The end of summer brings one of the most popular global sports events of the year -- the US Open.

More than 700,000 fans are expected to attend the matches at the USTA's Billie Jean King National Tennis Center in Queens, making the US Open the most-attended, single sports event in the world. Even more viewers are expected to watch this year's tournament on TV, topping the 53 million viewers who tuned in last year on CBS and ESPN.

And a record number of fans are expected to follow the US Open matches on their mobile devices, or seek out the latest match results, news or live streaming of tennis matches at USOpen.org on their computers at work or at home. We're expecting to easily top the 15.5 million visitors who caught the action last year via the tournament's website. These are big numbers all around.

You might not realize, however, that major sporting events like the US Open are not only exciting to watch and follow, but are also a living lab for how "big data" can translate into big business. This year, the USTA is using business analytics to improve the experience for everyone: fans, tennis players, event organizers and broadcasters.

We're all asking the same questions about the 2012 Open. What does Sam Stouser have to do to repeat last year's women's victory, or how can past winners Serena Williams and Maria Sharapova reign again? What can we expect from the men's side? With Rafa Nadal sidelined by injury, will past US Open winners Novak Djokovic or Roger Federer win the men's title? Or will Andy Murray break through, fresh from winning his gold medal at the Olympic Games in London. How can each of them outplay the others to bring home the trophy?

Answering those questions while connecting tennis fans to the action on the court requires a unique digital experience powered by analytics and cloud computing technologies. By offering deeper analysis and a better understanding of how players are performing and ensuring that USOpen.org can handle peak traffic when website demand picks up, my company is helping the USTA serve up an engaging and interactive experience.

For example, SlamTracker is an online dashboard that serves up statistics and information for every match being played. Not only can fans follow live scores, point by point, but they can click on a point on the match's timeline for additional details.

But most importantly, a SlamTracker feature, "Keys to the Match," provides insight into what each player needs to do in order to have a higher likelihood of winning. We analyzed 39 million data points covering Grand Slam matches over the past seven years to provide analytic assessments of players and what they need to do to succeed.

Based on head-to-head games in the past, the system filters and ranks the top three keys to the match for each player. Examples might be the need for an individual player to return a certain percentage of second serves in order to win or whether longer points favor one opponent over the other. Take a look at the keys before the match, then follow a player's performance against them as the sets progress. You'll see in real time that the keys are a great predictor of success.

Use of this technology is not limited to sports. The same analytic software is being used by hospitals to monitor babies in prenatal wards, police forces to prevent crime and financial services companies to improve customer service and cut costs.

Dating back to 1992, when my company became the official information technology provider, the US Open has embraced this type of cutting-edge technology in order to improve tennis fans' enjoyment of the sport. And 2012 is bringing even more ways for fans to follow the action.

This year, a new free iPad app has been added to the iTunes Store. It joins the existing US Open iPhone app, US Open Android app, and mobile version of www.USOpen.org at m.usopen.org to provide the latest news and scores. Last year, fans viewed a record number of 84 million pages from their mobile devices.

And while sports writers like to predict who they feel are the most likely to win the US Open, we'll use the IBM Social Sentiment Index to measure what fans are saying and expressing about the tournament on Twitter. Later in the tournament, we'll reveal unique insights based on our analysis of fan sentiment.

Enjoy the tennis no matter how you choose to follow it, since the experience will be immediate and insightful, thanks to technology.

For more information about IBM's work with the US Open, click here.

Editor's note: This article is by Christer Johnson, IBM Global Business Services' Advanced Analytics Services Leader for North America. A member of Christer's team, Ryan Hendricks, will participate in a panel "Big Data in the Sports Industry" at the IBM Research Colloquium "Box Office to Front Office: Winning with Big Data" on August 10, 2012. Watch over livestream beginning at 10 a.m. U.S. Pacific Time.

One of the many things I've learned from more than 19 years of using analytics to solve challenging business problems is that the word analytics means different things to different people. So before diving into numbers, I define analytics by the objectives they intend to achieve, and the decisions they intend to improve or accelerate. In that context, analytics falls into three categories: descriptive, predictive, and prescriptive.

Descriptive analytics, also referred to as business intelligence, provide a clear understanding of what has happened in the past, through visualization of key performance metrics or other data in a report or dashboard. Today, the past can be as recent as just a millisecond ago.

The sports world has long been a leader in the use of descriptive analytics to provide fans, coaches, and players with a wide range of statistical reports that help them understand what's happening on the field -- whether a coach wants to improve play, or fans want to win their fantasy league.

However, with descriptive analytics, fans and coaches alike must rely on their intuition and ability to interpret the data in order to gain any insight on the relationship or correlation between data inputs and data outputs.

That's where predictive analytics, the second category of analytics, comes into play.

In predictive analytics, the objective is to use advanced mathematical techniques on that past data to understand the underlying relationship between data inputs, outputs and outcomes. Effective predictive models let us quickly understand and estimate outcomes across a wide array of scenarios and conditions. Commonly used for forecasting, simulation, root cause analysis, and data mining, predictive modeling techniques provide insight into complex data that we can't manually interpret from a report or interactive dashboard.

Billy Beane of the Oakland A's famously used predictive modeling techniques to uncover new data inputs that were highly correlated with the outcome of winning baseball games. In tennis, IBM recently began using predictive analytics to automatically sift through a multitude of factors from seven years of data about every point played in the Grand Slam tournaments -- all to estimate the top three keys to each player's match.

Predictive analytics still requires manual evaluation of the various scenarios and the predictive results of each scenario, in order to make a decision. This works well when a decision involves just a few options and the decision maker has time to interpret the predictive results from the various scenarios (for example, a coach using past game statistics to plan for the next game).

It does not work well, however, when a decision maker is faced with thousands or millions of options. Nor does it work well when a decision is needed just seconds after key data inputs are received. This is where prescriptive analytics comes into play.

This third category of analytics, prescriptive analytics, uses mathematical optimization to take into account a multitude of data inputs and constraints related to an objective. The formulas sift through potentially millions of possible decisions to prescribe the actions that will maximize the user's objectives.

Major League Baseball now uses a complex collection of optimization models to create its schedule each year. And some of the most-common uses of optimization outside of sports include pricing optimization for airlines, hotels, and retail chains; transportation planning and scheduling for distribution companies; and the decisions around how to allocate marketing dollars across channels and product categories.

Analyzing Big Data
Today, even small companies are armed with the software and hardware platforms that can efficiently and effectively perform these three types of analytics on enormous volumes of data -- Big Data.

Big Data is defined by the four Vs: Volume (terabytes, petabytes, or more), Velocity (streaming data), Variety (structured variables in a database, versus unstructured text, voice, or video), and Veracity (the degree to which data is accurate and can be trusted).

With the explosion of unstructured data on social media, companies are rushing to analyze this type of Big Data to better understand customers' views, preferences, and behaviors. As exemplified during the Olympics, there are few industries that generate more excitement, discussion, and ultimately data than sports.

The key for sports franchises, as with any company needing to make the most of big data, is to start with the question to be answered, and the decision to be made. Once the question and decision are clear, you have a much higher chance of collecting the right data; using the most-appropriate analytical techniques; and producing insight that you can turn into value for your customers, your company, and yourself.

Watch "Winning with Big Data"
On August 10, my colleague, Ryan Hendricks, will join a panel with Dave Kaval, the club president of the San Jose Earthquakes, Mike Zoglio, the vice president of Marketing, Electronic Arts Sports, and Rory Brown, the director of content operations and analytics with the Bleacher Report as part of "Box Office to Front Office: Winning with Big Data."

Editor's note: This article is by Michelle Zhou a senior manager at IBM Research - Almaden. Dr. Zhou will participate in a panel "Big Data in the Entertainment Industry" at the IBM Research Colloquia "Box Office to Front Office: Winning with Big Data" on August 10, 2012. Watch over livestream beginning at 10 a.m. U.S. Pacific Time.

My husband and I like to go to our local library and will occasionally borrow movies. But without a way to search beyond general categories in alphabetical lists, it's hard to find something we want to watch. With Netflix, on the other hand, we get recommendations based on our past rentals and movies we rated.

My team at IBM works in people analytics, which aims to gain a deep understanding of people including understanding their personality and needs. We then use such an understanding to create hyper-personalized engagements with individuals (such as making a movie recommendation based on personality). Right now we're looking at how to use big data generated on social media to analyze brand perception and the people who voice those perceptions. We want to help companies better serve their customers with what they want, when they want it.

Our work will also help companies better serve new customers who don't have enough behavioral data for the companies to analyze. Social media data can help companies find people with similar interests of those they already serve. In addition, social media helps companies learn about what their competitors' customers are like, and how they are served.

The Four Vs

IBM defines big data according to Volume (the scale of the data); Velocity (how fast is the data moving); Veracity (how accurate/truthful is the data); and Variety (forms of the data)
Today, not only are traditional data (books and sales transactions) captured, digitized and becoming more accessible, but new types of data (social media and mobile activity) are also being generated at an incredible pace.

IBM has helped its customers efficiently, effectively, and securely manage their data - even before the big data boom. Now, our analytics software and services can help them extract more value in all the data they own.

Big Data Insight Helps Business and You

Some would say this is just another effort to sell more to more people. But I think that the better we understand our customers, and people in general, the better we can serve them and help them.

Analytics is more than targeted advertising. It can be a public service.

For example, after the 2011 earthquake and tsunami in Japan, a rumor spread through China that iodized salt would prevent radiation sickness. The false alarm caused a rush on salt at supermarkets across the country, leading to shortages and scalpers looking for quick profit. What if China's government could have detected and responded as quickly as the rumor spread?

Whether it's helping an individual find a product or service that he or she wants, when he or she wants it, or helping a government agency respond to a crisis, my team wants to find ways to use big data (Volume) in real time (Velocity), which may come in different forms (Variety), to give people the most-accurate (Veracity) and useful information as possible.

Watch "Winning with Big Data"

On August 10, I will join a panel with Todd Yellin, the vice president of Product Innovation at Netflix, and Ray Elias the CMO at StubHub, as part of "Box Office to Front Office: Winning with Big Data."

Add a reminder to your calendar, here, to watch it live, and join a chat with a member of my team, Jeff Nichols, on IBM's Smarter Planet Facebook page.

It's a staggering amount to think about: Health care fraud costs the economy about $250 billion every year, according to the FBI.

Fraud is a major contributor to the skyrocketing waste plaguing the U.S. health care system. And people bilking the system are coming up with more sophisticated, fraudulent schemes, whether it's through overbilling, charging for services never performed, or even billing for more hours than there are in a day.

According the Institute of Medicine, $2.5 trillion is spent annually on health care in the U.S. So in a system that large, pinpointing fraud is harder than ever using traditional approaches.

Still, the same aspects that make U.S. health care so ripe for fraud -- the fragmentation of care, disconnects in the system between suppliers -- are also what makes it so well-suited for the use of smart technology that can root out fraud.

Because today's smarter analytics systems can do what humans just aren't capable of in a system of this size and complexity, we can now use smarter analytics to track millions of ever-changing pieces of data, from claims to past payouts, or to pinpoint patterns of behavior that point to fraud. And because today's systems can track incoming data in real time, they aren't just reacting after the fact. They can detect fraud as its happening -- before a single dollar is paid out.

North Carolina's Department of Health and Human Services is leading the pack in adopting this new approach. The state began rolling out predictive analytics software to ferret out patterns of waste and fraud in its Medicaid program, which covers two million people. Each year, the department spends $12 billion in handling 88 million claims.

Analytics enables North Carolina to automatically look through tens of thousands of documents and hundreds of millions of pieces of data in mere minutes. As a result, in the first phase of data analysis, the state was able to pinpoint hundreds of millions of dollars in suspicious claims by hundreds of providers, which are now being investigated.

Advances in analytics also mean that today's systems get smarter the more data they're given. As data about past fraud cases are stored in the system, for instance, the system learns to be on the lookout for similar patterns of behavior in the future. The systems can also recommend the best responses to different situations, whether it's a simple letter requesting payment in one case, or a full-blown investigation in another. This can result in better customer service by using a fast-track process for legitimate claims.

At a time when consumers are shouldering an ever bigger burden of the growing health care costs and governments are being forced to cut back services, it seems even more unfair that unscrupulous actors should get away with ripping of the system. Analytics makes sense, even in these lean times of budgetary cuts because even as they've become more sophisticated, they're also more cost-effective.

In fact, health care organizations can't afford not to take advantage of analytics. It's like letting money float out the door when they have the power to put a stop to it.

To learn more about how IBM can help prevent fraud in health care, click here.

It's no secret that government agencies store some of the world's most valuable data. In terms of applying analytics technology to unlock the power of what big data has to offer, government data has the potential to solve some of society's biggest challenges. And government clients are looking at big data as the next great natural resource.

The United States government, in particular, hosts some of the largest data centers in the world. A recent MeriTalk survey reported that U.S. government agencies will add a petabyte of stored data during the next two years. To put that into context, a petabyte of data is equal to 20 million four-drawer filing cabinets filled with text.

Currently, most of these organizations are just starting to explore ways to leverage analytics to manage for results, and are spending more time collecting and organizing data than analyzing it. The same study also found that 60 percent of civilian agencies and 42 percent of Department of Defense and intelligence agencies say they are just now learning about big data and how it can work for their agency.

Last week, I had the pleasure of meeting with a number of government agencies to discuss these challenges at the IBM Business Analytics in Government Forum in Washington, D.C., and they shared their experiences. These customers realize that there is tremendous opportunity to use their growing mountains of data to make better, fact-based decisions. And they feel encouraged by the White House's commitment of $200 million toward a Big Data initiative designed to better harness and utilize the massive amounts of data in the government.

The advent of Watson-like technologies such as software and hardware that instantly analyzes natural human language, and massive amounts and varieties of big data flowing from sensors, mobile devices, and the Web, can potentially help governments to find answers to questions like "which public services are most effective" and "are social benefits reaching their intended targets?" and "is energy being used efficiently in our office buildings?" These systems literally sift through the data and identify patterns and trends on the fly, then present it in a way that's easy for people to understand.

One of the greatest challenges facing government agencies is determining the most important information to look at. In the past, companies looked in the "rearview mirror," collected information from social media sites, and stored it inside a database. Then they analyzed it, which could take weeks, and brought those insights back into the business.

At IBM, we've designed a big data platform that can access, store and analyze any data regardless of how fast it is moving, what type it is, or where it resides. The platform enables clients to perform advanced analytics on data in its native form, visualize all available data for analysis, build new analytics applications, optimize workloads, and apply security and governance to big data.

Now that government can analyze any information as it happens, it can stop looking at the rearview mirror and focus on the road ahead. We're at a unique point in time where governments can better understand their citizens and the effectiveness of the services they provide.

With a population expected to exceed nine billion by mid-century and a fixed water supply, the world's demand for water is quickly outpacing its supply.

We rely on steady seasonal rainfall to restore our ground water sources, but droughts are becoming more frequent, often forcing communities to enforce stringent water restrictions.

Nearly half of the world's 6 billion people live in water stressed areas. Eighty countries already have water shortages, and the World Bank warns that the demand for water doubles every 21 years. Thirty countries already get more than one-third of their water from other regions. And treating the water we do have to make it safe for consumption requires great amounts of energy and generates as much carbon emissions as a passenger jet.

Growing the food we eat consumes 70 percent of our freshwater supply. And much of the remaining amount is used extravagantly at home. A family of four typically uses 260 gallons of water a day, mostly in the bathroom. Flushing toilets, using faucets, and taking showers or baths account for 75 percent of the total.
At the Desert Mountain golf community, we are partnering with the City of Scottsdale and the State of Arizona to utilize treated effluent, or reclaimed water, to reduce our water footprint. To further reduce our footprint, we are using a deeper level of analytic insight that also saves energy and cuts operating costs. More importantly, everything that's done at Desert Mountain is done with the indigenous plant life and wildlife of the community in mind.

For many years now, Arizona has been in a drought. In fact, we're supposed to average 12 inches of rain annually, but we haven't had that amount in many years. So at Desert Mountain we're implementing a water conservation program that will help us become even more efficient and better prepared to meet water shortages and environmental changes.

The information we collect from underneath the ground and from weather stations helps us make better decisions on how much watering to do. The less water we use, the more energy and costs we save on treating and transporting water. We are not only making our business more efficient, but we are also playing a role in conserving a precious natural resource.

To date, we've saved eight to 10 million gallons of water by understanding the data we're getting from the sensors in the ground that tells us when moisture levels are low or when there is water leak. And we can react faster to that leak.

If you look at the people who live here, you'll see that Desert Mountain is a community of leaders. We are taking a proactive stance to become better stewards of the land and in becoming educators on the role we all can play in protecting our natural resources. That's the goal we've set for ourselves going into the future.

There's no shortage of European sporting events for fans to enjoy these days. The Euro 2012 football championships are going strong in Poland and Ukraine even while fans gear up for the London Olympics. So, how does the All England Lawn Tennis and Croquet Club, whose Wimbledon Championships qualifying rounds kicked off this week, get people to pay attention to tennis?

One way the club generates excitement is by enriching the fan's experience through the use of cutting-edge technology. For several years, IBM, a sponsor of the Wimbledon Championships, has used the event as a showcase for some of our leading-edge solutions-technologies that we believe can make the whole world work better. It's a way of using a popular sport to bring our Smarter Planet agenda to life.

This year, in collaboration with the All England Club, we're introducing a new technology to Centre Court. It's called IBM SecondSight. Using a handful of strategically placed video cameras, we track the movements of players in real time and present to fans a digital on-screen representation of the match, complete with avatars representing the players. Fans can click buttons to see up-to-the-second analysis of the match. Who is moving faster? Who is running more? Is somebody tiring?

It's the deepest look ever at the purely physical dimension of the game, enriching the fan's (and coaches and officials) knowledge of the science of tennis.

Here's a video that shows how the technology works:

The idea for IBM SecondSight emerged out of a remarkable happening at the Championships two years ago when American John Isner and Frenchman Nicolas Mahut played the longest match in professional tennis history. Their 183 games lasted a total of 11 hours and five minutes over a three-day period.

I was sitting in the control room as the points racked up in the tie-breaker session. The designers of the scoring system had not anticipated ever needing to record and display such a high score, so we were in danger of running out of numbers. I was a nervous wreck. But, at last, Isner put the match away with a passing shot and won the tie breaker. The score: 70-68. When he flopped to the ground in a combination of exhaustion and elation, I bet I was as relieved as he was.

That's when I had the revelation: why don't we track the players' movements? After all, we recorded almost everything else about the matches. So we set about developing just such a system in collaboration with a technology partner whose main line of business is tracking missiles. We tested the system last year in a discreet pilot program on Court 18.

For this year's Championships, IBM SecondSight is moving to Centre Court, but, for now, it will only be available for visitors to Wimbledon. In the future, once we certify the quality of the data, I expect this system and others like it to be available for Web site viewers at all major tennis tournaments worldwide.

For next year's Wimbledon, we plan on adding the capability to IBM SlamTracker, our cloud-based package of predictive analytics for enabling fans to gain deeper insight into matches. The movement-tracking technology will bolster the Keys to the Match feature, which leverages historical and real-time information to determine the top three things a player must do to win a specific match.

It's early days, but I can envision powerful uses for the movement-tracking technology in realms far beyond tennis and sports. For instance, it could be used to monitor and analyze movements of people in a shopping mall, a factory or an airport, or traffic on highways. Imagine the valuable insights one could draw from that kind of information.

When IBM hosts clients at Wimbledon, we talk to them about Keys to the Match. We urge them to think about how analytics can help them understand the "three keys" (or four, or five...) in their businesses. And we can talk to them about how technology can boost their performance. It's a useful exercise, clients tell us. Now, thanks to IBM SecondSight, we can give the concept of business velocity a whole new meaning.

Rugby is one of the world's toughest sports. Large men wearing little or no protective gear collide with each other at full speed. They leap. They scramble. They mash together in scrums. So it's no wonder that rugby's injury rates are nearly three times higher than soccer's.

In professional rugby, one of the essentials for achieving a winning record is reducing the injury rate. That's why the Leicester Tigers, the most successful professional rugby team in the United Kingdom, recently adopted predictive analytics software aimed at proactively reducing injuries. The goal is to avoid the physical and mental fatigue that sets players up for some of the most common rugby injuries, which include muscle and ligament tears and joint dislocations.

"Our data suggests that if we have a fully fit squad, we'll rival any team in Europe. If we have a lot of injuries, we'll have trouble competing with the best," says Andy Shelton, Head of Sport Science. In spite of having three key players out with injuries right now, the Tigers are in second place in the premier division in the final weeks of the season.

The Tigers' project is just one of many examples of data analytics helping to transform the way professional sports teams operate. Statistics have long played an important role in sports, but deep analysis of data to spot unexpected patterns became mainstream after Oakland A's manager Billy Beane built a top-flight baseball team on a shoe-string budget-a story told in the book and movie, Moneyball.

It's all part of the data analytics revolution. Industries from retailing and healthcare to banking and law are increasingly using analytical tools to gain competitive advantage.

Professional rugby teams have long used analysis of game play to improve their performance and prepare for the upcoming rivals. For several years, led by Alex Martin, Head of Strength and Conditioning, the Tigers have been gathering detailed data on player's individual fitness and performance. Now they're going even deeper-evaluating each player's vulnerability to injury.

They gather data in two ways. The sports science team records every event involving a player-collisions, leaps, kicks and sprints. In addition, players wear small monitoring devices during games and practice that measure the intensity of their activity and transmit the data wirelessly to a computer system on the sidelines.

Once the team gathers detailed information, it hopes to be able to anticipate when each player is fatigued and, therefore, is more vulnerable to injury. That way, the coaches can take the player out of a game or reduce the intensity of their practice or fitness regimen before they're injured. They'll also be able to better manage a player's recovery from injury-making sure they don't try to come back too quickly and risk re-injuring themselves. "The end goal is that nobody gets to a state which may predispose them to injury," says Shelton.

Thanks to funding from The Matt Hampson Foundation, the Tigers are using software from IBM, SPSS Modeler, to perform predictive analytics. The plan is to spend the next year or so fine-tuning the system. They'll discover each player's fatigue threshold based on detailed activity and injury records. Ultimately, Shelton says, they'll be able to measure each player's freshness during the game and decide in real time whether to leave them in or send in a substitute. "We want to be the leader in analytics," he says. "It's simple. If you have your best players on the pitch, combined with the best tactical knowledge, you'll win more games."

There's valuable content in those emails, documents, correspondents, statements, videos and blogs. The key is to find it, make it accessible, and share it.

By rethinking the strategic role of content, businesses can harness all this data, better connect people to key information, and analyze content to make better business decisions and reduce risk. Why the need? There are three key shifts today driving the need for a "strategic" view of content management.

Big Data: It's here to stay. We're creating 2.5 quintillion bytes of data every day from virtually everywhere--social media, emails, online videos, energy grids, online purchases, mobile GPS signals, and more. On the one hand, this explosive growth is enabling enhanced collaboration, productivity and innovation. On the other hand, it dramatically increases IT costs and risk exposure. A recent IBM study showed that an estimated 50 percent of organizational content carries risk, without delivering any measurable business value. Organizations today are increasingly tasked with managing this wealth of structured and unstructured data, while at the same time keeping a keen eye on value and exposure.

New Technologies: A recent AIIM study showed that 60 percent of companies surveyed believe that content chaos is out of hand and needs to be controlled. But the wealth of big data is also an opportunity to unlock the insight that lives in that data to increase business agility and answer previously unanswerable questions. Now the good news -- new technological innovation is enabling organizations to improve business outcomes by applying analytics to gain actionable insight. At the same time, other new technologies are enabling businesses to proactively dispose of unwanted, unnecessary content effectively.

Lack of Trusted Info: A recent IBM study found that one in three business leaders frequently make decisions based on information they don't have, or don't trust. Lack of trusted information is forcing line-of-business organizations in companies of all sizes, in virtually every industry, to change the way they view content, and see it as a strategic asset in the context of high-value business solutions that can deliver positive outcomes and real business transformation. And line-of-business is increasingly taking control of content management within organizations.

Forward thinking companies are mitigating that volatility and risk, and in the process transforming the way they do business. They're capturing enterprise data, extracting its value, and turning it into a valuable asset that can provide insight into how optimize supply chains, uncover consumer behavior patterns, and identify traffic and energy patterns. They're putting that content in context, essentially putting it in motion, and using it to solve problems, and make decisions that deliver better outcomes, faster. They're using it to deliver innovative products, superior customer service, all while empowering knowledge workers like never before.

Tejon Ranch, for example, the largest land management company in California, uses an IBM mobile content application that enables iPad users to take advantage of key content and case management capabilities remotely for contracts management, insurance verification, and bond management. With 270,000 square to traverse, Tejon employees now have a mobile solution when they're off-site to access key information and collaborate across the organization.

By turning passive content repositories into active sources of business insight and gaining control of information, companies today can optimize their business and reduce costs -- maximizing productivity, increasing competitiveness and being more prepared for the unexpected.

To me, the recently-announced collaboration between my institution, Memorial Sloan-Kettering Cancer Center, and IBM is of profound importance for reasons both obvious and more subtle. The obvious reasons concern what we may call knowledge. The subtle ones would be apparent only to those of us who care for patients on a daily basis and concern what we may call wisdom. Let me explain what I mean by the distinction.

First: What is the project? Our two organizations plan on applying IBM's Watson technology, which impressed all of us by beating two grand-champions on the Jeopardy! TV quiz show, to MSKCC's vast store of cancer case histories, utilizing the skills of expert computer scientists and highly experienced cancer doctors. The goal is to develop a tool to help physicians all over the world better care for their patients with cancer.

We live in a time of massive expansion of knowledge about cancer. Biological scientists all over the world, including at MSKCC, are producing information continuously, including information about the molecules that go awry to cause cancer in the first place. Furthermore, there is a steady stream of clinical research advances in the diagnosis and management of cancers of all types.

To be useful in patient management all of these bits of information need to be gathered and assimilated and made practical. For decades MSKCC has had specialized cancer physicians and scientists focused on the production and accumulation of such knowledge. In addition, we have used this information in the care of our patients and we freely share our knowledge as well. For example, colleagues around the world frequently call us to discuss difficult or unusual cases. By asking questions and exchanging information the treating physician and the MSKCC specialist arrive at a management plan that is best for that particular patient at that moment in time.

But this is not an efficient process. Most patients with cancer in the world are not treated by specialists in their type of cancer. The physicians treating them do have access to written guidelines and other sources of information, but that is not the same as having an experienced, specialized medical expert immediately available. Hence, we have long sought a means of bringing up-to-date knowledge to the bedside of every cancer patient.

But how can we do that when there is so much information, when it is constantly changing, when it needs to be interpreted, and when it is in the form of language with all of its subtleties and nuances?

Along comes IBM Watson.

Here we have the confluence of two highly developed areas of intellectual activity. At MSKCC we not only have extensive experience in the care of tens of thousand of cancer patients--how they have been treated and their outcomes as well--but we have this in written form. This is because of our use of a sophisticated electronic medical record system, meticulously constructed so as to capture all of the relevant clinical information in a way that absolutely protects patient privacy.

In Watson we have a computer system that can read and understand language, interact with human experts, and remember everything it has ever learned. And it can use this knowledge to arrive at answers to real-life questions.

As medical educators, we take young doctors and educate them to be expert cancer specialists in a few years, and we do this by teaching in English. Now we have a machine that can be taught in English and will never forget the knowledge we impart. And, by constantly learning, the machine will also produce new research questions that will help us improve the state of the art as we learn together.

But, one may ask, what about the human side of the equation? Medical decisions are not just about information: they are also about judgment. As experienced physicians we need to take everything into account in arriving at a best management plan--not just the individual patient's biology and the biology of their disease, but social, psychological, environmental, motivational and interpersonal factors.

It is here that the less obvious advantages to our project become paramount. These personal elements are captured in the language of our case histories, and Watson will learn them-while remaining ignorant of patient identity. Furthermore, Watson will not make decisions, but will interact with the on-site physician in asking the right questions to help the patient and the doctor arrive at the right decision for that particular individual. The human is never left out of the conversation. And that humanity--never before captured in guidelines or lists of therapeutic options--is what makes this project unique: It goes beyond mere knowledge, as important as that is, by entering the realm of human wisdom.

So I see the IBM-MSKCC collaboration as a way of bringing wisdom as well as knowledge to the care of cancer patients anywhere in the world. And that is the essence of why I see this project as one of profound importance.

Whether it's through comments posted on Twitter, Facebook, LinkedIn or other social media sites, companies these days are listening to what we're saying. They realize that our complaints -- or compliments -- can be very public. What we say can be read, shared and re-posted by thousands of others via social networks.

Recently, I ordered a gift online from one of my favorite retailers. Unfortunately, it arrived two days later than promised. I had the option of complaining to my Facebook friends and Twitter followers, but I didn't need to. When I called the customer service hot-line, the retailer took good care of me, waiving the shipping charges and providing a decent credit for a future purchase.

Companies are getting smarter. They realize that it costs six to seven times more to acquire a new customer versus retaining existing customers. Lots of companies are using analytics to better understand social conversations and improve customer service.

We are drowning in data. It's estimated that there will be one trillion devices operating in the world by 2015. IDC predicts that the amount of digital data in 2020 will be almost 50 times larger than that of a decade earlier. Eighty percent of the information will be what's called "unstructured" data, including all those YouTube videos, baby pictures, and "songs of the day" floating around the Internet in emails and instant messages. This massive amount of data is something we call "big data" because, well, there's so much of it.

And to make sense of it, we need to analyze it.

In fact, by using advanced analytics and technology to analyze human language, (in other words, the tons of big data associated with language) we can harness that information.

Recently, in the build up to the Oscars awards, we used an algorithm, or mathematical formula, to analyze millions of "tweets" on Twitter to see what people were saying about potential Oscar winners. Turns out "Girl with the Dragon Tattoo" was mentioned very positively in Twitter chatter, yet did not receive a nomination for "Best Picture" category.

The so-called "Oscars Senti-meter" combed through a high volume of tweets daily and used language-recognition technology to gauge positive, negative and neutral public opinion contained in the 140-character messages. We call this process "sentiment analysis."

A similar project to analyze sentiment found that Quarterback Eli Manning of the New York Giants was more popular in social media than Quarterback Tom Brady of the New England Patriots -- before the first play of this year's Superbowl Game, won by the New York Giants. While popularity didn't determine the game's outcome, one can envision how it can influence future player contract negotiations and sponsorship valuations.

In addition to sentiment analysis, organizations are also doing social network analysis, which starts to measure influence. By doing so, companies can begin to pay more attention to the voices that are likely to have the greatest influence.

At the same time, companies are also looking at broader sentiment trends to predict how confident people are feeling, so that they can more accurately predict consumer confidence, say, ahead of a shopping season. We are only experiencing the introductory effects of the analytics that will soon grasp more and more influence in our world. We've only just begun to harness the power of big data and analytics.

For more information on how business analytics can help organizations use the power of insights to shape business outcomes, click here.

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In an era of Smarter Analytics, it is imperative that businesses leverage the massive quantities of data available to them. In order to remain competitive, data must be transformed into insight and integrated into business processes.

Real-time analytics from IBM allow businesses to predict and respond to a situation in an instant. From train track malfunctions, to power disruptions, to financial fraud, Smarter Analytics means more accurate predictions and faster reactions.

When I attended the City College of New York, I was sneaking into the computer lab well past midnight so I could use the computers, those foreign entities that liberal arts students did not need to interact with.

Today, we've entered the age of what we call "Big Data," where students in all fields will need new skills that take advantage of computer-based analytics if we're going to find fact-based solutions to the grand challenges of the world, such as water, energy, and food shortages, global warming, and many more global issues.

New tools are needed to deal with vast amounts of data now being acquired that speaks to these issues. Every day, we wade through streams of texts, tweets, Facebook updates, PowerPoint downloads, Excel spreadsheets, traffic data, population statistics, etc.

What's sobering is that the barrage of data we're dealing with now is just a trickle compared to the flood that's coming. Social networking, cloud computing, sensors on water meters, supply chains, even sensors within our own bodies, all create vast streams of data.

As someone who cares about our educational system, and who has worked to make it more competitive, I think this data deluge makes one thing clear: students will need new skills in analytics. We need to make analytics a basic, essential piece of the general education process if we're ever going to make sense of all the data in our everyday lives.

In the past, studying analytics was the province of math geeks and computer science majors. Going forward, almost every profession in every field, whether it's healthcare, finance, retail, engineering or energy, will need to be able to manipulate these new streams of data. Not simply to process this information, but to come up with creative new ways of sifting through this data, pulling out unexpected insights, and innovating around these observations.

Students need hands-on experience in using data in their own studies, whether it's the classics or chemistry. They need to be familiar with the most up-to-date tools and concepts that allow them to hunt for patterns and statistical correlations in these streams of data.

Perhaps yet to be apparent, analytics applied to "Big Data" will be as transformative as the advent of the industrial or digital age. Society has never been able to collect as much information nor apply as much computing power and collective analysis to it. Analytics is already ushering in new ways of thinking about and optimizing our world.

In this changing world, analytics will be a key competitive advantage, a skill that sets employees, companies, and potentially even countries, apart. The impact of analytics applied to Big Data has already been seen on many fronts, from optimizing an entire city's transportation system, to understanding disease recognition and treatment. Those who cannot practice this new "art" will be ill equipped to compete and potentially, will be left behind. We cannot allow yet another digital divide to develop because of inaction.

To learn more about IBM's University Relations programs, visit www.ibm.com/university.

Learn more about what universities around the world are doing with analytics here.