Basit Chaudhry, MD, PhD

Medical Scientist, IBM Research

The U.S. healthcare system is capable of producing breathtaking innovations that drive progress forward.  New frontiers open up on an almost regular basis. This is the “miracle of medicine.” At the same time, however, advancements made at the leading edge of science are slow to diffuse through the system and enormous inefficiencies exist in how scarce resources are used. Our ability to generate new scientific knowledge and develop advanced medical technologies has never been greater. Our ability to apply those innovations rationally in practice has not kept pace, unfortunately.

One of the major reasons for this disconnect has been the limited integration of data into the care delivery enterprise. Too often clinical decisions need to be made based on intuition and opinion alone, leading to significant variations in care and waste. Medical knowledge continues to expand at dizzying rates that push practicing state of the art medicine beyond the cognitive capacity of any individual. The absence of data and knowledge at the point of care helps create a gulf in quality between the science of medicine and it’s application in practice.

To address these issues, the Robert  H. Smith School of Business at the University of Maryland and IBM organized a workshop of thought leaders to address how data analytics can be harnessed to transform healthcare. The purpose of the workshop was to explore how quantitative approaches pioneered in other fields such as operations management, statistics, economics and information science can be leveraged to help create new models for service delivery in healthcare.

While the problems of ever growing costs and variable quality in healthcare delivery are daunting. The good news is that pathways for improvement are emerging. Historically, quantitative data has been difficult to access due to low levels of information technology adoption in clinical care. This is changing rapidly. Over 40% of physician offices and 80% of hospitals have now adopted electronic health records. Large scale clinical and financial data assets are emerging as never before, opening up the possibility for transforming healthcare like industries such as retail and manufacturing have already done.

Digitizing medical records isn’t enough however. For industry transformation to occur, that data needs to be analyzed and the results of that analysis integrated into the care delivery process; knowledge needs to diffuse into clinical workflows and back to patients in a continuous process.  In this way, new insights into how best to deliver care are generated, shared and amplified on an ongoing basis that will make care more convenient, higher value and ultimately more humane.

Click here to view the embedded video.

Fashioning these new data driven approaches to care delivery will be challenging however given how clinical practice has traditionally been organized. The University of Maryland-IBM workshop explored how advanced data analytics can be applied to healthcare to spur innovation in service deliver at the point of care. An example of this kind of breakthrough is IBM Watson for Healthcare. This technology can analyze massive stores of structured and unstructured medical data and provide answers to clinical questions posed in natural human language.

Rising costs and deficits in quality have made transforming the healthcare system a national priority. While the problems are significant, promising solutions exist and are already being put into practice around the country. Innovation is needed but at the same time we don’t have to reinvent the proverbial wheel. Learning from how other fields and other industries can provide a vital map for changing the value proposition that the healthcare system offers the country.

Drawing insight from data has been fundamental to the advancement of industries around the globe, creating vibrant change such as increasing productivity and improving customer service. Similarly, large scale innovation is possible in how we deliver and pay for healthcare. Analytics will be vital to this transformation as well. The lessons are there. We now have the tools to learn in healthcare as well.

By Paulo Albuquerque, Asst. Professor of Marketing, University of Rochester, Simon Graduate School of Business

Give a college student a question and you’ll get an answer. Give them an answer, and you’ll get a lot of questions. The right questions can trigger responses that represent an entirely new way to look at solutions to today’s most pressing societal and business challenges.

This is exactly what happened at the Simon Graduate School of Business where I teach business students to take an analytical approach to marketing. Instead of the usual case competition where students are asked to develop a strategy to address a specific business challenge, this time the university collaborated with IBM and regional business leaders to look at ways IBM’s Watson technology could be applied to a variety of industries.

Entering a new era of computing, Watson represents a way to pique the interest of students in areas such as analytics and cognitive computing. The Watson system has the extraordinary ability to learn and become more accurate over time.

At the Simon Graduate School of Business, we aim to familiarize students with new technologies and provide students with the chance to take a hands-on approach looking at how technology could be used in business. This required students to think critically about combining technology tools with fundamental business practices to uncover new solutions and market opportunities.

Specifically, over the course of their studies, students at the Simon School master the FACt approach (Frame, Analyze, Communicate) to handle unstructured problems: they first frame the issue in the context of economics, analyze each dimension of the problem, and then come to a decision that must be communicated throughout the organization. With the help of IBM, the case competition brought the learning experience one step further. The students were asked to look at industries that the Watson technology could be used to manage information from a variety of sources to create long-lasting value to all business stakeholders.

Marketing has always required a combination of access to data, along with the smarts to know which data to use and when. What’s different today is the massive amount of data — called Big Data — that’s flowing at incredible speeds online and through mobile devices. Consumers are making their presence known in new ways on Facebook and Twitter, and on thousands of new channels cropping up inside mobile marketplaces. For today’s students, navigating all these social media channels is part of their DNA. It’s natural for students to poll Facebook friends for a restaurant recommendation, check in on Foursquare once they get there and then post a Yelp review when they get home that night. But how can they use these channels to solve real-world business problems and create new market opportunities?

What we witnessed during the case competition was a remarkable process among students. They started with open ended questions and began to apply the Watson technology in entirely new ways. Beyond IBM’s current work applying Watson in healthcare and financial services, students researched how Watson could be used to make more accurate weather predictions and help organizations allocate resources during times of disaster and crisis, taking the aggravation out of travel by reducing wait times in airports, or make mining for natural resources such as oil, gas and minerals safer and more effective.

We had 25 students participate in the Watson case competition, each with their own ideas. All were good, and some were excellent. You can read about the three standout ideas here (link to press release). The competition has inspired students to take a more creative approach to business and societal challenges. It’s a new way of thinking, innovating, and creating new business opportunities that our future leaders must be prepared to deliver by combining business acumen and technical savvy.

Click here to view the embedded video.

Technorati Tags: Analytics, Big Data, ibm watson, Smarter Education

Want proof? Two teams made up entirely of masters-of-marketing candidates placed first and second in last week’s Watson competition at the University of Rochester’s Simon School of Business. They beat five teams made up of traditional MBA candidates.

Their apparent edge: Simon School’s marketing program concentrates on quantitative analysis—the art of turning data into valuable insights. “We didn’t have a lot of background information, so we had to find a lot of data,” says Christian Beck, a 25-year-old from Hannover, Germany, who was on the winning team. “This reinforces my belief in the power of data.”

The seven teams spent two weeks preparing for the competition. Their task was to choose applications within specific industries that they believe will be fertile ground for IBM’s Watson, which last year defeated two former grand-champions at the TV quiz show Jeopardy! After that, they presented their proposals before a panel of judges including Simon School faculty members, a Rochester-area business CEO and two IBM executives. It was the first of a series of such competitions, which are aimed at getting top business students excited about the potential for data analytics.

Click here to view the embedded video.

The winners in the Simon School's Watson competition

In the past, in many business schools, marketing didn’t get the same level of respect as some other fields, such as finance. But the wind is shifting. That’s because, increasingly, marketing executives are in a position to dramatically impact the success of their companies.

These days, the combination of social networking and powerful new data analytics tools makes it possible for companies to know their customers and potential customers better than ever before. Increasingly, they can target customers with offers of products and services that will appeal to them via the marketing channels they’re most comfortable with. Thanks to data analytics and social networking, we’re approaching the point where we can market to individuals rather than segments.

This is nothing less than a revolution in the way marketing is done.

Students on the two top teams in Rochester get this, and they’re excited about their career prospects. Shan Huang, a 23-year old from southern China, says, “Any kind of market decision we make in the future will be a process of combining qualitative thinking and quantitative information. If we have the right data, we’ll be able to come up with good solutions.”

Huang’s team, made up of our Chinese women, initially targeted retailing and tourism, two industries they knew something about. But then they realized they needed to start not with what they knew but with Watson’s capabilities—and then map them to an industry where they would be extremely valuable. In the end, they chose mining. They developed a scenario for managers at mines or oil drilling rigs using Watson to determine where and how to search for minerals or oil and also to help them respond quickly to changes as they occur.

Beck’s team, which included people from Pakistan, Spain and the United States, focused on developing a crisis-management application.

The competition got the students thinking about all sorts of ways that Watson could be used in the future, some serious, an some not. “We joked that in a few years, every individual will have access to Watson. When Christmas is coming, you can ask Watson for gift-giving advice,” Beck says.

That may seem silly now, but the fact is that at some point individuals will have  Watson-like intelligence at their fingertips. For now, though, the Watson development team, and the students participating in the Watson competition series, are focusing on new capabilities that can help transform industries.

Technorati Tags: IBM, Simon School of Business, University of Rochester

Four years after the worldwide financial crisis began, the recent elections and their aftermaths in Greece and France are fresh reminders that the global financial system and the global economy itself are still fragile.  They also teach a powerful lesson about the importance of managing risk.

For many years, investors and regulators viewed government bonds as practically risk-free investments. We now understand how wrong a lot of smart people can be.

Risk exists in all the domains of human endeavor, and, as the financial crisis illustrates, it’s vital for people and organizations to adopt strategies for either reducing risks or understanding them better. This goes for governments, banks, investors and other business leaders alike

In this world of ever-more-complex systems, what is needed is the ability to go beyond the known and explore the unknown. By using technology it’s possible to adopt a holistic view of systems, everything from banking to maritime shipping to retail supply chains, and from that information create realistic scenarios of possible future outcomes of the decisions we make individually and collectively. These scenarios, or models, are the language of risk.

This approach is necessary because it’s often difficult to identify all of the significant risks in a complex system. The financial crisis first shattered our beliefs about supposed low-risk counterparties. It also proved us wrong on “safe” AAA-rated securitized assets. Then we learned that funding would not always be available even for well-regarded financial institutions. Now we face the sovereign debt debacle.

All enterprises face hidden risks. One of the primary reasons for this is they often assess risks business line by business line or function by function. Instead, they should form an integrated view of the entire enterprise, including all assets and liabilities, business strategies going forward and anticipated future investments.

This week at the IBM Vision conference, we will exploring the new technologies that are helping to address risk and solve critical problems that chief financial officers face every day. For example, businesses and government agencies can use analytics software that makes it possible for them to spot fraudulent activity in real time, so they can take action before funds are paid out to crooks.

But, unless it’s managed wisely, technology can  cause as many problems as it solves.  Too often,  business units and functional organizations within an enterprise use different technology tools for assessing risks. That makes it difficult to gain a consistent view of the risk portfolio of the entire firm. The same risk scenario may be interpreted very differently in various parts of the business. A unified approach ensures consistency and gives us confidence that our scenarios are applied in the same way across the board.

Insights are not useful if they don’t lead to action. A unified approach to risk management needs to be complemented with the capabilities to drive action or prepare an action plan.

As the financial crisis has shown, getting enterprise risk right can make all the difference between success and failure for companies–and even countries.

Technorati Tags: IBM, Michael Zerbs

When I was a child, my father worked at General Electric’s research lab in Niskayuna, N.Y. I would visit and watch him tinker with vacuum tubes—light bulb-like devices that were used to direct electrical current in all sorts of gizmos, from radios and TVs to radar and computers. At the time, I didn’t fully understand what he was doing, but those visits inspired me to study science and, ultimately, to get degrees in physics and materials engineering.

I later came to understand that I had witnessed one of the great transitions in the history of technology. While my dad was showing me vacuum tubes, other engineers at GE’s lab were experimenting with the vacuum tube’s successor, the transistor, which ultimately ushered in modern electronics and personal computing. Those core technologies enabled computers that could be programmed to perform a wide variety of tasks.

Today, we are at the dawn of another epochal shift in the evolution of technology. At IBM Research, we call it the era of cognitive systems.

This is a big deal. The changes that are coming over the next 10 to 20 years—building on IBM’s Watson technology–will transform the way we live, work and learn, just as programmable computing has transformed the human landscape over the past 60+ years. You could even call this the post-computing era.

Notice, I don’t use the term “thinking machines.” That’s because I don’t want to suggest that cognitive systems will think like humans do. Rather, they will help us think and make better decisions.

How do we define the era of cognitive systems? It helps to compare it to what came before. The tabulating era began in the 19^th century and continued until the 1940s. Those mechanical devices were used to organize data and make calculations that were useful in everything from conducting a national population census to tracking the performance of a company’s sales force. The programmable computing era emerged in the 1940s when scientists built the first electronic programmable computers. Successive generations of computing technology enabled everything from space exploration to the Internet.

Cognitive systems are fundamentally different. Traditional computers, which are still based on the blueprint that mathematician John von Neumann laid out in the 1940s, are programmed by humans to perform specific tasks. Cognitive  systems are capable of learning from their interactions with data and humans—essentially continuously reprogramming themselves. Traditional computers are designed to calculate rapidly. Cognitive systems are built to analyze information and draw insights from it. Traditional computers are organized around microprocessors. With cognitive systems, it’s about the data and drawing insights from it through analytics.

Because of these changes, the machines of the future will do much more than compute. They will be able to sense, learn and better predict the consequences of actions. In the years ahead, machines will cull insights from the vast amounts of information being gathered to help us learn how the world really works, and make sense of all of that complexity, and provide trusted advice to humans—whether heads of state or individuals trying to manage their careers or finances. Computing intelligence will become ubiquitous and pervasive. Increasingly, computers will offer advice, rather than waiting for commands.

This new era of technologies is essential to fulfilling IBM’s goal of using technology to help create a smarter planet—to make the world work better.

We need cognitive systems because recent developments in business, society and technology require new capabilities. The emergence of social networking, sensor networks and huge storehouses of business information create a seeming overabundance of information that some call Big Data. Systems are being asked to find patterns and draw conclusions, often in near real time, from huge quantities of information—and in situations where precise answers are hard to find. At the same time, in fields ranging from retailing to healthcare to government, the individual increasingly stands at the center. People are newly empowered with information about how the world works and able to express themselves in powerful new ways. They’re also becoming increasingly decipherable via the cloud of data that surrounds them and the digital exhaust they leave behind wherever they go. Through data analytics, we can know each other much better.

In addition, we need cognitive systems because some of the fundamental building blocks of traditional computing are crumbling. For instance, consider what’s happening with silicon-based integrated circuits. Today’s microchips are the electronic brains in everything from refrigerators to space ships. However, because of the laws of physics, we are no longer achieving improvements in the performance of microchips that we are accustomed to, and need, using traditional methods. We need to invent new materials and new chip architectures.

As I mentioned before, Watson represent the beginning of the great shift. The system was built by scientists using Von Neuman-based computing hardware—a cluster of 90 servers with a total of 2,880 processors. But Watson’s DeepQA software represents the paradigm shift. It uses a combination of natural language processing and machine learning to understand questions, search for answers in huge databases, and present possible answers rated by its confidence in their accuracy. Watson learns from experience.

Created as a so-called grand challenge by IBM Research, Watson caused a stir last year when it defeated two past grand-champions at the TV quiz show, Jeopardy! Now, IBM scientists are working with experts in healthcare, financial services, government and other domains to create versions of Watson tailored to their needs.

IBM embarks on grand challenges like Watson in efforts to reach ambitious goals with five- to 10-year time horizons. The challenges require significant breakthroughs and, if successful, would represent a revolution in the way we work, create and live our lives.

IBM has long been at the forefront of major advances in technology. IBM was a leader in tabulating machines when four predecessor companies combined in 1911. In 1944, IBM engineers worked with scientists at Harvard University to build an early electromechanical computer, Harvard Mark I, the first machine that could execute long computations automatically. It was the dawn of the computing era. Since then, IBM has been in the forefront of one important computing advance after another.

Just because we’re moving to a new era doesn’t mean the old one is going away.  Far from it.  Programmable systems will be around for years to come.  But the center of innovation is beginning to move to cognitive systems.

We’re on the leading edge of a technology transformation that promises to utterly transform business and society once again. But even though IBM has a broad portfolio of technologies and expertise, no single company can handle this sort of thing alone. We look to our clients, university researchers, students, government policy makers, industry partners and entrepreneurs to take this journey with us. Welcome, all, to the era of cognitive systems.

Technorati Tags: IBM, John Kelly

Researchers at The State University of New York at Buffalo are using a new approach to computing  in an attempt to identify the causes and promising therapies. We’re conducting a TweetChat on Thursday, May 10, from 12 to 1 p.m. ET at  Twitter hashtag #IBMDataChat. Please join the conversation about using technology to help defeat MS.

Participants:
·    Shawn Dolley, IBM VP of Big Data Healthcare & Life Sciences (Moderator), @shawndolley
·    Dr. Murali Ramanathan, SUNY Buffalo Professor Pharmaceutical Sciences and Neurology, Director of Graduate Studies & Co-Director, Data Intensive Discovery Initiative, @M_Ramanathan
·    David Smith, Revolution Analytics R Evangelist & VP of Marketing, @revodavid
·    Tim Coetzee, National MS Society Chief Research Officer, @tim_coetzee

Technorati Tags: IBM, MS, Multiple Sclerosis, The State University of New York at Buffalo

Dimitri Kanevsky demonstrates an Internet-based system for capturing real-time transcripts of teleconferences.

Kanevsky, a member of the speech and language algorithms department at IBM Research, has invented a long string of hearing- and speech-related  technologies. They include  a system for helping people improve the effectiveness of lip-reading, a method that enables deaf people to converse on the telephone and an Internet-based system for capturing real-time transcripts of phone conferences. “I like to solve challenging problems, and I get a thrill from creating novel math concepts and making discoveries,” he says.

Today, Kanevsky will get another kind of thrill–when he’s honored with a Champion of Change award at the White House. The award recognizes individuals who make a positive impact on science, technology, engineering and math for people with disabilities.  Here’s a livestream video link for the event.

While Kanevsky has a long record of achievements as an inventor, including 152 US patents, it’s clear from talking to him that some of his most important inventions may come in the future.

One area where his work could have a significant impact is in turning IBM’s Watson computer into a conversationalist. The brainy computer, which gained fame last year by beating two past-grand-champions on the TV quiz show Jeopardy!, translates text into speech, but, so far, doesn’t understand speech. Potential users of the technology say they would like to be able to converse with the machine in their work settings.  Physicians, for instance, envision using IBM Watson as a well-informed adviser when they’re examining and talking to patients.

Kanevsky can help out. He and colleagues at IBM Research have contributed notable improvements to the Baum-Welch algorithm, which is used to make up for shortages of training data in speech-recognition systems. These days, they’re developing new methods for producing more accurate results in situations where people speak with heavy accents or use jargon, or where there’s a lot of background noise.  These so-called discriminative algorithms could make possible real-time speech recognition systems that are fully automated and extremely accurate.

Watson is a first step in the emergence of a new era in information technology–which IBM calls the Era of Cognitive Systems. Big shifts are coming in chips, systems, data and programming. Kanevsky believes that these developments offer the promise of major breakthroughs in accessibility technologies–but only if those who architect them take people with disabilities into account from the start. “If this is done in the right way, it will have a tremendous impact on all people with disabilities,” he says.

One of Kanevsky’s earlier inventions, Artificial Passenger, is a system for keeping sleepy drivers awake by telling them stories and jokes, asking questions, and suggesting that the driver take a rest break if it detects that she or he is sleepy. Kanevsky came up with the idea after keeping his wife, Galina, awake by talking to her on a late night drive.

Watson’s a whole lot smarter than Artificial Passenger. For example, Watson can digest the universe of published information about a particular topic and then answer open-ended questions about it. So adding conversational capability to Watson would take speech recognition to a whole new level. Systems can be created that understand both speech and meaning. That’s a worthy challenge for an ambitious serial inventor.

Technorati Tags: Champion of Change, Dimitri Kanevsky, IBM, Watson, white house

Multiple sclerosis is a cruel disease. It typically strikes young adults. The body’s own immune system attacks the brain and spinal cord, resulting in physical disabilities, cognitive problems and a host of other chronic symptoms. The cause isn’t known. There is no cure.

Fortunately, the amount of biomedical and clinical data related to MS has exploded over the past decade, and, at the same time, new research methods make it possible to assess environmental factors and hundreds of thousands of genetic variations taken from single samples.

Researchers at The State University of New York at Buffalo are using a new approach to computing  in an attempt to identify the causes and promising therapies. “The eventual goal is to help develop a cure or prevention for MS,” says Dr. Murali Ramanathan, a professor of pharmaceutical sciences and neurology at SUNY Buffalo. “The ability to do this kind of computational analysis is a great complement to basic science and clinical research.”

Ramanathan is one of two researchers leading the Data Intensive Discovery Initiative, or DI2, which is focused on developing new algorithms and modeling techniques for analyzing genetic and environmental factors in multiple sclerosis. They also train graduate students.

He and his colleagues believe that MS is caused by a complex combination of gene-to-gene and gene-to-environment interactions. The chief environmental factors include living far from the equator, viral infections and cigarette smoking. They evaluate a wide range of factors, including gender, geography, ethnicity, diet, exercise, sun exposure and living and working conditions. The clinical data include medical records, lab results, MRI scans and patient surveys.

The researchers are using a computer cluster from IBM’s Netezza that combines processing, a database, storage and analytics into a single system, or appliance. This data-intensive architecture makes it possible to handle large amounts of data and derive insights quickly. The Buffalo researchers have found that analyses that once took days can now be completed in mere minutes.

Previously, Ramanathan used conventional parallel processing on a cluster of computer servers to crunch his data. Those systems divide large problems into smaller ones and solve them concurrently using hundreds or thousands of processors. The Netezza system also takes advantage of parallel processing. But, in addition, it uses specialized processor chips to filter the data that’s sitting in on storage disks before passing along only the relevant pieces to the main processors. At the same time, the system performs some of the analysis as the data is moving off the disks, rather than handling all of it on the main processors. So the design, essentially, moves a lot of the processing to the data rather than moving so much data to the microprocessors. As a result, the work can be done faster and more efficiently.

It took Ramanathan months to warm up to this approach to number crunching, but now he’s a huge fan. “I believe that the vast majority of problems in clinical and biomedical research will be addressed through this new way of computing,” he says.

Technorati Tags: IBM, Murali Ramanathan, SUNY Buffalo

by Yuchun Lee, Vice President and General Manager, IBM Enterprise Marketing Management Group 

Chief marketing officers (CMO) are under the gun. Exhibit A: Customers are more empowered and fueling an era where smartphones and tablets have replaced PCs. Exhibit B:  Social networks have usurped ads and Sunday fliers as the resource for brand information. Exhibit C: Consumers are learning about brands quickly, forming opinions even faster and reacting before you’ve even finished your morning coffee. For CMOs, even the smallest blind spot or misstep could spark a behavioral change capable of fracturing the customer relationship.

This is why I’m calling for CMOs to take off their suit coats in favor of lab coats and trade their white boards for microscopes. Take the popular show CSI an example. To solve even the most basic of mysteries, the Crime Lab examines every piece of evidence under the microscope. The why is simple—the microscope takes things that normally are invisible to the human eye and puts them into focus, providing answers, which help the team anticipate their suspect’s next move.

In the world of brand marketing the situation isn’t that different. Marketers today are charged with investigating small objects–the minute to minute actions of their customers. This can include how they’re shopping or not shopping via their iPad or what they’re saying about their brand on Facebook and Twitter. But like in the lab, these insights can’t be gleaned without the right technology. In fact they, require the microscope of analytics.

Analytics give marketers a chance to watch how consumers interact with one another and react with brands, products and campaigns. An example of an analytic tool in action is our economic indicator. The indicator provides a snap shot of online retail sales and shopping trends as well as consumer sentiment towards the online and offline shopping experience.  This project is exactly the type of effort brands should be conducting every day, just on a much broader scale.

For instance, IBM has discovered that mobile commerce has passed the point of no return with sales over mobile devices reaching 13.3 percent, nearly double what we witnessed in the first quarter 2011.

We also found some interesting trends when it comes to consumer sentiment. Consumers are becoming more and more satisfied with the buying experience, including convenience, which had a 65.8 percent positive sentiment rating, more than six times the negative. However, there were also some areas that brands must address including issues around product availability.

Now imagine you are the CMO. By observing these customer protons and neutrons in real-time, they can secure an unfiltered view of how their brand is being viewed. Are consumers satisfied with the shopping experience your band is delivering? Perhaps issues exist around product availability. When marketing campaigns are delivered, buyers who find out that those products are not available or out of stock turn their focus elsewhere. With this insight the issue can be fixed through intelligent automation.

Next, you can observe their actions. For example, if sales for the past week have dropped off, what was the cause? Perhaps your latest campaign didn’t deliver a compelling mobile commerce element that your audience craves.  Maybe your customers have started to shop through their tablets but your site isn’t configured to handle the iPad and therefore you’re siphoning off dollars to the competition. Knowing the root cause can help you avert a bad chemical reaction that can ultimately blow up in your face.

While the IBM economic indicator is designed to put the retail industry under the microscope, the fact is that companies today have the opportunity to execute a similar analysis of their own business. This willingness to embrace this new technology may put them in a position to regain control at a time when it seems like the consumer is calling all the shots.

Technorati Tags: Analytics, CMO

Click here to view the embedded video.