New Terror Threats
In light of 9/11, the attempted Christmas Day bombing in 2009 and even last year’s WikiLeaks incident, it’s clear that the search and information-sharing process across government intelligence databases is flawed and missing an element that would potentially enable analysts to see threats and prevent future incidents.
Semantic technology is used increasingly to help organizations manage, integrate and gain intelligence from multiple streams of unstructured data and information. Semantic is unique in its ability to exceed the limits of other technologies and approach the automatic understanding of a text. While Semantic Web — a web of understood word meanings and connections — technology is quickly eclipsing first-generation, keyword-based index search systems and second-generation social media interaction, the transition is far from complete. Nowhere is this technology more useful than in the national intelligence space.
As a semantic technology professional, I think about how semantic technology could have aided in connecting the dots between the available information in the government intelligence community in the 2009 Christmas bomber case, and most recently, the highly publicized leak of classified government information about the war in Afghanistan.
As a former intelligence analyst, I know the frustration of lacking both complete information and computer systems capable of aiding the analysis process. Almost a decade after 9/11 and untold dollars later, the nation still struggles with effective intelligence sharing. An often mentioned issue is the lack of collaboration among intelligence teams on the analysis of incoming information from the multitude of existing databases.
The Los Angeles Times points to others:
“Lawmakers have been pushing for a capability to search across the government’s vast library of terrorism information, but intelligence officials say there are serious technical and policy hurdles. The databases are written in myriad computer languages; different legal standards are employed on how collected information can be used; and there is reluctance within some agencies to share data.”
The newspaper then makes the connection to 2009’s Christmas bomber threat:
“That makes it harder to connect disparate pieces of threat information, which is exactly what went wrong in the case of Umar Farouk Abdulmutallab, a Nigerian who on Christmas Day tried to blow up an airplane using explosives sewn into his underwear. The bomb failed to detonate, and a passenger jumped on him.”
Analysts must have a reason to collaborate. They must foresee or imagine how one or more evidence streams, often with many missing elements, overlap or fold into one another to form a complete picture. The reality is, even really good human analysts cannot juggle more than 50 to 60 data points — events, names, places, times, dates and the connections between them — at once.
But good technology that mimics the same approach has no such limitation. Allowing such a system to build the larger picture — to connect the dots — through trial and error, quickly and repeatedly with an analyst reviewing that picture for plausibility, internal consistency and impact, would be a more effective approach than adding a small army of new analysts to the problem.
A system that proactively and constantly builds and tests all the available evidence on a person, action, event, etc., is the current architecture of a Semantic Web. This approach is becoming prevalent in the private sector, and governments also are now taking advantage of the Semantic Web rather than a simple web of keywords.
To test this proposition, I used the timeline of known facts about the 2009 Christmas bomber as reported by The New York Times. Although this is a retrospective view, I wanted to know what I would have concluded over time, if I were an analyst and had good information sharing and robust analytical support, such as current Semantic Web technology can provide.
To begin, I took all the known facts and began to process them semantically. I used a semantic search and analysis system to analyze the content for people, places, things, facts, time and geography, but most importantly, for events. Such analysis answers: Who did what to whom when and where? Based on our established event timeline, in summer 2009, Abdulmutallab would have hit intelligence databases when Britain placed him on a watch/no-fly list after his student visa was rejected.
We can see right away that Abdulmutallab was known to have studied in 2004 and 2005 in Sanaa, Yemen; he has a direct connection to the radical Yemen cleric Anwar al-Awlaki; he’s loosely connected to al-Qaida because of his presence in Yemen; and he disappeared in September 2009. But the most important thread is that he was already on Britain’s watch/no entry list.
On the whole, perhaps this picture doesn’t portray a person who has planned a terrorist attack. But more connections come to light when we continue to build the picture of Abdulmutallab into fall 2009.
Through November 2009, several things become apparent. First, the number of connection points has risen significantly between Abdulmutallab, Yemen and al-Qaida relative to the previous summer. Second, the number of evidentiary warning signs around Abdulmutallab has grown to include his father, the United Nations and several U.S. agencies (e.g., the National Security Agency and National Counterterrorism Center). Third, there’s a lack of communication or information sharing among U.S. agencies.
Nonetheless, Abdulmutallab was placed on a terrorist watch list but not on the more restrictive no-fly list. This may not have been the case if analysts had a diagram that visualized the increased strength between him and al-Qaida, as well as the increase in additional connections of concern at this stage of this analysis.
In retrospect, we know that there was still more time. Adding the events from December 2009 in the examination makes the graph richer still:
Once again, as the connection between Abdulmutallab, Yemen and al-Qaida increases, more U.S. agencies take note, and now he has purchased airline tickets with a U.S. destination and didn’t check any baggage (a Transportation Security Administration warning signal since 9/11). As with most intelligence analysis, the strongest indicators come too late, so understanding how to fit them into the overall picture quickly is essential — in this case, the time it took Abdulmutallab to fly from Africa to the Netherlands and then to the United States. Semantic technology that can visualize the new input, can speed up analysts’ understanding.
Semantic Web technology can provide a window into how people, places, things and events come together into threats and opportunities. It’s impossible to expect analysts to manually “see” how anomalous and imperfect evidence streams fit together. And there is always more than one way that they fit together.
Let machines do what they’re good at. Namely when coupled with semantic understanding, measuring endless clues and hints, fitting, testing, removing and adding various puzzle pieces to see if the picture starts to make sense. Past a certain threshold, analysts can take over and do the work computers never will be able to do: apply human judgment and reasoning. Otherwise judgment and decision never arrive, connections are never made, and red flags are never raised.
The timeline of these past and recent events (9/11, the Christmas bomber and recent data leakage in Washington, D.C.) show a serious need to address the gaps in our country’s intelligence procedures and sharing processes. And this is where Semantic Web comes in.
Brooke Aker is CEO of Expert System USA. He writes and speaks on topics, such as competitive intelligence, knowledge management and predictive analytics.
You may use or reference this story with attribution and a link to
http://www.govtech.com/pcio/Semantic-Web-Could-Help-Spot-Terror-Threats-021111.html
Thursday, February 17, 2011
IBM Moving Watson Supercomputer Beyond 'Jeopardy' To Health-Care
By Shara Tibke, Dow Jones Newswires, Feb 16, 2011 10:28pm ET
After trouncing "Jeopardy!'s" best and brightest, International Business Machines Corp.'s (IBM) Watson supercomputer is on to a new challenge--health-care. IBM said it has reached a research agreement with Nuance Communications Inc. (NUAN), a provider of speech-recognition technology, to "explore, develop and commercialize" the Watson computing system's advanced analytics capabilities in the health-care industry. Columbia University Medical Center and the University of Maryland School of Medicine will be providing their medical expertise and research.
Watson, powered by 90 servers and 360 computer chips, was built over the past four years by a team of IBM researchers who set out to develop a machine that could quickly answer complex questions involving puns and wordplay.The room-sized system competed against former "Jeopardy!" champions Ken Jennings and Brad Rutter for three nights this week, finally winning the challenge Wednesday with a score of $77,147. Jennings finished with $24,000 and Rutter had $21,600. The victory nets Watson a total prize of $1 million, which IBM will be donating to charity. Jennings and Rutter get $300,000 and $200,000, respectively, with plans to donate half to charities.
After optimizing Watson for "Jeopardy!" play, IBM researchers are working to apply the system to business uses, such as helping physicians and nurses find answers within huge volumes of information. A doctor considering a patient's diagnosis could use Watson's analytics technology along with Nuance's voice and clinical language understanding offerings to rapidly consider all the related texts, reference materials, prior cases and latest knowledge in medical journals to gain information from more potential sources then previously possible, making the physician more confident in the patient's diagnosis, IBM said.
"Combining our analytics expertise with the experience and technology of Nuance, we can transform the way that health-care professionals accomplish everyday tasks by enabling them to work smarter and more efficiently," Dr. John E. Kelly III, senior vice president and director of IBM Research, said in a press release.
IBM and Nuance expect their first commercial offerings to be available in 18 to 24 months.
Katharine Frase, one of the IBM researchers working on business applications for Watson, said in an interview with Dow Jones Newswires that IBM is considering expanding Watson to other uses such as call centers, knowledge management and training of new employees in technical fields, financial sector applications and law--though she noted it is uncertain if there's a business model to support the law application.
"First we figure out the characteristics of uses that need the technology," Frase said. "Then we figure out a business model, how we deliver the service and who's the customer...Not every business problem needs Watson."
She said IBM has been fielding calls from clients, asking if Watson can help with their problems.IBM expects some customers will want to build versions of Watson behind their own firewalls, while others will want to access its capabilities as a cloud-delivered service. "There's a lot we don't know right now," Frase said. "We'll start with the medical realm and learn as we go along."
-By Shara Tibken, Dow Jones Newswires; 212-416-2189; shara.tibken@dowjones.com
Wednesday, February 16, 2011
Finance: Elusive information (FT today)
BY TOM BRAITHWAITE, FT, February 15 2011 22:25
It was Friday August 15 2008 and a senior official at the US Federal Reserve in Washington wrestled with a thorny problem: he wanted to know what was happening inside Lehman Brothers but was afraid to ask.
Pat Parkinson, now the Fed's top bank supervisor, was trying to find out which companies had derivatives contracts with Lehman as he gauged how severe the impact would be if the investment bank collapsed. But colleagues in New York told him that just requesting the data would be "a huge negative signal" for the bank's prospects and they were "very reluctant" to do anything that might "spook the market".
Lehman's implosion the following month was not the only recent instance where a calamitous lack of decent data has plagued financial markets. Others range from the European bank stress tests carried out last year, which officials admit relied on information "polluted by accounting", to the US stock market "flash crash" on May 6 that left the Securities and Exchange Commission floundering for answers.
But for the first time in decades there is a growing movement to rebuild the creaking data architecture that underpins modern finance.
Regulators' need to understand the crisis provided the impetus. When Lehman fell in September 2008, not only did institutions not know their rivals' exposure to Lehman, or to other problem areas such as subprime mortgages; they were sometimes unable to map their own with any speed. In the vortex, stock prices collapsed, liquidity dried up and investors and bankers ran scared.
"Everyone's going, 'what do I hold that is Lehman?'" says Mike Atkin, head of the Enterprise Data Management Council, a group of banks, information technology companies and regulators. "Wait a minute ... what is Lehman? Lehman isn't one entity – it's 10,000 entities. We don't know what our exposure is because we're not sure what Lehman is."
- Lowdown on the OFR ● The Office of Financial Research, set up by the DoddFrank financial reform act last year to improve the quality and analysis of US data, has wide-ranging powers enabling it to compel institutions to provide information. ● Lewis Alexander, interim head and former senior Citigroup economist, has "a few million" dollars from the Federal Reserve for staffing. By 2012 the OFR will be funded by a tax on big banks. ● Based in the Treasury, the OFR will have an independent chair who reports to Congress. The White House has approached candidates.
For years, individual statisticians, technology specialists and economists from regulators, financial institutions and academia had warned of the dangers. For years, they were dismissed as Jeremiahs and a root-and-branch reform of the data networks underpinning the financial system was rejected by the industry and regulators, which saw big costs and limited benefits.
John Geanakoplos, a Yale University professor, blames the Fed for not using data well, sometimes because of bureaucratic blockages, in one instance because officials balked at paying $400,000 for mortgage information, and sometimes because of a philosophical belief in self-correcting markets. In a recent presentation to the European Central Bank, he took aim at the "Greenspan-Bernanke doctrine" that "denied that there are bubbles, or that they could recognise one if they saw it".
In less damning terms, Keith Saxton, IBM's London-based global director of financial markets, points to the same problem. "Most of the data that the regulators and the central banks collect are what I call quite traditional," he says. "They have a view of 'The Bank' and everyone assumed that because that bank was healthy maybe there wasn't a problem with the system. It has turned out that the data they had about that bank weren't granular enough to be accurate: these guys can't get to the cash flow of an instrument."
But across the world, the evangelical geeks are gaining ground. In the US, Jack Reed, the Democratic senator from Rhode Island, took up the cause and managed to slip a new early-warning agency – the Office of Financial Research – into the mammoth Dodd-Frank financial reform bill that passed Congress last summer.
The OFR, which is being incubated in the Treasury before it gains independence, has begun work on standardising the components of every significant financial transaction. Eventually it will collect data, pull it all into a supercomputer and analyse the results, with the ultimate aim of spotting bubbles before they burst.
In a rare moment for Washington, an idea that was not proposed by the executive branch and without a big bloc of business support became law. A loose coalition of advocates managed to persuade one lawmaker, who sold the initiative to his colleagues in the face of scepticism from existing agencies. "Within the government there was a hesitancy to create an agency that was independent," says Mr Reed. The senator, an army veteran, says he wants the OFR to act like a "red team" – the military term for a group of soldiers charged with probing the weaknesses of their own comrades.
Words of change from previously sceptical bureaucrats are borne out by actions. The Fed does now buy the expensive granular mortgage data that Prof Geanakoplos highlighted. New rules will force more derivatives through clearing houses, allowing Mr Parkinson to grab data from fewer sources without alerting the market.
But the real data zealots think that regulators should go much further: the ultimate prize is a "dashboard" of the whole financial system. While anyone can view a snapshot of the stock market, the OFR and sister agencies in Europe and Asia would have the same visibility over darker parts of finance, allowing them to test various scenarios on Wall Street at the push of a button. The trouble is, at the moment, this is science fiction; it is impossible to create. "When something starts to go awry we go, 'what am I holding?' and I need to know it down to the loan level so I can run it against a scenario," says Mr Atkin.
"If Ford goes bankrupt what happens to the homes in Detroit? Well, I don't know how many homes in Detroit I've got in my mortgage-backed security because I can't unravel the bloody thing."
Francis Gross, head of external statistics at the Frankfurt-based ECB, points to the same issue: "The basic assumption is you have these vast pools of data that are quite homogeneous so that a spade is a spade – and that's where the problem comes."
The reason is that the building blocks of so-called "reference data" are not standard. As banks have grown by acquisition they have acquired thousands of legacy systems spread across different businesses – they do not have standard ways of recording data even within the group. So plotting relationships across the financial system is almost impossible. "The trading desk may book a transaction as Deutsche Bank and code that as 'DB'," says Lew Alexander, the Treasury official in charge of setting up the OFR. "When it gets to accounting, 'DB' may mean Dresdner Bank."
It is these non-standard "identifiers", for companies and for instruments, that Mr Gross and Mr Alexander are now trying to reconcile on both sides of the Atlantic. Unravelling garbled transactions costs the industry hundreds of millions of dollars a year in people and IT but during the boom years it always seemed too fiddly to fix.
One banker says: "We do a transaction and we get a lot of breaks and reconciliations ... Generally over 90 per cent of them are due to inconsistency of reference data, not due to misunderstanding between parties. If we all had the same reference data we would revolutionise how operations are done in our firms." He adds: "Logistically it's impossible to do unless it's imposed."
Mr Gross, who is leading calls for international standards of reference data, says regulators must be in the driving seat or it will be "ask[ing] cats to herd themselves".
The US now has the structure to start work, although even after Mr Reed managed to get the OFR through Congress, a handful of powerful Republicans continue to oppose it. Karl Rove, an adviser to former president George W. Bush, and Richard Shelby, the senior Republican on the Senate banking committee, both think it reeks of big government.
"I believe that the Democrats' new Office of Financial Research will not only fail to detect systemic threats and asset price bubbles in the future, it may threaten the civil liberties and privacy of Americans, waste billions of dollars of taxpayer resources and lull markets into the false belief that this new government power will protect the financial system from risk," says Mr Shelby.
Some Wall Street executives are worried about disclosing their trading positions to anyone, even regulators policing the system for systemic risk. Says the banker who has watched the OFR closely and supports it: "There is a concern about people giving up their positions ... There was a time in 1905 when people didn't report their income to the [Internal Revenue Service]. I think it will become: you do a transaction, you report it to the OFR."
From the other end of the political spectrum, liberal Democrats in Congress are worried the Treasury and the Fed are paying lip service to the OFR and will not give it the tools to be intrusive enough. They think scepticism among senior officials endures.
It is certainly a never-ending struggle. Forty years before Mr Parkinson grappled with Lehman, one of his antecedents extolled the virtues of technology for market supervision. In 1968, a year in which the New York Stock Exchange had to close for days at a time because paper records of trades were so out of hand, Manuel Cohen, chairman of the SEC, boasted that his agency was now using "its own computer" to monitor markets.
"We are able to provide a measure of protection to investors that theretofore had been virtually impossible due to budget and 'manpower' limitations," he said. "But our techniques in this area are not as fully developed as they will be."
..........................................
Information technology
How innovation has come to mean different things on different coasts.
In recent decades, a curious paradox has hung over the American economy. On the west coast, a gaggle of entrepreneurial companies, filled with some of the country's brightest brains, has been scrambling to track data in the smartest and most innovative way, writes Gillian Tett.
Companies such as Google, Amazon and Facebookare now able to monitor what consumers and businesses are doing around the world in real time. They can track everything from book purchases to friendship links and the consumption of breakfast cereal.
But on the east coast, another collection of highly talented brains has been delivering a very different form of innovation. Wall Street has produced a plethora of products and processes that has made the financial system more complex and (often) more opaque.
But while bankers have used cutting-edge computer technology to, say, develop ultra-fast automatic trading strategies, the data-handling innovations developed on the west coast have been slow to move east.
As recently as six years ago, traders in the credit default swaps market, for example, were still conducting deals by fax. Banks' back offices were not standardised and regulators could not collect data from them in anything resembling a timely manner.
Beyond banking, many other parts of the financial world went almost entirely untracked by regulators, who remain behind the technological curve.
The question that hangs over the Office of Financial Research, being set up as part of reforms to the sector, is whether these different west and east coast worlds can now meet – and apply Silicon Valley-style innovation to the financial system as a whole. Can the techniques that allow Facebook to aggregate data on online friends in a flash be used to track derivatives trades, say?
Optimists argue that the answer is yes, given the extraordinary strides in computing power that have already occurred. Officials linked to the OFR have started talking to companies such as IBMabout how to transplant innovations in the non-financial world into a coherent form of data collection in finance.
But pessimists retort that financial companies have little incentive to co-operate; after all, opacity has on the whole served Wall Street well, enabling traders to enjoy fat profits.
Either way, the really big question is whether the type of entrepreneurial, innovative drive that inspires Silicon Valley can be transplanted to the state sector.
"If you really wanted to revolutionise [data collection], you should ask somebody like Google to run it, and pay them properly," observes one senior banker, only partly in jest.
Right now, however, that prospect seems even harder to imagine than a world where the OFR starts to fly.
Copyright The Financial Times Limited 2011. Print a single copy of this article for personal use. Contact us if you wish to print more to distribu
Monday, February 14, 2011
Bringing Washington's technology into 21st century
http://www.cnn.com/2011/OPINION/02/14/kundra.white.house.tech/
STORY HIGHLIGHTS
· Vivek Kundra says improving government's information technology will help win future
· U.S. is changing how it manages its IT to increase transparency, accountability, he says
· IT Dashboard is new site where citizens can monitor every IT project in federal government
· Kundra: Innovations include new mobile apps, access to health records for veterans
Washington (CNN) -- Last month, in his State of the Union address, President Barack Obama talked about winning the future. One way in which we can help win the future is by closing the technology gap between the government and private sector -- and leveraging recent advances in information technology to serve the American people better.
Think about our everyday lives. You can launch your own website in seconds. A small business owner can manage payroll online. A grandmother can share pictures of her grandchildren with family across the world. But in the government, it can take years and cost millions of dollars to deploy technology.
It's not due to a lack of investment. In fact, the federal government is the largest purchaser of IT goods and services in the world. In 2010 alone, we spent nearly $80 billion on IT and have spent more than $600 billion over the past decade.
To get a better return on this investment for the American people, we have fundamentally altered the way we manage the federal government's IT projects -- using transparency to shed light on government operations and to hold government managers accountable for results.
We launched the IT Dashboard, a website where the American people can monitor every IT project in the federal government as easily as they can their personal investment portfolios. If a project is over budget, or behind schedule, the Dashboard tells you so.
We're now using the IT Dashboard to power in-depth accountability reviews to turn around, terminate or halt underperforming IT projects. So far, these reviews have resulted in overall budget reductions of more than $3 billion and cut the time for delivery of a needed functionality from two to three years down to an average of eight months. This means faster adoption of new biometric technologies for law enforcement investigations and accelerated access to cargo inspection systems for Border Control agents.
To crack down on redundant investment, we embarked upon the largest data center consolidation effort in history and will eliminate more than 800 federal government data centers by 2015.
But it is not enough to manage our existing technology investments effectively. Too often, federal agencies rely on custom and proprietary legacy technologies. To close the technology gap, we must embrace innovative technologies such as cloud computing, which provides access to shared computing resources much in the same way public utilities provide access to water and electricity, to enable better service at a lower cost.
This is why we instituted a "cloud first" policy that directs each federal agency to move three technology services (such as e-mail) to the cloud within the next 18 months. We're already seeing results. The Department of Agriculture is migrating 120,000 e-mail users across 5,000 locations to the cloud, saving $27 million over five years. Overall, based on our estimates, up to $20 billion of annual federal IT spending could potentially be migrated to cloud computing solutions.
Our ultimate goal in closing the IT gap is to improve the delivery of government services to the American people. We're already seeing how the effective use of innovative technologies can directly affect veterans, students and potential citizens:
• To make it easier for the public to access government services anywhere, anytime, we launched more than 100 mobile apps. They include an app that gives consumers instant information on safety recalls of products such as toys and food, and an app that provides travelers with around-the-clock access to wait times at airport security lines.
• To help veterans and Medicare beneficiaries, we created the "Blue Button" Personal Health Record. This new feature on the My HealtheVet and Mymedicare.gov websites allows users easily to access and download their personal health records anytime, anywhere. Having control of this information enables users to share this data with health care providers, caregivers and others they trust.
• To demystify the citizenship application process for potential citizens, we put online a case tracking system that provides prospective citizens a way to track the status of their immigration case just by entering a number, as they would a FedEx shipment.
These are but a few examples. More work lies ahead, but by embracing innovative technologies, eliminating redundant investment and intervening to turn around troubled projects, we can leverage the power of information technology to help win the future. You can find out more about our efforts to make technology work better for the American people in our IT reform plan.
The opinions expressed in this commentary are solely those of Vivek Kundra.
Editor's note: Vivek Kundra is the federal chief information officer at the White House. He will be speaking about the Obama administration's role in innovation and infrastructure at The Economist's Ideas Economy: Intelligent Infrastructure event in New York on Wednesday and Thursday.
--------------------------------------------------
Stefaan G. Verhulst
Chief of Research
Markle Foundation
10 Rockefeller Plaza, Floor 16
New York, NY 10020-1903
Tel. 212 713 7630
Cell 646 573 1361http://www.markle.org
STORY HIGHLIGHTS
· Vivek Kundra says improving government's information technology will help win future
· U.S. is changing how it manages its IT to increase transparency, accountability, he says
· IT Dashboard is new site where citizens can monitor every IT project in federal government
· Kundra: Innovations include new mobile apps, access to health records for veterans
Washington (CNN) -- Last month, in his State of the Union address, President Barack Obama talked about winning the future. One way in which we can help win the future is by closing the technology gap between the government and private sector -- and leveraging recent advances in information technology to serve the American people better.
Think about our everyday lives. You can launch your own website in seconds. A small business owner can manage payroll online. A grandmother can share pictures of her grandchildren with family across the world. But in the government, it can take years and cost millions of dollars to deploy technology.
It's not due to a lack of investment. In fact, the federal government is the largest purchaser of IT goods and services in the world. In 2010 alone, we spent nearly $80 billion on IT and have spent more than $600 billion over the past decade.
To get a better return on this investment for the American people, we have fundamentally altered the way we manage the federal government's IT projects -- using transparency to shed light on government operations and to hold government managers accountable for results.
We launched the IT Dashboard, a website where the American people can monitor every IT project in the federal government as easily as they can their personal investment portfolios. If a project is over budget, or behind schedule, the Dashboard tells you so.
We're now using the IT Dashboard to power in-depth accountability reviews to turn around, terminate or halt underperforming IT projects. So far, these reviews have resulted in overall budget reductions of more than $3 billion and cut the time for delivery of a needed functionality from two to three years down to an average of eight months. This means faster adoption of new biometric technologies for law enforcement investigations and accelerated access to cargo inspection systems for Border Control agents.
To crack down on redundant investment, we embarked upon the largest data center consolidation effort in history and will eliminate more than 800 federal government data centers by 2015.
But it is not enough to manage our existing technology investments effectively. Too often, federal agencies rely on custom and proprietary legacy technologies. To close the technology gap, we must embrace innovative technologies such as cloud computing, which provides access to shared computing resources much in the same way public utilities provide access to water and electricity, to enable better service at a lower cost.
This is why we instituted a "cloud first" policy that directs each federal agency to move three technology services (such as e-mail) to the cloud within the next 18 months. We're already seeing results. The Department of Agriculture is migrating 120,000 e-mail users across 5,000 locations to the cloud, saving $27 million over five years. Overall, based on our estimates, up to $20 billion of annual federal IT spending could potentially be migrated to cloud computing solutions.
Our ultimate goal in closing the IT gap is to improve the delivery of government services to the American people. We're already seeing how the effective use of innovative technologies can directly affect veterans, students and potential citizens:
• To make it easier for the public to access government services anywhere, anytime, we launched more than 100 mobile apps. They include an app that gives consumers instant information on safety recalls of products such as toys and food, and an app that provides travelers with around-the-clock access to wait times at airport security lines.
• To help veterans and Medicare beneficiaries, we created the "Blue Button" Personal Health Record. This new feature on the My HealtheVet and Mymedicare.gov websites allows users easily to access and download their personal health records anytime, anywhere. Having control of this information enables users to share this data with health care providers, caregivers and others they trust.
• To demystify the citizenship application process for potential citizens, we put online a case tracking system that provides prospective citizens a way to track the status of their immigration case just by entering a number, as they would a FedEx shipment.
These are but a few examples. More work lies ahead, but by embracing innovative technologies, eliminating redundant investment and intervening to turn around troubled projects, we can leverage the power of information technology to help win the future. You can find out more about our efforts to make technology work better for the American people in our IT reform plan.
The opinions expressed in this commentary are solely those of Vivek Kundra.
Editor's note: Vivek Kundra is the federal chief information officer at the White House. He will be speaking about the Obama administration's role in innovation and infrastructure at The Economist's Ideas Economy: Intelligent Infrastructure event in New York on Wednesday and Thursday.
--------------------------------------------------
Stefaan G. Verhulst
Chief of Research
Markle Foundation
10 Rockefeller Plaza, Floor 16
New York, NY 10020-1903
Tel. 212 713 7630
Cell 646 573 1361http://www.markle.org
Thursday, February 10, 2011
Getting More Value from Cell-Phone Data
Technologies for analyzing mundane smart-phone data trails could prove a boon to business, researchers say.
By Lauren Cox, Technology Review, February 10, 2011
A special program, which they'd agreed to install earlier in the conference, mined accelerometer data to tell when the phone had been moved, counted time spent on e-mail, noted how long they'd kept the phone unlocked, and where and when they connected to Wi-Fi. "We could point out 'you're not very active right now,' or 'you're not paying attention to this lecture,' or 'you're jet lagged," Alex "Sandy" Pentland, the professor of media arts and sciences at MIT who developed the program, told the audience.
In the view of researchers like Pentland, the proliferation of smart phones—and our attachment to them—presents a prime opportunity to measure unseen behaviors and social interactions with new algorithms that mine the value of this data. He calls this "reality mining," and says it can make companies more innovative and deliver new marketing insights.
Some initial applications of cell-phone data have already been commercialized. For example, Sense Networks, a startup cofounded by Pentland, uses the ever-growing streams of real-time location information from cell phones and navigation devices—combined with historical research on consumer behavior—to predict the purchasing intentions of individuals across cities. Data on locations visited can indicate whether a person is going out for drinks or spending the day car-shopping.
The Sense Networks algorithms also continually update their analysis of anonymous cell-phone data to predict for clients whether the foot traffic in their neighborhood is, say, currently made up of business travelers or young adults out on the town. It looks at what kinds of locations they are visiting—such as a conference hall, rock club, or a hotel—and what times they are visiting them.
But Pentland sees many more commercial possibilities for cell-phone data. In a 2009 campus experiment, Pentland and a grad student used text-messaging and call logs, e-mail activity, GPS, and Bluetooth data from student volunteers' cell phones to track informal social influence. Mining cell-phone data—and then surveying the students about what they were doing to create models that can help interpret future cell-phone data sets—allowed Pentland to track how students influenced each other's political opinions, eating habits, or even their illnesses.
Pentland is known for developing a gadget called "sociometer" in the late 1990s. That's a device worn around the neck that measures unspoken communication: a Bluetooth radio detects other nearby sociometers; infrared sensors detect face-to-face conversations; and a microphone picks up changes in intonation that can reveal who is the most influential speaker in a conversation.
In one experiment, sociometers revealed that gossip during coffee breaks at a call center for a major bank actually functioned as an exchange of informal tips about how to handle calls better. Pentland said changing the break schedule to optimize socializing made workers more productive and saved the call center $15 million a year.
Now "phones are sociometers, quite explicitly," said Pentland.
Nicholas Christakis, a Harvard sociologist and physician, says that cell phones are one of a few sources—others include e-mail and institutional and research databases—that can provide rich streams of data to uncover new social interactions that can be of value to business. "All of these projects are interested in using new tools to understand fundamental problems," Christakis said.
Of course, most data generated by cell phones is private. To develop more applications relevant to business, Pentland says, this issue will have to be addressed. Even in his small experiment at the World Economic Forum, individuals had to share their data for the program to make meaningful comparisons.
Pentland says he's working on a "trust framework for privacy" with the Berkman Center for Internet & Society at Harvard University to give individuals control over data while participating in new research.
The framework is still in development, but Pentland's goal is to create a tiered system where anyone who wanted to offer data—and perhaps in return use an application that leverages such data—could choose a level of privacy: a "bronze" tier would restrict data sent by the phone, and a "silver" or "gold" tier would send much more data.
Lauren Cox is a reporter for Technology Review.
By Lauren Cox, Technology Review, February 10, 2011
A special program, which they'd agreed to install earlier in the conference, mined accelerometer data to tell when the phone had been moved, counted time spent on e-mail, noted how long they'd kept the phone unlocked, and where and when they connected to Wi-Fi. "We could point out 'you're not very active right now,' or 'you're not paying attention to this lecture,' or 'you're jet lagged," Alex "Sandy" Pentland, the professor of media arts and sciences at MIT who developed the program, told the audience.
In the view of researchers like Pentland, the proliferation of smart phones—and our attachment to them—presents a prime opportunity to measure unseen behaviors and social interactions with new algorithms that mine the value of this data. He calls this "reality mining," and says it can make companies more innovative and deliver new marketing insights.
Some initial applications of cell-phone data have already been commercialized. For example, Sense Networks, a startup cofounded by Pentland, uses the ever-growing streams of real-time location information from cell phones and navigation devices—combined with historical research on consumer behavior—to predict the purchasing intentions of individuals across cities. Data on locations visited can indicate whether a person is going out for drinks or spending the day car-shopping.
The Sense Networks algorithms also continually update their analysis of anonymous cell-phone data to predict for clients whether the foot traffic in their neighborhood is, say, currently made up of business travelers or young adults out on the town. It looks at what kinds of locations they are visiting—such as a conference hall, rock club, or a hotel—and what times they are visiting them.
But Pentland sees many more commercial possibilities for cell-phone data. In a 2009 campus experiment, Pentland and a grad student used text-messaging and call logs, e-mail activity, GPS, and Bluetooth data from student volunteers' cell phones to track informal social influence. Mining cell-phone data—and then surveying the students about what they were doing to create models that can help interpret future cell-phone data sets—allowed Pentland to track how students influenced each other's political opinions, eating habits, or even their illnesses.
Pentland is known for developing a gadget called "sociometer" in the late 1990s. That's a device worn around the neck that measures unspoken communication: a Bluetooth radio detects other nearby sociometers; infrared sensors detect face-to-face conversations; and a microphone picks up changes in intonation that can reveal who is the most influential speaker in a conversation.
In one experiment, sociometers revealed that gossip during coffee breaks at a call center for a major bank actually functioned as an exchange of informal tips about how to handle calls better. Pentland said changing the break schedule to optimize socializing made workers more productive and saved the call center $15 million a year.
Now "phones are sociometers, quite explicitly," said Pentland.
Nicholas Christakis, a Harvard sociologist and physician, says that cell phones are one of a few sources—others include e-mail and institutional and research databases—that can provide rich streams of data to uncover new social interactions that can be of value to business. "All of these projects are interested in using new tools to understand fundamental problems," Christakis said.
Of course, most data generated by cell phones is private. To develop more applications relevant to business, Pentland says, this issue will have to be addressed. Even in his small experiment at the World Economic Forum, individuals had to share their data for the program to make meaningful comparisons.
Pentland says he's working on a "trust framework for privacy" with the Berkman Center for Internet & Society at Harvard University to give individuals control over data while participating in new research.
The framework is still in development, but Pentland's goal is to create a tiered system where anyone who wanted to offer data—and perhaps in return use an application that leverages such data—could choose a level of privacy: a "bronze" tier would restrict data sent by the phone, and a "silver" or "gold" tier would send much more data.
Lauren Cox is a reporter for Technology Review.
Friday, February 4, 2011
Big opportunities brewing in marketplace for Big Data | Data Explosion
February 03, 2011
The data marketplace, where users can hunt for specialized data, is becoming a lucrative market with growing opportunities, technologists stressed at a Silicon Valley technical conference Wednesday.
"Data is a $100 billion market worldwide," said Pete Forde, founder and chief technical officer at BuzzData, during a session at the O'Reilly Strata conference, which featured discussions on dealing with large data volumes and the concept of "Big Data." There are entrenched players, such as news tickers, as well as open source upstarts such as BuzzData, he said.
There is no universally accepted definition of what a data marketplace is, according to Forde. "There are actually so many different definitions that the only thing in common is that these are organizations that want to be the places you go to get the data you need," he said. But there is a data products value chain and opportunities for disruption and innovation, said co-presenter Peter Soderling, founder and CEO of Stratus Security, which offers its Stratus API management platform for Web applications.
Data sets are not all created equal, Forde stressed, and there are factors that need to be accounted for such as accuracy and freshness. "If you get past a certain point for a lot of data sets, the data is now disinformation," he said.
BuzzData, is developing a data collaboration hub, a service in which participants can get data and then discuss it. Collaboration services for private data sets would be offered via subscriptions. "Basically, our sort of [modus operandi] is to create an ecosystem of people that are excited about data but come with lots of different skills and opinions and ideas," Forde said.
Soderling cited the concept of data-as-a-service, listing data markets, vendor data stores and dataset downloads as examples. He also lauded REST-based APIs and their role in the data marketplace. "Finally, we have a way to integrate data and systems and services with each other that is not so complicated," he said.
Companies such as Infochimps and Microsoft already are involved in the data marketplace. And earlier in the day Microsoft's Zane Adams, general manager of the company's Windows Azure cloud platform, talked about the company's Windows Azure Marketplace DataMarket, Microsoft's data-as-a-service offering. "It's a one-stop shop for data," he said. The service has been in business for 90 days and thus far has accumulated 5,000 subscriptions and conducted 3 million transactions, he said.
Customers include companies such as Dun and Bradsteet, which offers up business information via the service. Microsoft is integrating its service with tools such as Excel, with Excel users able to import data from DataMarket. "By integrating it into the everyday tools we are seeing the usage go up," Adams said.
This article, "Big opportunities brewing in marketplace for Big Data," was originally published at InfoWorld.com.
[ Also on InfoWorld: Find out what you need to know about the big promise of Big Data and see why big data is expected to get even bigger in 2011. | Get smarter about how you handle the explosion of enterprise data with InfoWorld's Enterprise Data Explosion newsletter. | And discover the key technologies to speed archival storage and get quick data recovery in InfoWorld's Archiving Deep Dive PDF special report. ]
The data marketplace, where users can hunt for specialized data, is becoming a lucrative market with growing opportunities, technologists stressed at a Silicon Valley technical conference Wednesday.
"Data is a $100 billion market worldwide," said Pete Forde, founder and chief technical officer at BuzzData, during a session at the O'Reilly Strata conference, which featured discussions on dealing with large data volumes and the concept of "Big Data." There are entrenched players, such as news tickers, as well as open source upstarts such as BuzzData, he said.
There is no universally accepted definition of what a data marketplace is, according to Forde. "There are actually so many different definitions that the only thing in common is that these are organizations that want to be the places you go to get the data you need," he said. But there is a data products value chain and opportunities for disruption and innovation, said co-presenter Peter Soderling, founder and CEO of Stratus Security, which offers its Stratus API management platform for Web applications.
Data sets are not all created equal, Forde stressed, and there are factors that need to be accounted for such as accuracy and freshness. "If you get past a certain point for a lot of data sets, the data is now disinformation," he said.
BuzzData, is developing a data collaboration hub, a service in which participants can get data and then discuss it. Collaboration services for private data sets would be offered via subscriptions. "Basically, our sort of [modus operandi] is to create an ecosystem of people that are excited about data but come with lots of different skills and opinions and ideas," Forde said.
Soderling cited the concept of data-as-a-service, listing data markets, vendor data stores and dataset downloads as examples. He also lauded REST-based APIs and their role in the data marketplace. "Finally, we have a way to integrate data and systems and services with each other that is not so complicated," he said.
Companies such as Infochimps and Microsoft already are involved in the data marketplace. And earlier in the day Microsoft's Zane Adams, general manager of the company's Windows Azure cloud platform, talked about the company's Windows Azure Marketplace DataMarket, Microsoft's data-as-a-service offering. "It's a one-stop shop for data," he said. The service has been in business for 90 days and thus far has accumulated 5,000 subscriptions and conducted 3 million transactions, he said.
Customers include companies such as Dun and Bradsteet, which offers up business information via the service. Microsoft is integrating its service with tools such as Excel, with Excel users able to import data from DataMarket. "By integrating it into the everyday tools we are seeing the usage go up," Adams said.
This article, "Big opportunities brewing in marketplace for Big Data," was originally published at InfoWorld.com.
[ Also on InfoWorld: Find out what you need to know about the big promise of Big Data and see why big data is expected to get even bigger in 2011. | Get smarter about how you handle the explosion of enterprise data with InfoWorld's Enterprise Data Explosion newsletter. | And discover the key technologies to speed archival storage and get quick data recovery in InfoWorld's Archiving Deep Dive PDF special report. ]
Tuesday, February 1, 2011
Where Innovation Is Sorely Needed
The pervasiveness of data threatens to upend some business models and enhance others.
By Paul B. Carroll and Chunka Mui, Tuesday, February 1, 2011
Editor's note: Today we begin a new monthly topic in Business Impact at Technology Review:
Innovation Strategies. The world's most innovative companies are using technology to move faster and more decisively than their competitors. They are mining data on their operations and their customers, letting their employees do inexpensive experiments, and using business software to get sprawling organizations working together. We'll explore the industries that are most in need of fast innovation and examine new ways of doing business, with case studies and interviews from around the world.
Everyone recognizes that technology is destroying long-standing business models in news, music, and other media industries, but the next few years could also bring wracking changes in numerous other businesses, such as insurance, retail, cars, medicine, toys, and utilities.
The reason lies in the third wave of personal computing. The first, beginning in the late 1970s, gave us PCs. The second, the Internet revolution of the 1990s, hooked all those computers together. The third is letting us essentially carry the Internet with us, on smart phones, tablets, and other devices. Cameras and sensors are becoming cheap and ubiquitous. Every person and device will be able to talk to any other person or device, anytime and anywhere. And operating in this world of infinite connections will change almost everything for businesses.
Those accustomed to broadcasting their messages will have to get comfortable in the middle of a conversation where everyone is talking to everyone else, all at the same time. Businesses that act as middlemen will have to justify their value or get pushed aside. Businesses that depend on market ignorance will have to adjust to total transparency on price and quality. And that's just for starters.
For example, because sensors, cameras, and wireless connections will be active in cars or in the smart phones that drivers and passengers carry, auto insurance will increasingly be based on detailed real-time information, such as how many miles are actually driven, how fast the car is going, and whether the driver stops at stop signs and uses turn signals.
Insurers will need to respond with far more flexible and customized pricing than they offer now. Allstate, Progressive, and others are already offering "Pay-As-You-Drive" programs that offer lower rates to (presumably safer) drivers who allow them to monitor such information. Insurers may also communicate more with customers in an effort to reduce claims. For instance, they might warn that a customer's teenage son has deviated from the approved route from school and has seven friends in the car. Teens have accidents at about twice the rate of other drivers; any company that can prevent some of those accidents has the opportunity to lower individual premiums and capture much of the $20 billion in overall premiums that teens represent in the United States each year.
The story will be similar in other industries. For example:
• Retail stores. Physical stores will face increased price and quality pressure because of apps like Red Laser, which displays reviews for a product, and the prices it's selling for at neighboring stores and online, when the customer uses a smart phone to scan the bar code. Malls and physical retailers have long clung to the notion that once customers are in the store, they will want the immediate gratification of buying a product and taking it home. Yet with unlimited information about alternatives at their fingertips, more in-store shoppers might choose savings and free shipping from a cheaper supplier. It might not be long before location-based capabilities yield another level of price competition. For instance, Amazon could offer an additional 5 percent off to shoppers browsing its site from inside a Walmart store.
• Cars. There's a saying that car companies make cars while everyone else makes money (on financing, warranties, repairs, insurance, and so on). But car companies could move in on these money-making opportunities by capturing usage and diagnostic data in real time. GM, for example, is already offering discounted insurance to customers of its OnStar remote security system.
• Medicine. Health-care providers will have to switch from seeing patients episodically to seeing them, in essence, every moment of the day. Even now, implantable sensors for people with heart problems can send a steady stream of data through a wireless device to a doctor's office for evaluation. Over time, as sensors and wireless devices spread, doctors (or, more accurately, their computers) will start monitoring many patients for a whole array of health issues. The data will find its way into the public domain in some form, making it possible for patients to know which doctors are especially effective and creating new types of competition.
• Utilities. Utilities, which have barely innovated for decades, are now adding sensors throughout the electric grid and putting "smart" meters in homes and businesses to manage the grid more efficiently and get a better sense of demand. They will have to be able to vary the retail price of electricity in real time and relay that information to consumers and businesses instantly, so they can adjust their usage to limit demand when prices are high. Some utilities will handle the transition well, but many will not.
• Toys. Kids are migrating to higher-technology content earlier. That may be bad news for companies that sell dolls and blocks, but it's good for those that provide entertainment on smart phones and tablets. Already, the social aspects of such devices are creating opportunities for innovators to reinvent toys and games. For example, many people now use phones to play a game of Scrabble over the course of several days, making a move whenever they have time.
The companies that operate in this new world had better be smart. Tomorrow, we'll lay out key principles on what it takes.
Paul B. Carroll and Chunka Mui are cofounders and managing directors of Devil's Advocate Group, a consultancy that helps businesses test their innovation strategies. They are also coauthors of Billion-Dollar Lessons: What You Can Learn From the Most Inexcusable Business Failures of the Last 25 Years.
By Paul B. Carroll and Chunka Mui, Tuesday, February 1, 2011
Editor's note: Today we begin a new monthly topic in Business Impact at Technology Review:
Innovation Strategies. The world's most innovative companies are using technology to move faster and more decisively than their competitors. They are mining data on their operations and their customers, letting their employees do inexpensive experiments, and using business software to get sprawling organizations working together. We'll explore the industries that are most in need of fast innovation and examine new ways of doing business, with case studies and interviews from around the world.
Everyone recognizes that technology is destroying long-standing business models in news, music, and other media industries, but the next few years could also bring wracking changes in numerous other businesses, such as insurance, retail, cars, medicine, toys, and utilities.
The reason lies in the third wave of personal computing. The first, beginning in the late 1970s, gave us PCs. The second, the Internet revolution of the 1990s, hooked all those computers together. The third is letting us essentially carry the Internet with us, on smart phones, tablets, and other devices. Cameras and sensors are becoming cheap and ubiquitous. Every person and device will be able to talk to any other person or device, anytime and anywhere. And operating in this world of infinite connections will change almost everything for businesses.
Those accustomed to broadcasting their messages will have to get comfortable in the middle of a conversation where everyone is talking to everyone else, all at the same time. Businesses that act as middlemen will have to justify their value or get pushed aside. Businesses that depend on market ignorance will have to adjust to total transparency on price and quality. And that's just for starters.
For example, because sensors, cameras, and wireless connections will be active in cars or in the smart phones that drivers and passengers carry, auto insurance will increasingly be based on detailed real-time information, such as how many miles are actually driven, how fast the car is going, and whether the driver stops at stop signs and uses turn signals.
Insurers will need to respond with far more flexible and customized pricing than they offer now. Allstate, Progressive, and others are already offering "Pay-As-You-Drive" programs that offer lower rates to (presumably safer) drivers who allow them to monitor such information. Insurers may also communicate more with customers in an effort to reduce claims. For instance, they might warn that a customer's teenage son has deviated from the approved route from school and has seven friends in the car. Teens have accidents at about twice the rate of other drivers; any company that can prevent some of those accidents has the opportunity to lower individual premiums and capture much of the $20 billion in overall premiums that teens represent in the United States each year.
The story will be similar in other industries. For example:
• Retail stores. Physical stores will face increased price and quality pressure because of apps like Red Laser, which displays reviews for a product, and the prices it's selling for at neighboring stores and online, when the customer uses a smart phone to scan the bar code. Malls and physical retailers have long clung to the notion that once customers are in the store, they will want the immediate gratification of buying a product and taking it home. Yet with unlimited information about alternatives at their fingertips, more in-store shoppers might choose savings and free shipping from a cheaper supplier. It might not be long before location-based capabilities yield another level of price competition. For instance, Amazon could offer an additional 5 percent off to shoppers browsing its site from inside a Walmart store.
• Cars. There's a saying that car companies make cars while everyone else makes money (on financing, warranties, repairs, insurance, and so on). But car companies could move in on these money-making opportunities by capturing usage and diagnostic data in real time. GM, for example, is already offering discounted insurance to customers of its OnStar remote security system.
• Medicine. Health-care providers will have to switch from seeing patients episodically to seeing them, in essence, every moment of the day. Even now, implantable sensors for people with heart problems can send a steady stream of data through a wireless device to a doctor's office for evaluation. Over time, as sensors and wireless devices spread, doctors (or, more accurately, their computers) will start monitoring many patients for a whole array of health issues. The data will find its way into the public domain in some form, making it possible for patients to know which doctors are especially effective and creating new types of competition.
• Utilities. Utilities, which have barely innovated for decades, are now adding sensors throughout the electric grid and putting "smart" meters in homes and businesses to manage the grid more efficiently and get a better sense of demand. They will have to be able to vary the retail price of electricity in real time and relay that information to consumers and businesses instantly, so they can adjust their usage to limit demand when prices are high. Some utilities will handle the transition well, but many will not.
• Toys. Kids are migrating to higher-technology content earlier. That may be bad news for companies that sell dolls and blocks, but it's good for those that provide entertainment on smart phones and tablets. Already, the social aspects of such devices are creating opportunities for innovators to reinvent toys and games. For example, many people now use phones to play a game of Scrabble over the course of several days, making a move whenever they have time.
The companies that operate in this new world had better be smart. Tomorrow, we'll lay out key principles on what it takes.
Paul B. Carroll and Chunka Mui are cofounders and managing directors of Devil's Advocate Group, a consultancy that helps businesses test their innovation strategies. They are also coauthors of Billion-Dollar Lessons: What You Can Learn From the Most Inexcusable Business Failures of the Last 25 Years.
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