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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: 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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