How Big Data Powers GameStop to Gain Retail Advantage and Deep Insights into its Markets

Transcript of a BriefingsDirect discussion on how a gaming retailer uses big data to gather insights into sales trends and customer wants and needs.

Listen to the podcast. Find it on iTunes. Get the mobile app for iOS or Android. Download the transcript. Sponsor: HP.

Dana Gardner: Hello, and welcome to the next edition of the HP Discover Podcast Series. I'm Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator for this ongoing sponsored discussion on IT innovation and how it’s making an impact on people’s lives.

Gardner

Once again, we're focusing on how companies are adapting to the new style of IT to improve IT performance and deliver better user experiences, as well as better business results.

Our next innovation case study interview highlights how GameStop, based in Grapevine, Texas uses big data to improve how it conducts its business and serve its customers. To learn more about how they deploy big data and use the resulting analytics, we are joined by John Crossen, Data Warehouse Lead at GameStop. Welcome, John.

John Crossen: Thank you for having me.

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Gardner: Tell us a little bit about GameStop. Most people are probably familiar with the retail outlets that they see, where you can buy, rent, trade games, and learn more about games. Why is big data important to your organization?

Crossen: We wanted to get a better idea of who our customers are, how we can better serve our customers and what types of needs they may have. With prior reporting, we would get good overall views of here’s how the company is doing or here’s how a particular game series is selling, but we weren’t able to tie that to activities of individual customers and possible future activity of future customers, using more of a traditional SQL-based platform that would just deliver flat reports.

Crossen

So, our goal was to get s more 360-degree view of our customer and we realized pretty quickly that, using our existing toolsets and methodologies, that wasn’t going to be possible. That’s where Vertica ended up coming into play to drive us in that direction.

Gardner: Just so we have a sense of this scale here, how many retail outlets does GameStop support and where are you located?

Crossen:  We're international. There are approximately 4,200 stores in the US and another 2,200 international.

Gardner: And in terms of the type of data that you are acquiring, is this all internal data or do you go to external data sources and how do you to bring that together?

Internal data

Crossen: It's primarily internal data. We get data from our website. We have the PowerUp Rewards program that customers can choose to join, and we have data from individual cash registers and all those stores.

Gardner: I know from experience in my own family that gaming is a very fast-moving industry. We’ve quickly gone from different platforms to different game types and different technologies when we're interacting with the games.

It's a very dynamic changeable landscape for the users, as well as, of course, the providers of games. You are sort of in the middle. You're right between the users and the vendors. You must be very important to the whole ecosystem.

Crossen: Most definitely, and there aren’t really many game retailers left anymore. GameStop is certainly the preeminent one. So a lot of customers come not just to purchase a game, but get information from store associates. We have Game Informer Magazine that people like to read and we have content on the website as well.

Gardner: Now that you know where to get the data and you have the data, how big is it? How difficult is it to manage? Are you looking for real-time or batch? How do you then move forward from that data to some business outcome?

Crossen: It’s primarily batch at this point. The registers close at night, and we get data from registers and loads that into HP Vertica. When we started approximately two years ago, we didn't have a single byte in Vertica. Now, we have pretty close to 24 terabytes of data. It's primarily customer data on individual customers, as well Weblogs or mobile application data.

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Gardner: I should think that when you analyze which games are being bought, which ones are being traded, which ones are price-sensitive and move at a certain price or not, you're really at the vanguard of knowing the trends in the gaming industry -- even perhaps before anyone else. How has that worked for you, and what are you finding?

Crossen: A lot of it is just based on determining who is likely to buy which series of games. So you won't market the next Call of Duty 3 or something like that to somebody who's buying your children's games. We are not going to ask people buy Call of Duty 3, rather than My Little Pony 6.

The interesting thing, at least with games and video game systems, is that when we sell them new, there's no price movement. Every game is the same price in any store. So we have to rely on other things like customer service and getting information to the customer to drive game sales. Used games are a bit of a different story.

Gardner: Now back to Vertica. Given that you've been using this for a few years and you have such a substantial data lake, what is it about Vertica that works for you? What are learning here at the conference that intrigues you about the future?

Quick reports

Crossen: The initial push with HP Vertica was just to get reports fast. We had processes that literally took a day to run to accumulate data. Now, in Vertica, we can pull that same data out in five minutes. I think that if we spend a little bit more time, we could probably get it faster than half of that.

The first big push was just speed. The second wave after that was bringing in data sources that were unattainable before, like web-click data, a tremendous amount of data, loading that into SQL, and then being able to query it out of SQL. This wasn't doable before, and it’s made it do that. At first, it was faster data, then acquiring new data and finding different ways to tie different data elements together that we haven’t done before.

Gardner: How about visualization of these reports? How do you serve up those reports and do you make your inference and analytics outputs available to all your employees? How do you distribute it? Is there sort of an innovation curve that you're following in terms of what they do with that data?

We had processes that literally took a day to run to accumulate data. Now, in Vertica, we can pull that same data out in five minutes.

Crossen: As far as a platform, we use Tableau as our visualization tool. We’ve used a kind of an ad-hoc environment to write direct SQL queries to pull data out, but Tableau serves the primary tool.

Gardner: In that data input area, what integration technologies are you interested in? What would you like to see HP do differently? Are you happy with the way SQL, Vertica, Hadoop, and other technologies are coming together? Where would you like to see that go?

Crossen: A lot of our source systems are either SQL-server based or just flat files. For flat files, we use the Copy Command to bring data, and that’s very fast. With Vertica 7, they released the Microsoft SQL Connector.

So we're able to use our existing SQL Server Integration Services (SSIS) data flows and change the output from another SQL table to direct me into Vertica. It uses the Copy Command under the covers and that’s been a major improvement. Before that, we had to stage the data somewhere else and then use the Copy Command to bring it in or try to use Open Database Connectivity (ODBC) to bring it in, which wasn’t very efficient.

20/20 hindsight

Gardner: How about words of wisdom from your 20/20 hindsight? Others are also thinking about moving from a standard relational database environment towards big data stores for analytics and speed and velocity of their reports. Any advice you might offer organizations as they're making that transition, now that you’ve done it?

Crossen: Just to better understand how a column-store database works, and how that's different from a traditional row-based database. It's a different mindset, everything from how you are going to lay out data modeling.

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For example, in a row database you would tend to freak out if you had a 700-column table. In the column stores, that doesn’t really matter. So just to get in the right mindset of here’s how a column-store database works, and not try to duplicate row-based system in the column-store system.

Gardner: Great. I am afraid we’ll have to leave it there. I’d like to thank our guest, John Crossen, the Data Warehouse Lead at GameStop in Grapevine, Texas. I appreciate your input.

Crossen: Thank you.

Gardner: And also thank to our audience for joining us for this special new style of IT discussion. I'm Dana Gardner, Principal Analyst at Interarbor Solutions, your host for this ongoing series of HP-sponsored discussions. Thanks again for listening, and come back next time.

Listen to the podcast. Find it on iTunes. Get the mobile app for iOS or Android. Download the transcript. Sponsor: HP.

Transcript of a BriefingsDirect discussion on how a gaming retailer uses big data to gather insights into sales trends and customer wants and needs. Copyright Interarbor Solutions, LLC, 2005-2015. All rights reserved.

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RealTime Medicare Data Delivers Caregiver Trends Insights By Taming its Huge Healthcare Data Trove

Transcript of a BriefingsDirect podcast on how a healthcare data collection site met the challenge of increasing volumes by using HP tools.

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: HP.

Dana Gardner: Hello, and welcome to the next edition of the HP Discover Podcast Series. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator for this ongoing sponsored discussion on IT innovation and how it’s making an impact on people’s lives.

Gardner

Once again, we're focusing on how companies are adapting to the new style of IT to improve IT performance and deliver better user experiences, as well as better business results.

This time, we're coming to you directly from the recent HP Discover 2014 Conference in Las Vegas. We're here to learn directly from IT and business leaders alike how big data, cloud, and converged infrastructure implementations are supporting their goals.
   
Our next innovation case study interview highlights how RealTime Medicare Data analyzes huge volumes of Medicare data and provides analysis to their many customers on the caregiver side of the healthcare sector.

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Here to explain how they manage such large data requirements for quality, speed, and volume, we're joined by Scott Hannon, CIO of RealTime Medicare Data and he's based in Birmingham, Alabama. Welcome, Scott.

Scott Hannon: Thank you.

Gardner:  First, tell us a bit about your organization and some of the major requirements you have from an IT perspective.

Hannon: RealTime Medicare Data has full census Medicare, which includes Part A and Part B, and we do analysis on this data. We provide reports that are in a web-based tool to our customers who are typically acute care organizations, such as hospitals. We also do have a product that provides analysis specific to physicians and their billing practices.

Gardner:  And, of course, Medicare is a very large US government program to provide health insurance to the elderly and other qualifying individuals.

Hannon: Yes, that’s true.

Gardner: So what sorts of data requirements have you had? Is this a volume, a velocity, a variety type of the problem, all the above?

Volume problem

Hannon: It’s been mostly a volume problem, because we're actually a very small company. There are only three of us in the IT department, but it was just me as the IT department, back when I started in 2007.

Hannon

At that time, we had one state, Alabama and then, we began to grow. We grew to seven states which was the South region: Florida, Georgia, Tennessee, Alabama, Louisiana, Arkansas, and Mississippi. We found that Microsoft SQL Server was not really going to handle the type of queries that we did with the volume of data.

Currently we have 18 states. We're loading about a terabyte of data per year, which is about 630 million claims and our database currently houses about 3.7 billion claims.

Gardner: That is some serious volume of data. From the analytics side, what sort of reporting do you do on that data, who gets it, and what are some of their requirements in terms of how they like to get strategic benefit from this analysis.

Hannon: Currently, most of our customers are general acute-care hospitals. We have a web-based tool that has reports in it. We provide reports that start at the physician level. We have reports that start at the provider level. We have reports that you can look at by state.

This allows them to look not only at themselves, but to compare themselves to other places, like their market, the region, and the state.

The other great thing about our product is that typically providers have data on themselves, but they can't really compare themselves to the providers in their market or state or region. So this allows them to look not only at themselves, but to compare themselves to other places, like their market, the region, and the state.

Gardner: I should think that’s hugely important, given that Medicare is a very large portion of funding for many of these organizations in terms of their revenue. Knowing what the market does and how they compare to it is essential.

Hannon: Typically, for a hospital, about 40 to 45 percent of their revenue depends on Medicare. The other thing that we've found is that most physicians don't change how they practice medicine based on whether it’s a Medicare patient, a Blue Cross patient, or whoever their private insurance is.

So the insights that they gain by looking at our reports are pretty much 90 to 95 percent of how their business is going to be running.

Gardner: It's definitely mission-critical data then. So you started with a relational database, using standard off-the-shelf products. You grew rapidly, and your volume issues grew. Tell us what the problems were and what requirements you had that led you to seek an alternative.

Exponential increase

Hannon: There were a couple of problems. One, obviously, was the volume. We found that we had to increase the indexes exponentially, because we're talking about 95 percent reads here on the database. As I said, the Microsoft SQL Server really was not able to handle that volume as we expanded.

The first thing we tried was to move to an analysis services back end. For that project, we got an outside party to help us because we would need to redesign our front end completely to be able to query analysis services.

It just so happened that that project was taking way too long to implement. I started looking at other alternatives and, just by pure research, I happened to find Vertica. I was reading about it and thought "I'm not sure how this is even possible." It didn’t even seem possible to be able to do this with this amount of data.

So we got a trial of it. I started using it and was impressed that it actually could do what it said it could do.

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Gardner: As I understand it, Vertica has the column store architecture. Was that something understood? What is it about the difference of the Vertica approach to data -- one that perhaps caught your attention at first, and how has that worked out for you?

Hannon: To me the biggest advantages were the fact that it uses the standard SQL query language, so I wouldn't have to learn the MDX, which is required with the analysis services. I don’t understand the complete technical details about column storage, but I understand that it's much faster and that it doesn't have to look at every single row. It can build the actual data set much faster, which gives you much better performance on the front end.

Gardner: And what sort of performance have you had?

Hannon: Typically we have seen about a tenfold decrease in actual query performance time. Before, when we would run reports, it would take about 20 minutes. Now, they take roughly two minutes. We're very happy about that.

Gardner: How long has it been since you implemented HP Vertica and what are some of supporting infrastructures that you've relied on?

Hannon: We implemented Vertica back in 2010. We ended up still utilizing the Microsoft SQL Server as a querying agent, because it was much easier to continue to interface the SQL reporting services, which is what our web-based product uses. And the stored procedure functionality that was in there and also the open query feature.

So we just pull the data directly from Vertica and then send it through Microsoft SQL Server to the reporting services engine.

New tools

Gardner: I've heard from many organizations that not only has this been a speed and volume issue, but there's been an ability to bring new tools to the process. Have you changed any of the tooling that you've used for analysis? How have you gone about creating your custom reports?

Hannon: We really haven't changed the reports themselves. It's just that I know when I design a query to pull a specific set of data that I don’t have to worry that it's going to take me 20 minutes to get some data back. I'm not saying that in Vertica every query is 30 seconds, but the majority of the queries that I do use don’t take that long to bring the data back. It’s much improved over the previous solution that we were using.

Gardner: Are there any other quality issues, other than just raw speeds and feeds issues, that you've encountered? What are some of the paybacks you've gotten as a result of this architecture?

But I will tell people to not be afraid of Linux, because Vertica runs on Linux and it’s easy.

Hannon: First of all, I want to say that I didn’t have a lot of experience with Unix or Linux on the back end and I was a little bit rusty on what experience I did have. But I will tell people to not be afraid of Linux, because Vertica runs on Linux and it’s easy. Most of the time, I don’t even have to mess with it.

So now that that's out of the way, some of the biggest advantages of Vertica is the fact that you can expand to multiple nodes to handle the load if you've got a larger client base. It’s very simple. You basically just install commodity hardware, but whatever flavor of Unix or Linux that you prefer, as long as it’s compatible, the installation does all the rest for you, as long as you tell it you're doing multiple nodes.

The other thing is the fact that you have multiple nodes that allow for fault tolerance. That was something that we really didn't have with our previous solution. Now we have fault tolerance and load balancing.

Gardner: Any lessons learned, as you made this transition from a SQL database to a Vertica columnar store database? You even moved the platform from Windows to Linux. What might you tell others who are pursuing a shift in their data strategy because they're heading somewhere else?

Jump right in

Hannon: As I said before, don’t be afraid of Linux. If you're a Microsoft or a Mac shop, just don’t be afraid to jump in. Go get the free community edition or talk to a salesperson and try it out. You won't be disappointed. Since the time we started using it, they have made multiple improvements to the product.

The other thing that I learned was that with OPENQUERY, there are specific ways that you have to write the store procedures. I like to call it "single-quote hell," because when you write OPENQUERY and you have to quote something, there are a lot of other additional single quotes that you have put in there. I learned that there was a second way of doing it that lessened that impact.

Gardner: Okay, good. And we're here at HP Discover. What's interesting for you to learn here at the show and how does that align with what your next steps are in your evolution?

Hannon:  I'm definitely interested in seeing all the other capabilities that Vertica has and seeing how other people are using it in their industry and for their customers.

I'm definitely interested in seeing all the other capabilities that Vertica has and seeing how other people are using it in their industry and for their customers.

Gardner: In terms of your deployment, are you strictly on-premises for the foreseeable future? Do you have any interest in pursuing a hybrid or cloud-based deployments for any of your data services?

Hannon: We actually use a private cloud, which is hosted at TekLinks in Birmingham. We've been that way ever since we started, and that seems to work well for us, because we basically just rent rack space and provide our own equipment. They have the battery backup, power backup generators, and cooling.

Gardner: How about backup and recovery? How were those issues managed for you?

Hannon: We have multiple copies of it on multiple server systems and we also do cloud backup.

Gardner: I see. So you've got a separate location in the cloud that you use, should something unfortunate happen.

Hannon: Correct.

Gardner: So a good insurance for a Medicare insurance database.

Hannon: Absolutely.

Gardner: Okay. We’ll leave it there. Please join me in thanking our guest. We've been talking about how RealTime Medicare Data is managing a huge volume of data and providing analysis to care providers in 18 states in the US.

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So a big thank you to Scott Hannon, CIO at RealTime Medicare Data in Birmingham, Alabama. Thanks.

Hannon: Thank you, Dana.

Gardner: And thanks also to our audience for joining us for this special new style of IT discussion coming to you directly from the recent HP Discover 2014 Conference in Las Vegas.

I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host for this ongoing series of HP sponsored discussions. Thanks again for listening and come back next time.

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: HP.

Transcript of a BriefingsDirect podcast on how a healthcare data collection site met the challenge of increasing volumes by using HP tools. Copyright Interarbor Solutions, LLC, 2005-2015. All rights reserved.

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GSN Games Wins Big Using HP Vertica to Uncover Deep Insights into User Preferences

Transcript of a BriefingsDirect podcast on how big data and instant analysis can provide valuable feedback on entertainment company user preferences.

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: HP.

Dana Gardner: Hello, and welcome to the next edition of the HP Discover Podcast Series. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator for this ongoing sponsored discussion on IT innovation and how it’s making an impact on people’s lives.

Gardner

Once again, we’re focusing on how companies are adapting to the new style of IT to improve IT performance, deliver better user experiences, and stronger business results. We’re coming to you directly from the recent HP Discover Conference in Barcelona.

We’re here to learn directly from IT and business leaders alike how big data, mobile, and cloud, along with converged infrastructure, are all supporting their IT goals.

Our next innovation case study interview highlights how GSN Games is using big data to uncover more information to produce and deliver improved entertainment for their audience. Please welcome our guest, Portman Wills, Vice President of Data at GSN Games in San Francisco. Welcome, Portman.

Portman Wills: Hi. Nice to be here.

Gardner: Tell us about GSN Games. What do you do, and who plays these games?

Wills: GSN started as a cable network in the U.S. We’re distributed in 80 million households as the Game Show Network, and then we also have a digital wing that produces casual and social games on Facebook, web, tablets, and mobile. That division has 110 million registered game-players. My team takes data from all over those worlds, throws them into a big data warehouse, and starts trying to find trends and insights for both our TV audience and our online game-players.

Wills

In terms of the games, which is really where the growth is, our core demographic is older females, believe it or not, who love playing casual games. We skew more in the 55-plus age range, and we have players from all over the world.

Because we’re here in Spain, a quick tidbit that we uncovered recently is that our main time-frame in every country on Earth, when people play games, is 7 p.m. to 11 p.m., except in Spain where it’s 1 p.m. to 3 p.m. -- siesta time. That’s just one of the examples of how we use big data to use discover insights about our players and our audiences worldwide.

Understanding the audience

Gardner: I have to imagine that the data that led you to that inference in Spain was something other than what we might consider typical structured data. How did the different data brought together allow you to understand your Spanish audience better?

Wills: We use this product from HP called Vertica, which is just a tremendous data warehouse, that lets us throw every single click, touch, or swipe in all of our games into a big table. By big, I mean right now it’s I think 1.3 trillion rows. We keep saying that we should really archive this thing. Then, we say we’ll archive it when it slows down, and then it just never slows down, so we have yet to archive it.

We put all of the click stream data in there. The traditional joins, schemas, and all of that don’t really have to happen because we have one table with all of the interactions. You have the device, the country, the player, all these attributes. It’s a very wide table. So if you want to do things like ask what is the usage in five-minute slices by country, it’s a simple SQL query, and you get your results.

Gardner: The word “games” means a lot of different things to a lot of people. We’re talking about a heritage of network television games back in the ’60s and ’70s that have led us to what is now your organization. But what sort of newer games are we talking about, and what proportion of them are online games, versus more of the passive watching like that on a cable or other media outlets?

Wills: Originally, when our games division started as a branch of GSN, it was companion games to Wheel of Fortune, Minute to Win It, whatever the hot game show was. That's still a part of it, but the growth in the last few years has been in social games on Facebook, where a lot of our games are more casual titles and have nothing to do with the game shows -- tile-matching games or solitaire games, for example.

In the last year or year-and-a-half for us, like everyone else, there’s been this explosion in mobile.

Then, in the last year or year-and-a-half for us, like everyone else, there’s been this explosion in mobile. So it’s iPad, Android, and iPhone games, and there we have the solitaires and the tile matching, too.

Increasingly, a lot of our success and growth has come from virtual casino games. People are playing Bingo, video poker, even slots, virtual slots. We have this title called GSN Casino. That’s an umbrella app with a lot of mini games that are casino-themed, and that one has really just exploded really in the last six months. It's a long way from the Point A of Family Feud reruns to the Point Z of virtual slot machines, but hopefully you can see how we got there.

Gardner: It seems like a long distance, but it’s been also a fairly short amount of time. It wasn't that long ago that the information you might have in your audience came through Nielsen for passive audiences, and you had basically a one- or two-dimension view of that individual, based on the estimate of what time was devoted to a show. But now, with the mobile devices in particular, you have a plethora of data.

Tell us about the types of data that you can get, and what volumes are we talking about.

Mobile experience

Wills: Let’s take mobile, because I think it's easy to grok. Everything about the device is exposed to us. The fact that you’re playing on an iPad Mini Retina versus an iPad 1 tells us a lot about you, whether you know it or not.

Then, a lot of our users sign-in via Facebook, which is another vector for information. If you sign-in via Facebook, Facebook provides us your age range, gender, some granular location information. For every player, we get between 40 and 50 dimensions of data about that player or about that device.

That’s one bucket. But the actual gameplay is another whole bucket. What games do you choose to play in our catalog? How long do you play them? What time of day do you play them? Those start to classify users into various buckets -- from the casual commute player, who plays for 15 minutes every morning and afternoon, to the hard-core player who spends 8 to 10 hours a day, believe it or not, playing our games on their mobile devices.

Mobile doesn’t necessarily mean mobile, like out and about. A lot of our players are on their iPad, sitting on the couch in their home.

At that point, and this is a little bit of a pet peeve of mine, mobile doesn’t necessarily mean mobile, like out and about. A lot of our players are on their iPad, sitting on the couch in their home.

It’s not mobility. They’re not using 3G. They’re not using augmented reality. It’s just a device that happens to be a very convenient device for playing games. So it’s much more of a laptop replacement than any sort of mobile thing. That’s sort of a side track.

We collect all of this data, and it’s a fair amount. Right now, we’re generating about 900 million events per day across all of our players. That’s all streamed into our HP Vertica data warehouse, and there are a few tables, event time series tables, that we put the stuff into. A small table for us would be a few hundred billion records, and a large table, as I said, is 1.3 trillion records right now.

So the scale is big for us. I know that for other companies that seems like peanuts. It’s funny how big data is so broad. What’s big to one person is tiny to someone else, but this is the world that we’re dealing in right now.

We have 110 million players. Thankfully, not all of them are active at one time. That would be really big data. But we will have about 20 million at any given time in peak time playing concurrently. That’s a little bit about the numbers in our data warehouse.

Gardner: Understanding your audience through this data is something fairly new. Before, you couldn’t get this amount of data. Now that you have it, what is it able to do for you? Are you crafting new games based on your findings? Are you finding information that you can deliver back to a marketer or advertiser that links them to the audience better? There must be many things you can do.

No advertising

Wills: First of all, we don’t do any advertising in our mobile games. So that’s one piece that we’re not doing, although I know others are. But there are two broad buckets in which we use data. The first is that we run a lot of the A/B tests, experiments. All of our games are constantly being multivariate tested with different versions of that same game in the field.

We run 20 to 40 tests per week. As an example, we have a Wheel of Fortune game that we recently released, and there was all this debate about the difficulty of the puzzles. How hard should the puzzles be? Should they be very obscure pieces of Eastern literature, mainstream pop culture, or even easier?

So, we tested different levels of difficulty. Some players got the easy, some players got the medium, and some players got the hard ones. We can measure the return rate, the session duration, and the monetization for people who buy power-ups, and we see which level of difficulty performs the best. In the first test of easy, medium, hard, easy overwhelmingly did the best.

So we generated a whole bunch of new puzzles that were even easier than were the previous easy ones and tested that against what was now the control level. The easier puzzles won again. So we generated a whole new set of puzzles that were absurdly easy. We were trying to prove the point that if we gave Wheel of Fortune puzzles that are four-letter words like “bird” and “cups,” nobody would enjoy playing something that simplistic.

Well it turns that they do -- surprise, surprise -- and so that’s how we evolved into a version of Wheel of Fortune that, compared to the game show, looks very different, but it’s actually what customers want. It’s what players want. They want to relax and solve simple puzzles like “door.”

Hopefully faster than overnight. Overnight is a little too slow these days.

Gardner: So Vertica analysis determined that everyone is a winner on GSN, but you’re able to do real-time focus-group types of activities. The data -- because it's so fast, because there is so much information available and you can deal with it so quickly -- means that you’re able to tune your games to the audience virtually overnight.

Wills: Hopefully faster than overnight. Overnight is a little too slow these days. We push twice a day both to our platform code and updates to all of our games in the morning around 11 a.m and in the afternoon around 3:30. Each one of those releases is based on the data that came from the prior release.

So we're constantly evolving these games. I want to go back to your previous question, because I only got to talk about one bucket, which is this experimentation. The other bucket is using the usage patterns that customers have to evolve our product in ways that aren’t necessarily structured around an A/B test.

We thought when we launched our iPhone app that there would be a lot of commuting usage. We had in our head this hypothetical bus player, who plays on the bus in the morning. And so we thought we would build all the stuff around daily patterns. We built this daily return bonus that you can do in the morning and then again in the evening.

The data showed us that that really was only a tiny fraction of our players. There were, in fact, very few players who had this bimodal, morning and evening usage pattern. Most people didn't play at all until after dinner and then they would play a lot, sometimes even binge from 7 p.m. until 2 a.m. on games.

False assumptions

That was an area where we didn't even set up an experiment. We just had false assumptions about our player base. And that happens a surprising amount of the time. We all -- especially the game-design team and people who spent their careers designing video games -- have assumptions about their audience that half the time are just wrong. One of the things we use data for is to challenge all of our assumptions about our own products and our own businesses.

It's really gotten to a point where it's almost religious in our company. The moment two people start debating what should or shouldn't happen, they say, “Well let's just let the data decide.” That's been a core change not just for us, but for the game industry as a whole.

Gardner: I expect that to be a change, too, across many more industries. What you’re describing is very much desired by a lot of types of businesses through understanding a massive amount of data from their audience, to be able to react quickly to that, and then to stop guessing about products and pricing and distribution and logistics and supply chain and be driven purely by the data. You’re a really interesting harbinger of things to come.

One of the things we use data for is to challenge all of our assumptions about our own products and our own businesses.

Portman, tell me little bit about the process by which you were able to do this. Did you have an older data warehouse? What did you use before, and how did you make a transition to HP Vertica?

Wills: When we started the social mobile business three years ago, we were on MySQL, which we are still on for our transactional load. We have three data centers around the world. When people are playing our games, it’s recording, reading, and writing 125,000 transactions per second, and that MySQL, sharded out, works great for that.

When you want to look at your entire player base and do a cross-shard query, we found that MySQL really fell down. Our original Vertica proof of concept (POC) was just to replace these A/B test queries, which have to look across the entire population.

So in comes Vertica. We set up a single node, a Vertica data warehouse. We pull in a year's worth of data, and the same query to synthesize these sessions ran in 800 milliseconds.

So the thing that took 24 hours, which is 86,400 seconds, ran in less than one second. By the way, that 24-hour query was running across dozens of machines, and this Vertica query was running on a single server of commodity hardware.

That's when we really became believers in the power of the column store and column-oriented data warehouses. From the small beginning of just one simple query, it’s now expanded -- and pretty much our whole business runs on top of HP Vertica on the data warehouse side.

Lessons learned

Gardner: As I said, I think GSN Games is a really harbinger of what a lot of other companies in many different vertical industries will be seeking. Do you have any thoughts in terms of lessons learned, as you progressed over the past three years to this size of a data set, to this level of inference, that you can deliver to virtually everyone in your company?

Looking back, if you had to do it again, what might you have done differently or what suggestions might you have for others who would like to be able to do what you are doing?

Wills: I definitely wish that we had switched to a column store sooner. I think the reason that we've been so successful at this is because of our game design team, which was so open to using data.

I definitely wish that we had switched to a column store sooner.

I’ve heard hard stories from other companies where they want to use a data-driven approach, and there's just a lot of cultural inertia and push back against doing that. It's hard to be consistently proven wrong in your job, which is always what happens when you rely on data.

The real thing that's helped us get to the point we are in is a culture and a company where everybody is open to being wrong -- and open to being proven wrong by the data, which I am very thankful for.

Gardner: Well, it's good to be data-driven, and I think you should feel good being responsible for making 110 million people feel good about themselves every day.

I'm afraid we will have to leave it there. We've been talking about how GSN Games is using HP Vertica to gather amazing insights and go beyond instinct and intuition into more of a science for their audiences' benefit -- and for their business’s benefit.

I would like to thank our guest, Portman Wills, Vice President of Data at GSN Games in San Francisco. Thank you, sir.

Wills: Thank you.

Gardner: And thank you to our audience as well for joining us for this special new style of IT discussion, coming to you directly from the recent HP Discover Conference in Barcelona.

I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host for this ongoing series of HP-sponsored discussions. Thanks again for listening, and come back next time.

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: HP.

Transcript of a BriefingsDirect podcast on how big data and instant analysis can provide valuable feedback on entertainment company user preferences. Copyright Interarbor Solutions, LLC, 2005-2014. All rights reserved.

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