Transcript of a Briefings Direct discussion on how gaming companies can gain a competitive advantage in grabbing and analyzing data in near real time.

1. In Building a Culture of Big Data, Zynga Grabs Huge
Volumes and Makes the Analytics Open to All Employees
to Pursue an Innovation Edge
Transcript of a Briefings Direct podcast on how gaming companies can gain a competitive
advantage in grabbing and analyzing data in near real time.
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. 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.
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 Zynga in San Francisco is depending on
big-data analytics to improve how it does its business and provide its gaming services to its
customers.
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To learn more about how big data impacts Zynga in the gaming industry, please join me now in
welcoming our guest. We're here with Joanne Ho, a Senior Engineering Manager at Zynga.
Welcome, Joanne.
Joanne Ho: Hi.
Gardner: And also, Yuko Yamazaki, Head of Analytics at Zynga. Welcome,
Yuko.
Yuko Yamazaki: Thank you.
Gardner: Let’s start with you, Joanne. Tell us a little bit about what Zynga needs to do with big
data in order to improve your business. How important is big data analytics to you as an
organization?
Gardner

2. Ho: To Zynga, big data is very important. It's a main piece of the company and as a part of the
analytics department, big data is serving the entire company as a source of understanding our
users' behavior, our players, what they like, and what they don’t like about games. We are using
this data to analyze the user’s behavior and we also will personalize a lot of different game
models that fit the user’s player pattern.
Gardner: What’s interesting to me about games is the people will download them. They're also
upgradable and they are changeable. People can move so that the feedback loop between the
inferences, information, and analysis you gain, and then what you can do with it is rather
compressed, compared to many other industries.
What is it that you're able to do in this rapid-fire development and in release? How is that
responsiveness important to you?
Real-time analysis
Ho: Real-time analysis, of course, is critical, and we have our streaming system that can do it.
We have our monitoring and alerting system that can alert us whenever we see
any drops in user’s install rating, or any daily active users (DAU). The game
studio will be alerted and they will take appropriate action on that.
Gardner: Yuko, what sort of datasets we are talking about? If we're going to the
social realm, we can get some very large datasets or you might have actual data
that’s coming directly from your apps. What's the volume and scale we're talking
about here?
Yamazaki: We get data of everything that happens in our games. Almost every single play gets
tracked into our system. We're talking about 40 billion to 60 billion rows a day, and that's the
data that our game product managers and development engineers decide what they want to
analyze later. So it’s already being structured and compressed and comes into our database.
Gardner: That’s a very impressive scale. Is there a visualization approach? It’s one thing to have
a lot of data, but it’s another to be able to make that actionable. What do you do once that data is
assembled and you've got the analysis underway?
Yamazaki: The biggest success story that I will normally tell about Zynga is that we make data
available to all employees. From day one, as soon as you join Zynga, you get to
see all the data through our visualization to whatever we have. Even if you're
FarmVille product manager, you get to see what Poker is doing, making it more
transparent. There is an account report that you can just click and see how many
people have done this particular game action, for example. That’s how we were
able to create this data-driven culture for the Zynga.
Gardner: And Zynga is not all that old. Is this data capability something that
Ho
Yamazaki

3. you’ve had right from the start, or did you come into it over time? I suppose the question is how
early on in your organization did this data imperative become clear?
Yamazaki: Our analytics has been going on since the beginning. Our cluster scaled 70 times
since then.
Ho: It started off with three nodes, and we've grown to 230 node clusters.
Gardner: So you're performing the gathering of the data and analysis in your own data centers?
Yamazaki: Yes.
Gardner: When you realized the scale and the nature of your task, what were some of the top
requirements you had for your cluster, your database, and your analytics engine. How did you
make some technology choices?
Biggest points
Yamazaki: When Zynga was growing, our main focus was to build something that was going
to be able to scale and provide the data as fast as possible. Those were the two biggest points that
we had in mind when we decided to create our analytics infrastructure.
Gardner: And any other more detailed requirements in terms of the type of database or the type
of analytics engine?
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Yamazaki: Those are two big ones. As I mentioned, we wanted to have everyone be able to
access the data. So SQL would have been a great technology to have. It’s easy to train PMs
instead of engineering sites, for example, MapReduce for Hadoop. Those were the three key
points as we selected our database.
Gardner: What are you hearing from HP that’s of interest? Are there future directions and
requirements that you have? Are there things that you’d like to see from HP in order to continue
to be able do what you do at increasing scale?
Ho: We're interested in real-time analytics. There's a function aggregated projection that we're
interested in trying. Also FlexTable sounds like a very interesting feature that we also will
attempt to try. And cloud analytics is the third one that we're also interested in. We hope HP will
get it matured, so that we can also test it out in the future.

4. Gardner: While your analytics has been with you right from the start, you were very early in the
Vertica ecosystem?
Ho: Yes.
Gardner: So now we've determined how important it is, do you have any metrics of what this is
able to do for you? Other organizations might be saying they we don't have as much of a data-
driven culture as Zynga, but would like to and they realize that the technology can now ramp-up
to such incredible volume and velocity, What do you get back? How do you measure the success
when you do big-data analytics correctly?
Yamazaki: Internally, we look at adoption of systems. We we have 2,000 employees, and at
least 1,000 are using our visualization tool on a daily basis. This is the way to measure adoption
of our systems internally.
Externally, the biggest metric is retention. Are players coming back and, if so, was that through
the data that we collect? Were we able to do personalization so that they're coming back because
of the experience they've had?
Gardner: These are very important to your business, obviously, and it’s curious about that buy-
in. As the saying goes, you can lead a horse to water, but you can't make him drink. You can
provide data analysis and visualization to the employees, but if they don’t find it useful and
impactful, they won’t use it. So that’s interesting with that as a key performance indicator for
you.
Any words of advice for other organizations who are trying to become more data-driven, to use
analytics more strategically? Is this about people, process, culture, technology, all the above?
What advice might you have for those seeking to better avail themselves of big data analytics?
Visualization
Yamazaki: A couple of things. One is to provide end to end. So not just data storage, but also
visualization. We also have an experimentation system, where I think we have about 400-600
experiments running as we speak. We have a report that shows you run this experiment, all these
metrics have been moved because of your experiment, and A is better than B.
We run this other experiment, and there's a visualization you can use to see that data. So
providing that end-to-end data and analytics to all employees is one of the biggest pieces of
advice I would provide to any companies.
One more thing is try to get one good win. If you focus too much on technology or scalability,
you might be building a battleship, when you actually don’t need it yet. It's incremental.
Improvement is probably going to take you to a place that you need to get to. Just try to get a

5. good big win of increasing installs or active users in one particular game or product and see
where it goes.
Gardner: And just to revisit the idea that you've got so many employees and so many
innovations going on, how do you encourage your employees to interact with the data? Do you
give them total flexibility in terms of experiments? How do they start the process of some of
those proof-of-concept type of activities?
Yamazaki: It's all freestyle. They can log whatever they want. They can see whatever they want,
except revenue type of data, and they can create any experiments they want. Her team owns this
part, but we also make the data available. Some of the games can hit real time. We can do that
real-time personalization using that data that you logged. It’s almost 360-degree of the data
availability to our product teams.
Gardner: It’s really impressive that there's so much of this data mentality ingrained in the
company, from the start and also across all the employees, so that’s very interesting. How do you
see that in terms of your competitive edge? Do you think the other gaming companies are doing
the same thing? Do you have an advantage that you've created a data culture?
Yamazaki: Definitely, in online gaming, you have to have big data to succeed. A lot of
companies, though, are just getting whatever they can, then structure it, and make it analyzable.
One of the things that we've done that do well was to make a structure to start with. So the data is
already structured.
Product managers are already thinking about what they want to analyze before hand. It's not like
they just get everything in and then see what happens. They think right away about, "Is this
analyzable? is this something we want to store?" We're a lot smarter about what we want to store.
Costwise, it's a lot more optimized.
Gardner: Great. We'll have to leave it there. We have been hearing about how Zynga in San
Francisco has, right from its inspection, created a very strong culture around big data and grabs
as much as they can from the volumes and then makes the results of that data acquisition
available across the board to its employees.
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I'd like to thank our guests. We've been joined by Joanne Ho, Senior Engineering Manager at
Zynga. Thank you, Joanne.
Ho: You’re welcome.
Gardner: And also Yuko Yamazaki, Head of Analytics at Zynga. Thank you, Yuko.
Yamazaki: Thank you.

6. Gardner: And a big thank you to our audience as well, 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. Download the transcript. Sponsor: HP
Transcript of a Briefings Direct podcast on how gaming companies can gain a competitive
advantage in grabbing and analyzing data in near real time. Copyright Interarbor Solutions,
LLC, 2005-2015. All rights reserved.
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