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An Interview With Michael Lewis And Billy Beane: Data In Baseball And Business

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Michael Lewis, journalist and author of several best-selling books, including Moneyball, his 2003 tome that revealed how a small market baseball team could compete with the big teams by using empirical statistical data from baseball to predict future player performance.

Billy Beane is a former Major League Baseball player and the current general manager and minority owner of the Oakland Athletics. He was the first major league manager to put "sabermetrics" into practice.

At the recent IBM Information on Demand 2011 event in Las Vegas, Nevada, one of the key themes of the event was the idea of putting business analytics into practice to help improve business outcomes.  No one was better prepared to address this topic than Michael Lewis, author of best-selling books Moneyball, The Big Short, Boomerang, and others, or Billy Beane, general manager of the Oakland A’s baseball team and the first major league manager to utilize “sabermetrics,” or statistical historical data about baseball player performance, to take a smaller market team with less money to spend on players to the post-season several years in a row.  I sat down with the two of them backstage at the Mandalay Bay Events Center just prior to their keynote presentation for several thousand IOD attendees, and following is the result of that interview.

Todd Watson: One of the key themes of the IOD event has been “turning insight into action,” and that seems to be a theme prevalent in some of your books — most notably Moneyball and The Big Short. I’m curious, in terms of baseball managers who are using sabermetrics to make more informed decisions, I’m really interested in how you got turned on to that topic and also just how that came to be and what inspired you to write the book?

Michael Lewis: It was really simple. I was living in Billy’s [Beane] backyard in Berkeley so I was paying attention to the A’s. I didn’t know…I wasn’t a baseball fanatic, but I did know there was this payroll issue and I got interested in that.

I got interested in that in the first place, because at first I thought I was going to write a piece about the A’s. I think it was when Jose Canseco got this giant deal, and he was being paid something like $8 million, and the right fielder and left fielder was being paid something like $150,000, and I wanted to know if the outfielders were pissed!

And, how they felt when those Jose Canseco dropped a fly ball. (Laughter) And I was going to come out and write about that, and then I started thinking about it, and I realized there were these huge discrepancies from team to team. And then I wondered, so how does the whole team feel about being poor???

Todd Watson: As a long time Rangers fan I can sympathize…

Michael Lewis: But I didn’t do anything about it. And then the As had a sensational year in 2001. They were clearly the force in baseball, although they didn’t win the World Series, and I thought this is really weird, they’re playing against four times the sum of money and they are as good or better. How does that happen? And I knew someone who knew Billy, who set me up to go see him.

I thought I was just going to write this little magazine piece, maybe. And when I went to go see him, the answer was so interesting, I just kept listening for a couple of months.

Billy Beane: And I just kept talking.

Michael Lewis: You just kept talking. Because there was so much to talk about. I mean the idea that the market for baseball players didn’t work, or was inefficient some way, had vast implications for all sorts of other things. But I’ve never written a sports book. So it took me a while to get my mind around the idea that I would, and how to do it. That’s kind of how it started

Todd Watson: Okay. And Billy, I’m curious for you… I’ve seen the movie and I’ve read the book and I understand what happens in Hollywood movies, but I’m curious was there some kind of cultural aversion at first, a resistance amongst your staff, to move in this direction?

Billy Beane: Yeah, I mean even in my own upbringing through the business it was somewhat traditional as well. Until I worked under a guy named Sandy Alderson who never played the game, who was a Harvard law graduate, and who was a former Marine who taught himself the game through books. So that was my first exposure.

So I sort of straddle both sides, which was a benefit to some extent. So yes, there was certainly some resistance internally, but anytime there is mathematics involved there’s going to be a certain amount of resistance. I think 99% of us have some resistance because going back to seventh grade, and when are we going to use this [math], and now you’re seeing somebody use it!

But from our standpoint, it was really out of necessity, and, we had a blank canvas. We had a fertile place to do it because we really didn’t have the pressures of a Boston or New York, and if we failed, we were probably going to finish wherever we were, anyway. So for us it was no risk, high reward.

And the other thing was, we had spent a number of years previously looking at this, and there was more evidence…The great thing about math is there something logical to it, so once we had faith in it among a small group of us, we did really feel like we weren’t taking a risk. You know, it didn’t seem risky to us. That’s what everyone asks all the time, and quite frankly, we were wondering why doesn’t anybody else get this?!

Todd Watson: So once the book got out the secret was out. How did you stay competitive then?

Billy Beane: Well, you know there was some momentum going on, and there was this other line of young executives who were taking a similar way to Sandy, and who read outside sources for information and looked at it in a rational way. So, now you’re starting to get a movement…if there’s anything I think the book did, I think it accelerated it by putting it [sabermetrics] out there for everybody. It was gonna happen anyways.

Also the parallel with technology helped it take off — access to information was all over the web because it was being gathered. You know, whereas before Bill James was doing it — and he used to print basically these tight little pamphlets – and even some of the stuff that we were originally using was somewhat, not literally, but was somewhat manual, related to how information is gathered today.

I think the parallel with technology also helped it take off, because it just gave it access to more people out there who were sort of running their own models as well. So, there was just more and more evidence that there was something to this.

Todd Watson: So this question is some red meat for my baseball buddies. Is, in fact, on-base percentage a better metric now for a player’s contribution to advancing scoring than runs batted in (RBI)? And if so, could you talk a little bit about how you arrived at realizing that?

Billy Beane: Well, that’s interesting, I don’t think we’ve ever taken ownership of inventing anything. I mean, there were some academics there outside the industry…

Todd Watson: Sure. But it’s a different way of viewing the data, right?

Billy Beane: Yeah, I mean for us, simply put, it was what metric or statistic were we going to get the most value for the dollar because we had to be more efficient than the others! Quite frankly, the correlation between on-base percentage and winning was stronger than any other statistic, other than pitchers’ E.R.A. [Earned Run Average], which is almost equal.

That being said, pitchers, going back to the earliest part of the century — everyone knows you need pitching to win. Therefore, it was an expensive commodity. At the time, on-base percentage, or guys who got on base, were not being paid at the rate the statistic probably said they should. And there is a very strong correlation…well, like you said, on-base percentage is the one metric that has the strongest correlation with winning games.

For us, it was very linear, and it was very easy for us to put all our money in these guys. And, there were players we couldn’t acquire who had skills beyond that, but the fact is those were the Derek Jeters and Ken Griffey Jrs. who did everything. And for us, we focused on one to maximize their ability to hit or walk and get on base.

If we did one thing really well, and that one thing had the strongest correlation to winning, we were going to be able to compete.

Todd Watson: So why hasn’t somebody really put this together…I mean baseball has a 100-something year history?

Billy Beane: Like I said, we were borrowers of an idea that had been around for years. Once again, I think it is part of any business culture that there is a traditional way of doing things. One of the beauties of data and statistics is that it’s the one rational way that you can challenge conventional wisdom, and somebody has to be there first to go all in.

Todd Watson: Michael, I wanted ask you one more question of you. I read The Big Short and I’ve read some of Boomerang, and I remember when the Iceland story first came out in Vanity Fair, which cracked me up, the idea that these fishermen suddenly became world bankers…

Michael Lewis: It didn’t crack them up!

Much laughter.

Todd Watson: Yeah, obviously the repercussions were not good for them. I’m just curious, then, if someone had taken an objective look at all of that data at the time surrounding the collateral debt obligations and some of the sub-prime stuff at the time, would that data have told us something was amiss?

Michael Lewis: Oh yes! The Big Short has the story. Michael Burry, the hedge fund manager in San Jose, did just that. But what he did…the subprime story is only partly a story of data, but in part it’s an illustration of data measuring the wrong thing.

Because what everybody did was just accept the measurements of the ratings agencies. Their measurement was AAA, and nobody looked at what that meant, or if that meant anything, and this investment manager looks at this pile of supposedly AAA-rated bonds and finds the individual loans and starts to measure them. And he starts to calculate what these loans are, what their loan-to-value ratios are, and the profile of the borrowers and all that, and when he did that, he saw it was a disaster, and not a question of if, but when.

So, the funny thing was, Wall Street, the big firms figured out, they could make a lot of money going with misleading information. That for the machine to keep working on Wall Street, they had to keep going with what a lot of them all knew was a false measurement taking place.

And they rated things as AAA so they could sell them. Because once you got that point you had a whole group of unthinking buyers who just accepted the stats – kind of in the same way that someone in baseball might used to say, “The guy hits .300, he must be really good.” — without thinking whether or not his hitting .300 leads to runs.

So it was a really kind of textbook case of first, people measuring the wrong things, and then, having measured the wrong things, figuring out how to make those things work for them narrowly. But it was a disaster for the system.

Todd Watson: So what are the lessons…I mean, we’re here at Information On Demand 2011 just to bring it back home to IBM and our customers. So what’s the lesson in it for our customers who are interested in using Big Data and some of these analytics capabilities effectively in their business?

Michael Lewis: It depends on the business. I think the Wall Street story is as illustrative as the Moneyball story…just be very careful what you measure, because the minute you start measuring the wrong thing it becomes a fetish.

You sort of like…organizations are sort of like greyhounds at a dog race. You set the mechanical rabbit on a track and go where the rabbit goes.  So you gotta be careful where you set the rabbit.

So that’s the…there’s nothing you can do beyond where it’s okay to have a culture that you can critique existing standards of measurements and existing value systems.

And that’s just a matter of keeping it in an intellectually open place. That’s the big challenge. Bosses don’t like that. It’s just disruptive to the hierarchy to be able to ask the kinds of questions you need to ask to keep yourself heading in the right direction, I think. Moneyball has some of that in it, measuring the wrong thing.

The other thing is just how much opportunity there is to measure new things. It’s amazing what happens when markets form around existing metrics. Take the stock market…price/earnings ratios…and forever it’s been sort of where value investors go to figure out if there’s value. It’s a stat that’s been around 60 or 70 years, and it’s pretty good. But is there a better way?

There’s nothing wrong with accepting if we’re doing things a certain way. Or asking some of the questions, if we weren’t doing it this way, would we invent some other way to do it, would we find some better way.

So I gotta ask you, your last name’s ‘Watson’?

Todd Watson: No relation to the computer or to the founder of IBM. In either case, I’d be on a yacht somewhere in the Caribbean.

Much laughter.  End of interview.

Information On Demand 2011: A Data-Driven Conversation With Michael Lewis & Billy Beane

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At Information On Demand 2011, day 3, BBC presenter showed up onstage ready to play ball with Moneyball author Michael Lewis and Oakland A’s general manager Billy Beane.

Michael Lewis and Billy Beane talk baseball, arbitrage, and sabremetrics with Katty Cay onstage at Information On Demand 2011 in Las Vegas this morning.

Fitting, considering we’re currently in the midst of this year’s World Series between the Texas Rangers and St. Louis Cardinals (Game 6 is tonight in St. Louis!)

Kay first asked Lewis why a book on baseball statistics, and Lewis explained that people are sometimes misvalued by markets, and that what Beane was doing with his team in Oakland in 2001 was a science experiment where “the lab rats [the players] didn’t really know they were lab rats.

Lewis went on to tell a hilarious story about first seeing the A’s players walking naked out of the showers, and how what he saw did not seem to be a gathering of muscle-ridden athletes.  They were fat, misshaped, and otherwise seemingly disfigured.

When Lewis approached Beane to ask him about this, Beane explained “that’s kind of the point. We’re in the market for defective people.  We’re in the market for players whose value the market does not grasp.  We’re a magnet for these unattractive bodies!”

Lewis says that’s the moment it hit him: Beane’s assembled the misfit toys of baseball, the people who have been discriminated against because of their appearance and who are greatly undervalued when compared to their actual player statistics.

Lewis went on to explain, “I realized there was this discrimination going on in the market for baseball players.  The way they had done it, with statistics, getting below it…the statistics though were besides the point.  You had to think of it as a business.  These baseball players, who do what they do, for the past 100 years, and there were all these people who considered themselves experts based on intuition instead of actual performance.”

So there they were, October 2001, the A’s v. The Yankees, and Billy Beane had some of the best players out there: Jason Giambi, Johnny Damon…but he knew he wasn’t going to be able to hold on to them, so he was going to have to throw the intuition playbook out the window.

Beane: “I remember thinking I will never have a collection of talent like this.  What the heck are we gonna do?  We knew they were gonna go (Giambi, Damon, etc.).  We knew the whole year that was gonna happen, but we were trying to find some solution and replace in the aggregate what they did.  So, we scoured guys who had A skill, not five skills.  And because we had no money…we had one of the lowest payrolls…we couldn’t afford to invest in the romance of a player, but really what they could do and with no biases for or against them, just their performance.”

Oakland A's general manager Billy Beane explains to the Information On Demand audience how data now trumps intuition in that great American of pastimes, baseball.

“Quite frankly,” he went on to explain,  “if we were ever going to trust the mathematics, this was the time.  We had nothing to lose!”

Cay then posed the all important question: How did you come to this way of looking at the data?

Beane responded that “we never claimed to have invented anything.  Numbers are historically scary to everybody, and math doesn’t come easy and doesn’t come from sports. Sports are more about the gut. But we had to be a disciplined card counter.”

Lewis elaborated: “The fact that they weren’t actually generating themselves a whole lot of new baseball knowledge, but that a lot of it was on the web, available to any team, and they recognized it as knowledge. And the use of analytics was so critical, as it took them to another decision point in the game of baseball.”

“This is why the market was so hostile,” Lewis went on.  “That there was a new and valuable way of analyzing baseball players, because it implicitly undermined their intuition and knowledge of the game.  All these years you did this job, spouting out an intuitive response. So it was finding a better way to measure baseball. Baseball stats are so clean, and it’s easy to assign them in the field of play.  The second thing was, sports are somewhat anti-intellectual, and baseball was really anti-intellectual.  Most of the kids who go on to play the game don’t go to college, and the game itself is not intellectually challenging.”

“You can’t be too stupid to play baseball,” Lewis explained, eliciting great laughter from the audience, and what had to be the most highly-Tweeted quote from the conversation.

The Information On Demand audience was packed into the Mandalay Bay Events Center to listen to Moneyball author Michael Lewis and Oakland A's general manager Billy Beane.

Then, to the heart of the matter in terms of bridging baseball analysis to business purpose: How did you get to the right numbers? asked Katty Cay.

Beane: “If you’re following metrics that have no correlation to business success, or in our case, winning games, you’re in trouble. The older the business, the more challenges and the more traditional and conventional thought.

“Baseball started in the mid 1800s. For us, it was simply put, out of necessity, if we had a dollar, where were we going to get the most efficiency from it.  Bill James really started this whole thing, but he didn’t have a venue by which to test this out.

“But I was in the game, and I had the forum and the platform, and really no other choice.  So, they had to be the stats that correlated the most to winning.”

Beane went on to detail his recipe: “We were able to pile all our chips to guys who got on base, and on base percentage had the strongest correlation to winning games.  For us, this was the statistic that had the most impact on winning.”

Cay: In the moments, you have moments of tension with the staff re: intuition.  Did you waver at all when you looked at the numbers?

Beane: “There was this perspective that it was risky, but it wasn’t, and the beauty of baseball over time is that there’s so many games you weed out the randomness and ultimately we thought we’d come out where we thought we could.  We thought there was more risk in NOT doing it, in going with our guts.”

“To go with our gut would have been the most irrational thing to do.”

Cay: Michael, how do you think Billy was able to get away with this?

Lewis: “He had to be able to intimidate his staff. It was just him and an assistant who were privy to what the goals were.  Re: the players, he said, we don’t tell them, it’ll just confuse them.”

“But he did get some resistance, yet it went away, because he was basically bigger than everyone else in the organization.  He could beat up everybody there.  There’s this law of the jungle quality to the clubhouse.  The players also knew he was a better athlete than they were. It came clear to me right away where reason was being imposed by violence.”

Katty Cay interviews Moneyball author Michael Lewis and his featured subject, Oakland A's Billy Beane, in a fascinating and relevant interview at Information On Demand 2011.

More laughter.

Cay: He looked like such a nice guy.

Lewis: “He’s mellowed.  He would chew tobacco, and his eyes would get red, and I would think, ‘Don’t get in his way!'”

Cay: Let’s translate that to the business environment.  You have to have the confidence to go with what your’e analyzing with the data.

Lewis: “It’s sort of like, did it work or did it not worth? The confidence comes from having the information and feeling like you’re right.”

Billy: “As Michael said, the tough thing is how you give out the information, and you have to be careful.  One of our directors in the back office, he said, ‘I don’t know what you guys are doing back there, but whatever it is, it works.'”

“If you were disciplined with it, you were going to be right to the end.”

Lewis: “There’s a huge amount of randomness, and you can have made a huge amount of decisions, but you can’t change the process of how you made that decision.  People make decisions based on outcomes in sports all the time.”

“If you’re the casino, and you stack the odds in your favor, and you play a really disciplined game, it’s going to be an optimum strategy.”

Cay: You described it as a flipping a coin….if you flip it a million times, it will come out well.

Beane: “The great thing is that the eight teams that get there, those are usually the best teams.  But then you get into a round robin series, and the best team doesn’t always win.  The Phillies were one of the best teams this year, but micro events did them in.”

“So a lot of decisions are made on those random events that happen in a short series.”

Lewis: “For me, this was not just a sports story, it was a market story.  It wasn’t the actual number crunching that interested me, but rather what it exposed about the world around me.”

“You could quantify a player’s value very precisely, but you could value what he’d done in the past.  How can a market be so misvalued for such an obvious thing as a baseball player.  What’s going on in markets is people are operating using intuition vs. statistics, and that influences their judgement!”

“People generalize from small sample sizes.  People overvalue things that are flashy and easy to see, like foot speed or arm strength.  And they underestimate things like plate discipline or ability to get on base.  The big thing is understanding those biases and you, the business manager, are making at least partially intuitive judgments.”

Cay: Why did you let him write a book about this?

Beane: “This is a long answer. There was a momentum that was already starting to happen, and other teams were out there.  Brian Cashman in NY, others, were already on their way. So the book maybe accelerated it a bit.  But the information was on the Web so fans could do the same work. And technology, there was just no way to ignore the fact that technology was creating data that they could go out and analyze themselves.”

Cay: Arbitrage only lasts for a small period?

Beane: “Yeah, other people catch on, even with Wall Street.  The other thing was, when my assistant came in, who was a Harvard graduate, there was now an avenue for people to come into the game who were highly intelligent. Smart people had an opportunity, and it became a meritocracy in the front office.”

“Today, the people who are running sports teams…well, I like to say, in 10 years, I won’t be employable.”

“And what really captured us about Michael, he said right away, ‘you guys are arbitraging the misevaluation of baseball players.'”

“We sort of viewed him as a resource to us as well.  And he was validating everything we did.  He became one of the guys.”

Lewis: “I just had a single question: How is this happening? And it was more than five minutes than I caught on, but it was the Wall Street story in the easy 1980s when a previously, not intellectual business, got complicated and people saw arbitrage opportunities in the market.”

“And in the course of the reporting, it became clear that other teams, especially the Boston Red Sox, had started learning what was going on.  The Boston folks tried to talk me out writing the book, and they wanted me to try and talk Billy into coming to work for them!”

“You could already see that the market was going to move, and the opportunities I identified in the book were going to go away.   So it would have been socially awkward to have thrown me out by this time.”

“So the book was all about them, and all about him [Billy], and he gets a galley, and he’s at spring training in 2003.  And he calls me, and he’s upset after reading it.  What’s disturbing you?”

“You had me saying ‘F—k’ all the time.  And I said, ‘But you do.’  And he says, ‘My mother is going to be furious!'”

“As a coda to the story, when I’m on the book tour, and I’m doing a reading in San Diego, and there’s a lady at the back, with her arms folded like this, and I thought, ‘Oh no…that’s Billy’s mother.'”

“She comes up afterwards and says, ‘My son doesn’t talk like that.’  And I covered for him.”  Lewis explained he went on to have the most awkward dinner with Beane’s mother for the next two hours.

Cay: So weren’t you concerned he wrote this blueprint for arbitrage?

Beane: “No.  Because I said to Michael, “You don’t think anybody in baseball is going to read your book, do you?”

Huge laughter from the audience.

Cay: But they did.  And the game changed…baseball changed…so how are they using analytics today in a way they weren’t 10 years ago?

Beane: “None of them are stupid enough to let Michael in so we don’t know!”

But seriously, Beane explained, “The Yankees now have 21 statisticians!”

Lewis: “Think about why that’s changed.  20 years ago, signed a player who didn’t perform, that was a $20K mistake. Now, that’s a $20M mistake.  So all the front offices have evolved and they’ve ballooned their analysis staff.”

“After the book came out, what’s amazing was how it changed. Baseball owners were getting calls from Wall Streeters, telling them they were wasting money. But the industry left to its own devices would have not changed.”

“The lesson?  If you got a business with an entrenched culture, you don’t know how entrenched it is.  There are so many disincentives to not changing what they know to what they don’t know. There’s a personal resistance to that.”

Cay: So are we seeing a generational shift in the game?

Lewis:  “Sure, all these 50 year olds have been lopped off, and all these 20 and 30 somethings are now running the game.  There’s a book entitled The Structure of Scientific Revolutions, which explains how middle aged physicists are hesitant to embrace ideas from the younger generation coming after them.”

Lewis concluded: “Progress is a funeral at a time.”

Information On Demand 2011: Steve Mills On Big Data

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Greetings from the Mandalay Bay Hotel and Convention Center in Viva Las Vegas, Nevada.

Steve Mills explains to the Information On Demand 2011 audience why "Big Data" will require new ways of working but also bring organizations new and valuable insights.

I’m pretty sure I saw Elvis in the hallway yesterday, joined by Marilyn Monroe, and they were taking pictures with IODers.

My mom would have been proud (Elvis used to write on her arm after shows at the Louisiana Hayride), but I was too busy getting my fill of big data.

Speaking of which, BBC presenter Katty Cay returned in this morning’s general session to remind us of some big data statistics, including this one: There are now over 34K Google searches per second!

And in our Information On Demand polling overnight, the most popular name at IOD 2011 was tomorrow’s keynote speaker and Moneyball author, Michael Lewis.  We’re all looking forward to his discussion with Oakland A’s manager Billy Beane.

And I, of course, will continue to root on my Texas Rangers as they go 3-2 in the World Series against the St. Louis Cardinals.

Now, enter Steve Mills on the big stage at IOD to tell us more about Big Data.

In his keynote session, Mills explained that we’re all living in a world where the reality is that the art of the possible has only been improving with the advent of new technologies.

Scott Laningham and I interviewed IBM senior vice president and group executive, Steve Mills, on a range of info management related topics, including Watson and "Moneyball." You can view this and other interviews from IOD 2011 at http://www.livestream.com/ibmsoftware

Mills recalled the days when he had to pick up extra RAM — all 128KB of it — to pick up from Endicott, NY, to deliver to IBM customers in Albany.

Nobody talks about data or RAM in terms of “Ks” anymore — these days, we’re talking petabytes.

The challenge, Mills suggested, is that we can now turn all that additional data into useful information, to hone in to identify patterns and relationships and what the data could be telling us.

It’s like mining for gold, Mills went on, but there’s a lot of dirt and rock you have to remove to get to get to the “vein.”

Mills explained that though data is increasing in volume, it’s also metamorphosing in a way: Data is no longer a static thing, but that increasingly we’re dealing with “data in motion.”  Think about traffic data, or sensor outputs from pipelines — the stream is never-ending, so the data is always moving.

There’s also the issue of variety we have to contend with, Mills explained: We’re dealing in all kinds of data types, from audio to video, and certainly no longer just numbers and text.

The big data challenge, then, is how to take advantage of all the possibilities, including high performance hardware and rich bandwidth, and pull together comprehensive solutions to enable governments and businesses to deal effectively with this new volume.

Watson, the IBM computing system that won the “Jeopardy!” match earlier in the year, is a good example of how all these different capabilities can come together. It included big data technologies like Hadoop, as well as DB2, language understanding, and an alert system that allowed Watson to iterate and improve. It was a system of elements brought together to target a specific problem.

Which is exactly what we’re doing with our customers, Mills explained.

Take Catalina Marketing, a supermarket chain that deployed real-time analysis of current transactions and past purchasing history to trigger printouts of customer specific offers — that’s some 300 million retail transactions per week, and some 195 million shipper households and 400+ billion market-based records!

The solution: IBM Netezza, which allows them to do real-time database analytics.

Or Banco Bilvao Vizcaya Argentaria (BBVA), which deployed IBM Cognos Consumer Insight based on IBM InfoSphere BigInsights and Apache Hadoop to analyze internet and social media sentiment (5.8 terabytes of data) about the bank.

Mills went through several more examples, and his message was this: No problem is the same.

There is a constant need for customization, which IBM solutions can provide.

But, patterns do emerge and you can deal with them creatively, and it does require a very broad range of technical capability up and down the line.

“Let’s have a great big data day,” Mills concluded.

Blogger’s Note: Read this blog post by Steve Mills to learn more about the opportunities and challenges presented by Big Data.

Information On Demand 2011 Breaking News: IBM Accelerates Big Data Analytics

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Today, here at Information On Demand 2011 in Las Vegas, Nevada, IBM unveiled new software that brings the power of managing and analyzing big data to the workplace.

Whether in the office or on the road, employees can now gain actionable insight anytime, anywhere from the broadest range of data and put it to work in real-time.

IBM Senior VP Steve Mills explains the "why" of business analytics at today's press conference here at Information On Demand 2011 in Las Vegas, Nevada.

The new offerings span a wide variety of big data and business analytics technologies across multiple platforms from mobile devices to the data center to IBM’s SmartCloud.

Now employees from any department inside an organization can explore unstructured data such as Twitter feeds, Facebook posts, weather data, log files, genomic data and video, and make sense of it on the fly as part of their everyday work experience.

With today’s news, IBM is placing the power of mobile analytics into the hands of iPad users with a free software download at Apple’s iTunes Store. The new software is designed to help employees in key industries such as financial services, healthcare, government, communications, retail, and travel and transportation use and benefit from business analytics on the go.

Organizations of all sizes are struggling to keep up with the rate and pace of big data and use it in a meaningful way to improve products, services, or the customer experience. 

Every day, people create the equivalent of 2.5 quintillion bytes of data from sensors, mobile devices, online transactions, and social networks; so much that 90 percent of the world’s data has been generated in the past two years.

Every month people send one billion Tweets and post 30 billion messages on Facebook. Meanwhile, more than 1 trillion mobile devices are in use today and mobile commerce is expected to reach $31 billion by 2016.

A 2010 IBM/MIT Sloan Management Review survey of 3,000 executives across 30 industries from 100 countries reveals that 60 percent of respondents said they have more data than they can effectively use.

A new IBM study of 1,700 chief marketing officers from 19 industries and 64 countries further exposes this issue with 71 percent saying their organizations are unprepared to handle the explosion of big data. 

To address these challenges, IBM is delivering new analytics and information management offerings, and skills resources to make it easier to explore and capitalize on big data:

  •  New Hadoop-based analytics software on the cloud that can be up and running in less than 30 minutes.  The new software helps employees tap into massive amounts of unstructured data from a variety of sources including social networks, mobile devices and sensors.
  • New mobile analytics software for iPad users that makes it easy to explore any type of data on the go with location-aware analytics. Clients can download the free app here: http://itunes.apple.com/us/app/ibm-cognos-mobile/id455326089?mt=8
  • New predictive analytics software with a mapping feature that can be used across industries for marketing campaigns, retail store allocation, crime prevention, and academic assessment.
  • New software that sifts through all types of data behind the scenes and ranks its quality, makes it secure, and ensures business decisions are based on trusted data.

Big Data Analytics On The Cloud

IBM InfoSphere BigInsights on the IBM SmartCloud Enterprise makes big data analytics accessible for any user inside an organization.

Like the on-premise version, BigInsights on the cloud analyzes traditional structured data found in databases along with unstructured data — such as text, video, audio, images, social media, click streams, log files, weather data — allowing decision makers to act on it quickly. Bringing big data analytics to the cloud means clients can capture and analyze any data without the need for Hadoop skills, or having to install, run, or maintain hardware and software.

BigInsights on the cloud is available in both basic and enterprise editions with the options of public, private and hybrid cloud deployments. The basic edition is an entry-level offering available at no-charge that helps organizations learn how to do big data analytics including “what-if” scenarios with its BigSheets component.

Clients can seamlessly move to the enterprise edition when ready and set up Hadoop clusters in under 30 minutes to start analyzing data with low usage rates starting at $0.60 (US) per cluster, per hour. Both versions include a developer sandbox where clients can develop a new generation of business analytics applications complete with tools and a test and development environment.

Today, market leaders in banking, insurance, retail, communications and digital entertainment are using BigInsights on the cloud to analyze massive amounts of unstructured data.

These clients are analyzing data flowing from social networks, sensors, mobile devices, log files, and voice and video systems to understand consumer sentiment, make computing networks and smart grids more secure, and create new customer experience programs.

IT professionals and students looking to build Hadoop skills can take advantage of IBM’s BigDataUniversity.com, a new web site where users can learn the basics of Hadoop, stream computing, open source software development, and database management techniques to prepare for careers as Data Scientists.

The site includes hundreds of easy-to-use tutorials, videos, and coding exercises geared to build Hadoop, BigInsights, DB2 and WebSphere skills, and many courses are free. More than 8,000  students worldwide have already registered from countries such as Brazil, Russia, China, India, Korea, and South Africa and the US.

Analytics In The Office And On The Road

IBM continues to advance business analytics for the 21st century workforce by delivering expanded mobile device support with IBM Cognos Mobile software for the iPad.

The software enables mobile workers to take their business analytics on the road whether offline or online, allowing for uninterrupted productivity. iPad users can enjoy a rich, visual business intelligence experience to analyze any data about their business including sales, customer, and financial data with reporting, dashboard and scorecards.

Cognos on iPad is designed to help employees in key industries such as financial services, healthcare, government, communications, retail, and travel and transportation use and benefit from analytics on the go.

For example, doctors and dentists can use it to analyze electronic medical records and show patients customized treatment plans and explain procedures based on that analysis; social workers can check the health and well being of children in foster homes throughout a city and update supervisors, police and courts on their status in real-time; and bankers and insurance agents can use it to analyze loan or policy data to create individual products or services for clients.

Cincinnati Zoo, one of the oldest zoos in the United States with more than 1.2 million visitors annually, uses Cognos on iPad to give management instant access, and a single view of visitor and business information to drive new revenue and improve member visits.

The flexibility of mobile business analytics allows managers to bring together sales and attendance data on their iPads from wherever they are inside the park to track purchase patterns and adjust marketing spend based on that information. Using Cognos software, the Zoo has increased in-park spending by 25 percent this year.

IBM Puts Predictive Analytics On The Map 

With today’s news, IBM is delivering new software that allows organizations to gain predictive intelligence on geographic data. Organizations can use the software to understand data, analyze trends, forecast, plan and validate assumptions to drive accurate conclusions.

SPSS Statistics 20.0 software includes a new mapping feature that gives users the ability to add a geographic dimension to analysis and reporting, and allows users to target, forecast, and plan by geographical areas.

This mapping feature can be used across industries to analyze data and create statistics for marketing campaign effectiveness, store allocation decisions in retail, to detect crime hot spots, and for student test score assessments. The software comes with views of the United States, countries, continents, and prebuilt map templates where users can quickly populate them with data including geospatial information from ESRI files.

Healthcare organizations can use the new software to visually pinpoint areas of high accident or illness rates, or identify differences in care across different regions of a state or country.

Government employees can analyze past and present census data by city block or in dense county populations, and identify high crime areas to allocate more law enforcement, or update tax and zoning changes. Direct marketers can locate their most profitable customer base and store locations to allocate advertising resources, and academia can use it to concentrate recruiting and alumni efforts geographically.

The scene from this morning's press conference at IBM Information On Demand 2011 at the Mandalay Bay in Las Vegas, Nevada.

New Software Speeds Governance of Big Data

Big data analytics can be a competitive advantage, however, the quality of the analysis is only as good as the data it’s fed, and the data itself has to be available to those who can use it.

IBM is the only vendor with a market-leading information integration and governance platform for big data that ensures only trusted information is delivered to business users and applications across the enterprise.

New IBM InfoSphere Information Server 8.7 software enables integration with Big Data as both a source and a target for information integration. The proven performance and parallel engine of Information Server provides the massive scalability required for big data. Also new in this release is a next generation connector to Netezza, built for balanced optimization and high performance, and packaged specifically for Netezza implementations, and an operations console to view system usage across all integration jobs, to improve productivity of integration projects.

New IBM InfoSphere Master Data Management 10 software unifies IBM’s market leading MDM capabilities into a single product that handles any MDM requirement. New features include integration with Business Process Management software for MDM-centric business processes, greater connectivity to consuming applications via adaptable service interfaces, and a shared matching engine that maintains the single version of the truth. MDM technology improves the outcome of Big Data analytics by providing a better understanding of customers, products, suppliers, employees and accounts for further analysis.

Clients Turn To IBM To Analyze Big Data 

With today news, IBM also announced that hundreds of new clients are turning to IBM to gain actionable insight on the broadest range of big data.

Whether it’s collecting data to manage the placement of windfarms, gauge customer sentiment on social media sites, or predict potentially fatal infections in hospitals, IBM is helping clients across every industry to put their data to work.

Clients such as Hertz, Beacon Institute, KTH Royal Institute of Technology, Marine Institute Ireland, Technovated, [x+1], TerraEchos, University of Ontario Institute of Technology and Uppsala University are using IBM analytics technologies to address the growing volume, velocity and variety of big data, and use it to make decisions that are transforming their industries.

Additional examples include:

  • Danish energy company Vestas Wind Systems is using IBM’s big data software to analyze petabytes of weather data to improve wind turbine placement for optimal energy output. Analysis that used to take weeks can now be done in under one hour.
  • XO Communications has reduced its customer churn rates by nearly 50 percent using IBM SPSS predictive analytics software. The company can predict customer behaviors, spot trends, and identify those likely to switch to another carrier, allowing them to take steps to keep their most valuable customers.
  • [x+1], an end-to-end digital marketing platform provider, is helping their clients realize a 20 percent growth in digital sales by analyzing massive volumes of advertising data in real-time using IBM Netezza technology.
  • Worldwide advertising agency Ogilvy is using IBM’s analytics software for the iPad to help employees assign resources, track utilization rates, and identify new revenue opportunities on the fly.

To read about more clients that are tackling big data challenges with IBM analytics technologies, download the new IBM Big Data Book at http://www.ibm.com/bigdata.

Follow breaking news from Information On Demand 2011 on Twitter at #iod11.

Information On Demand 2011 Live: Opening General Session — Data Puzzles

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This morning’s opening general session at Information On Demand 2011 here at the Mandalay Bay in Las Vegas, Nevada, was jam packed, both in terms of stage setting for the rest of the event, and literally in the audience, which filled up much of the Mandalay Bay Events Center.

BBC America presenter Katty Kay was our host, and opened morning’s tidings with a wonderful video and photo montage that she provided the voiceover for, explaining how IBM innovations had helped change our world over the past 100 years (remember that 2011 is the year of IBM’s centennial).

BBC America presenter Katty Kay kicks off Information On Demand 2011 this morning in Las Vegas, Nevada.

There were pictures and reminiscing about IBM’s Social Security Administration contract, the early days of data processing, the IBM 350 Ramac, the introduction of sequel databases and CICS, and even a remembrance of the floppy disk.  Ah, I remember those well.  They make great coasters.

Kay explained that the pace of computing has only accelerated, the advent of the Internet and the birth of e-business paved the way to our global connectivity, and the smarter planet initiative from IBM is poised to help companies and organizations around the globe maximize their resources and be more socially responsible.

Considering the financial meltdown of 2008, which journalist Kay didn’t gloss over, companies also need guidance and direction to help them with their analytical capabilities, particularly in an age when 1B Tweets are posted a week, and 90% of the world’s information was gathered in only the past two years.

Enter Jeff Jonas, IBM chief scientist who was most recently featured in this fascinating TV commercial about skating to where your data puck is going to be, NOT where it was five minutes ago:

IBM Chief Scientist Jeff Jonas explains the intricacies of information insight vis a vis tabletop puzzles.

Jonas explained that organizations are getting dumber as more data arrives, but that that’s not a fait accompli, that it’s not a foregone conclusion that your organization will have enterprise amnesia.

He started with the video of a blackjack dealer, who was clearly taking a stacked deck from a player, a dealer whom, if the casino had better information, would have discovered that said customer and said dealer had the same home address.  That casino would later go on to get taken for $250K!

Here’s a retail example Jonas shared: 2 out of 1,000 employees employed in retail have already been arrested for stealing at the very same store.  Yikes!

Jonas went on to explain that good information understanding and analytics means context: Better understanding something by taking into account the things around it.

He used, as his example, a series of jigsaw puzzles he had his girlfriend’s kids put together, but having strategically taken some of the pieces of each out.  Even with the missing pieces, the kids were able to get a picture of what the puzzles were about, revealing necessary context.  But until you take the pieces to the table and attempt assembly, Jonas explained, you don’t know what you’re dealing with.

And that’s where many organizations are today.

Incremental Context provides incremental discovery, and given sufficient observations, there can come a tipping point when confidence improves while computational effort decreases.

Put that in your database pipe and smoke it!

Next, IBM’s managing partner of financial services, IBM Global Business Services, Sarah Diamond took the stage to explain how dire the banking situation was in 2008-2010.  The number of bank failures went from 25 in 2008, to over 157 in 2010.

Eventually, the IMF calculated that the banks wrote off $2.2 trillion in toxic assets and bad loans, and emerging from that mess many banks recognized their need to change, to respond to pressure from regulators and their public constituents to better manage risk, and their overall businesses.

IBM customer SunTrust, was one of those banks which were better prepared for the crisis than many, in that they had put together a risk analytics and governance regime before the crisis occurred.

As their senior VP of risk technology explained, SunTrust went from standardized reporting to client-centric reports delivered via intranet portals in just a few short years, and when the financial crisis hit, SunTrust was prepared to know exactly what their exposures were, what they consisted of, and most importantly, who was on the other end of the transaction.

This allowed them to report to senior management on a daily basis at 7 am exactly what their positions were based on their trading partners’ exposures.

If only all banks had been so well prepared.

Written by turbotodd

October 24, 2011 at 5:59 pm

Information On Demand 2011: Let The Interviews Begin!

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Scott and I are well into our Sunday afternoon interviews, and we’ve had a couple of great sessions already discussing key themes emerging both at the conference and in the broader information management landscape.

We discussed the opportunities and challenges brought about by predictive analytics, and the unique requirements for establishing an effective information governance regime, and how to sell it into your organization.

Keep an eye for those interviews on our LiveStream channel, which you can reach at www.livestream.com/ibmsoftware

Also, remember you can follow all the action via the social media, including via the Twitter hashtags #iod11, #iod2011, and #iodgc2011, and as well on YouTube at www.youtube.com/user/ibmbusinessanalytics.

If you want to drink from the information firehouse and see the full social media stream, visit ibm.co/iodsocial.

Information on Demand 2011: Big Data, Bigger Insights

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Greetings from Viva Las Vegas, Nevada.

The CNN Republican debate is long over, the media circus is over, and the information gatherers for IBM Information on Demand 2011 are arriving en masse.

My Webcasting partner-in-crime, Scott Laningham, and I arrived here yesterday mostly without incident. We scoped out the situation, and decided that the Mandalay Bay Race and Sports Book was the perfect venue to sit down, have a burger, and watch the third game of the World Series.

Since baseball and data are going to be an underlying theme in Michael Lewis and Billy Beanes’ keynote about Moneyball later this week, it only seemed appropriate.

And though my Texas Rangers ended up taking a beating, we did witness some new data added to the baseball history books: The Cardinals’ Albert Pujols tied Babe Ruth and Reggie Jackson for the most home runs struck in one game of a World Series, the magic number three (to be precise, the Babe did it twice).

And though you may never be able to fully predict the specific outcome of a single baseball game, Billy Beane and his Oakland A’s team proved that you can use past player performance statistics to help build a better team, one that could compete with the “big money” teams.

Okay, so if past prediction can help prove future performance, where does that leave we Information On Demanders for this 2011 event?

Let’s start with the business benefit, which in these tough times are necessary for even the most profitable of enterprises.

IBM studies have demonstrated that the performance gap between those leaders and the laggards and followers is widening: Organizations that apply advanced analytics have 33% more revenue growth and 12X more profit growth.

That ought to get some executive attention.

But we’re also seeing some major shifts in the external environment. Information is exploding. We’ve now got over 1 trillion devices connected to the Internet, and we’re expecting 44X digital data growth through 2020.

And yet we’re also finding that business change is outpacing our ability to keep up with it all: 60% of CEOs agree they have more data than they can use effectively, and yet 4 out of 5 business leaders see information as a vital source of competitive advantage.

So what’s the remedy? Well, those flying in to Vegas have taken the first step, admitting they have a problem (No, not “The Hangover” type problems — you’ll have to talk to Mike Tyson about those).

No, successful organizations are turning all that data into actionable insight by taking a more structured approach through business analytics and optimization (BAO).

They’re embracing it as a transformational imperative, and demonstrating that they can improve visibility throughout the enterprise, enhance their understanding of their customers, and fostering collaborative decision-making while providing those key predictive insights and optimizing real-time decision making.

So, like a good baseball player, or manager, your job over the next several days here in Vegas is to do a few key things, and do them well.

Focus, keep your eye on the ball and on the topics most important and relevant to you.

Listen, including both in the general sessions and individual tracks, but also in those all important hallway conversations — you never know what you might learn.

Participate, particularly in the social media. We IBMers and our key partners want to hear from you, and we’re only a Tweet away. Use conference hashtag #iodgc2011 to speak up, as we’re listening in return.

Commit, to the actions coming out of the event that you think will be helpful to you and your organization, and to bring those business and technology goals into becoming a reality.

And one other thing…have fun! Whatever happens in Vegas may not stay in Vegas…it may even end up on Facebook…but that shouldn’t stop you from having a good time and learning a lot this week.

As for Scott Laningham and myself, we’ll be blogging and covering key sessions, and “livestreaming” from the Expo floor. Stop by and say hello.

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