Transcript of a BriefingsDirect podcast on how firms are using HP Vertica to gain more and faster insight from customer actions and interaction.

1. Panel of Business Experts Explores Role and Value of Big
Data in Customer Analytics
Transcript of a BriefingsDirect podcast on how firms are using HP Vertica to gain more and
faster insight from customer actions and interaction.
Listen to the podcast. Find it on iTunes. Sponsor: HP
Dana Gardner: Hello, and welcome to the next edition of the HP Discover Performance
Podcast Series. I'm Dana Gardner, Principal Analyst at Interarbor Solutions, your
moderator for this ongoing discussion of IT innovation and how it’s making an
impact on people’s lives.
Once again, we’re focusing on how IT leaders are improving their business
performance for better access, use and analysis of their data and information.
This time we’re coming to you directly from the HP Vertica Big Data Conference
in Boston. Our next innovation case study panel discussion highlights how various organizations
are developing the means to develop far better analytics about their customers. [Disclosure: HP is
a sponsor of BriefingsDirect podcasts.]
To learn more about how high performing and cost-effective big data processing enables a steep
learning curve from customers on their wants and preferences, please join me now in welcoming
our guests. We're here with Rob Winters, the Director of Reporting and Analytics at Spil Games
based in Amsterdam. Welcome, Rob.
Rob Winters: How is it going?
Gardner: It’s going great. We're also here with Davide Conforti. He is the Business Intelligence
Director at Jobrapido, based in Milan. Welcome, Davide.
Davide Conforti: Thank you, guys. Welcome.
Gardner: And we are also here with Pete Fishman, the Director of Analytics at Yammer, based
in San Francisco. Welcome.
Pete Fishman: Thanks, Dana.
Gardner: Businesses have been analyzing customers for a long time. This isn’t something new
-- needing to know a lot about your customer. What’s different now about truly getting to know
your customer. Let’s start with you, Pete.
Fishman: I work in the software industry, and our data now on the customers is all living in a
central place. We're a cloud software service, and the data is big. By aggregating across
companies that are using your software, you can get really significant sample sizes and real

2. inference, both from an economic sense, in terms of measuring the lift, but actually because the
sample sizes are big, you can get statistical inference.
That’s the starting point for making analytics valuable and learning about your customers.
Gardner: Rob, what’s different now, in terms of being able to get information, than 10 years
ago?
Different problems
Winters: For me, the problem space is extremely different from what I was dealing with a
couple of years back.
I was in telecom before this. There, you're dealing with 25 million people, and if
you rescore them once a month, that’s fast enough. On a web scale problem, I'm
dealing with 200 million customers and I have to rescore them within 10 or 15
minutes. So you're capturing significantly more data. We're looking at billions of
records per day coming into our systems. We have to use it as fast as possible,
because with the customer experience online, minutes matter.
Gardner: Is this a familiar story to you Davide? How are things different for you in terms of
getting to know your customers?
Conforti: It’s absolutely the same story. We have about 40 million unique visitors per month
now. We've grown by double digits since our start as a startup in 2006. Now,
everything is about user interaction, how our users behave on-site, and how we
can engage them more on-site and provide them a tremendous ad-hoc user
experience.
Gardner: So it's not just getting to know your customers. It's following your
customers. It’s their actions that you can capture. I suppose that's pretty
interesting and new, but let’s start with Spil Games. Tell us about your
organization. How did you get such a big audience?
Winters: We've been around for about nine years. We started out as just a Dutch company and
then we've acquired other local domain names in a variety of languages. At this point, we have
about 50 different platforms, running in about 20 different languages. So we support customers
from all over the world. In a given month, we have over 200 countries with traffic onto our sites.
For us, growth was initially about just getting that organic traffic. Up until a few years ago, if
you had a good domain name, you were competing based off of where you ranked in search.
Now, the entire business is changing, and you're competing based off that customer experience
that you can deliver.

3. Gardner: Tell us what kind of games, and who are they targeted at?
Winters: We have a couple target audiences: girls, young girls, 8-14; boys; and then women.
We're primarily a platform. We do some game development and publishing, but our core business
is just being the platform where people can come and find content that’s interesting to them.
Gardner: Let's hear more about Yammer. Tell me, Pete, what Yammer is and does, and how you
got to such huge numbers and big data.
Fishman: Yammer is a startup in San Francisco. We were acquired about a year ago by
Microsoft and we're part of the larger Office organization. We view ourselves
as enterprise social, taking this many-to-many communication model and
making communication at your company much more efficient.
It's about surfacing relevant knowledge and experts and making work lives
better. I run an analytics team there, and we essentially look at the aggregate
customer behaviors and what parts of our tool people are using.
Social networks
Gardner: So, this was interesting for you as a social network within the confines of an
enterprise of a business. What goes on in that network is imported data. You can learn tribal
knowledge, capture it, and apply it to other problems which perhaps you can't do on some of the
more public or free and open social networks.
Fishman: Exactly. This was a really revolutionary idea that our founders David Sacks and Adam
Pisoni had, way back when Facebook wasn't nearly as relevant as it is today. But we've leveraged
a lot of the way that people have learned to interact in their social life and bring some of that
efficiency of communication.
For example, telling you that I've gotten engaged or I'm having a baby, all these pictures go on
Facebook. It's an efficient way of getting many-to-many communication. They saw that these
social networks would grow and be relevant in a private, secured context of your business.
Gardner: Let's learn more about Jobrapido. Tell me about your organization and the some of the
reasons that there's so much data to analyze.
Conforti: Jobrapido started in 2006 as an entrepreneurial challenge that Vito Lomele, an Italian
guy, started in Milan. It's quite a challenge to live in the online market in Italy, because talent
pooling isn't as wide as in U.S. or in other countries in Europe. What we do is provide jobseekers
the opportunity to find their new job.

4. We're an online job-search engine and we currently operate in 58 different countries with more
than 20 languages. We're all in this big headquarters in Milan with a lot of different
nationalities, because of course, we provide the service in local languages for
most of our customers.
Recently, we have been purchased by the Daily Mail group, a big media group
based in London. For us, it's everything from job-seeker acquisition and retention
and engagement deals with constant quality and user experience on-site. We use our
big data warehouse in order to understand how to better attract and retain customers on the basis
of their preferences. And we also use it to tweak our matching algorithm, which works more or
less like a Google algorithm.
We crawl a lot of contents from different sources, both job boards and other job sites or directly
in the working pages of individual companies. We put them together in a big database and, using
statistical tools, we infer which kind of rankings our jobseekers are willing to see.
So it's a pretty heavy data crunching exercise that we do everyday on millions and millions of
different sponsored or organic postings.
Gardner: And just to be clear, this is a site for not only those who are looking for job use, but
those who are looking to hire as well.
Moving to B2B
Conforti: True. Most of our business deals with B2C, but we're developing tools and a B2B
platform to address players such as job boards, for example. We crawl and get sponsored ads
from job boards as well, but we're more and more going towards our end customers.
For example if Yammer guys or if Spil Games guys want to hire a software engineer, they can
directly promote their sponsored ads on Jobrapido without having to sponsor them on a job
board. So we're trying to aggregate and simplify the chain of job search.
Gardner: Now that we know more about you, let's learn more about the problem that you had
when it comes to managing big data and where to get to those all important customer insights
and analysis to make those available to your workers and strategists.
Rob, let's start with you. What was the problem you had to solve when it comes to getting at this
data in analysis?
Winters: For me, my problem was that no one had ever tried to do it in my company before. We
walked in with effectively a clean slate. But as you start to bring in different data sources, you
start with all the stuff that you know you're going to need right away.

5. You start seeing needed links for other data sources. At this point, we're pulling data from
thousands of databases, merging with dozens of application programming interfaces (APIs).
You're pulling in your web log data, so that you can personalize for those folks who aren’t giving
you registration information.
For me the challenge was multi-fold. How do you deal with this data problem, with this variety
and volume information? How do you present it in a meaningful fashion for employees who've
never looked at data before, so that they can make good decisions on it? And how do you run
models against it and feed that back into a production environment as quickly as possible, so that
you can give those customers a better experience than they were ever getting before on your
platform?
Gardner: How did you solve it?
Winters: We're still trying to solve it, to be honest. If you look at it, we've built a technology
stack that is a mixture of open source, commercial, and proprietary software that we've
developed to solve these different problems. It's an ongoing journey for us -- how we do these
things, and we're moving forward two steps, falling back one, and continuing along this path.
Gardner: What was it about a Vertica architecture that helped mitigate some of these issues?
Was there a comparison to the way you had done it before, or did you go directly to a Vertica
solution when you encounter these issues?
Large data
Winters: When we first started looking for a data-warehouse appliance or application, we
were running Postgres with no indices, just copies of production data. For data guys, that means
that a query will take eight hours to execute. It's a table of a couple of million rows.
We knew that a typical row-based solution was out. So we started looking at some of the other
applications out there. The big ones are Teradata, Exadata, and Greenplum, but you're going to
have to mortgage the house of every employee in the company to be able to afford a license for
those applications, and we're a pretty small company. So those were out.
Then, we started looking at some of the other boutique vendors like Infobright, and basically we
saw that with Vertica, we can have relatively low load on our database administrator (DBA), so
we can develop quickly without a lot of maintenance.
The pricing model fits what we need to achieve, and the performance is so good that we don't
have to spend a ton of time on optimization now. We can basically move very rapidly along this
path of becoming a data-driven organization without having to get held up on index optimization
or trying to optimize our queries and rewrite paths.

6. We can just throw a lot of stuff into the system, smash it together, take the results, and get big
wins for the company quickly.
Gardner: And how important is it for you to be able to deploy this on appliances only, or do you
have other directions that you would like to go with that?
Winters: No, we're doing everything within our own premises. We have a data center, and we do
everything on our own private servers. For us, the next step is probably going to be moving more
into a private-cloud model, and hopefully, Vertica will work in that environment as well.
Gardner: At Yammer, let's look at your problem set and how you went about solving it.
Fishman: I think more broadly than just data as the problem set. Our problem set was that there
were a lot of people trying to get into the enterprise social space. A lot of social networks are
popping up, and essentially competing for attention at work is a challenge.
We felt that data was necessary to have a competitive advantage. David Sacks and Adam Pisoni
had a vision of developing a consumer software company with rapid iteration. With that rapid
iteration you get an extra advantage if you're able to reorient yourself based on what part of the
product is working. Our data problems were largely about making data be a competitive
advantage in our development methodology.
Gardner: What was it about Vertica that was instrumental to the point where you've adopted it?
Is it a concurrency issue, a volume issue, speed, or all the above?
It's about speed
Fishman: It's all of the above, but the real highlight is always going to be about speed,
especially, given the incredible competition for talent, not just in the Bay Area, but all over,
especially in the data field.
Anybody that has data in their title is someone that’s highly sought after. That ability to minimize
the cycle times for those folks who are such a challenge to keep and get excited about the
projects that they're working on and is a tremendous solution that allows them to maximize their
own abilities is really critical. It's the same in our space, and in software development in general.
Since we're in Boston, I feel like I can use baseball analogy. Hall of Fame product managers are
like Hall of Fame baseball players, meaning they get it right about a third of the time. When we
take on these big risks and challenges, the ability to very quickly identify whether we're going in
the right direction, and then reorienting where we are going, has been really critical to Yammer
being successful.
Gardner: I guess we could say it's better to give your data scientists a Ferrari than a go-kart?

7. Fishman: That seems like a good investment these days.
Gardner: Davide, what's the Ferrari in your organization? How did you get to one and what
were you using before?
Conforti: When I joined Jobrapido, we already ran tons of A/B tests, which are the lifeblood of
our product innovation. We want to test everything, from changing the color or the font of one
button to a different layout, because these have tremendous impact on improving the user
engagement.
Before, we used the Google Analytics tools, but we didn't like that much, because it's sample
data, so you hardly reach statistically meaningful results. We decided to build a data warehouse
to assure flexibility, performance, and also a higher level of control and data consistency. That's
end-to-end control from the source, towards the visualization, in order to make them more
actionable in terms of product development.
With Vertica, we did exactly this. We poured all the different data sources into one bucket,
organized it, and now we have a full control over the data model. With my team, I manage these
data models. It's fascinating how fast you can add pieces to the puzzle or remove others that are
no longer interesting, because our business model, of course, is a living animal, a living creature.
We really appreciate this flexibility and the high level of control that Vertica allows. This
improved a lot our innovation throughput and it's going to improve it even more in the future.
Gardner: Do you have any metrics of success for comparison, either in time, concurrency, or
volume? Most of our listeners and audience are interested in some hard facts. Do you have any
feeds and speeds you can share?
Conforti: Currently, we crunch on Vertica about 30 GB of data everyday (i.e. we upload 30 GB/
day on Vertica). But we're going to double it in a few months, because we're adding more stuff.
We want to know more about the click patterns of our jobseekers on the site, and this is massive
data flowing into Vertica. Also, our licensing in terabytes will likely double in the future.
Increased performance
Another hard fact that I can share with you guys is that every one of you using Vertica doesn't
have to be satisfied with the first implementation of the query. If you're able to optimize it, you
almost increase the performance of the query by more than 100 percent. This is my personal
experience with consultants or advisers. Vertica is happy to provide the support, and this is really
value adding.
Gardner: Given that you're seeing such a large increase very rapidly in terms of your data
volume, do you have a sense of cost prediction, or is there a visibility at least into the
relationship between the task and the total cost?

8. Conforti: What we try to understand is whether we have to pour this big amount of data, all into
Vertica or if we have to flank it with Hadoop or some sort of cheaper storage solution, in order to
get better control costs. Currently, I don't have the figures or a model to estimate how the cost
moves with the numbers. This is a pretty good point. I will build it and I will share the results
with you in the future.
Gardner: Rob Winters, any metrics of success and/or how do you feel about visibility into
controlling costs?
Winters: As far as metrics of success, when we were doing our proof of concept (POC), we
looked at primarily query performance. At that point, we weren’t looking at using it for
prediction and personalization, but just for analytics and reporting.
What we saw was against an indexed Postgres database. We had done some optimization on the
data. Our queries were running more than 1,000 percent faster, and Vertica was scaling pretty
linearly, whereas with Postgres, when we put more data into the tables, they just started choking
and just died completely.
For me, it allowed me to actually do my job and have my team do their jobs, which is a pretty
big metric of success.
The other thing is that with a relatively small cluster, we can support hundreds of people and
reports directly accessing the database, a dozen analysts or people who directly query
information out of the database, and all of our personalization activities simultaneously with
minimal performance hiccups. That’s a big metric of success.
Gardner: Pete, how do you judge that this is a success? What are the important metrics? Maybe
you could wow us with some of your speeds and feeds too.
Fishman: I have similar feedback as Rob, which is a comparing against a Postgres database. The
speeds are at least one, and probably closer to two or better, orders of magnitude faster. Certainly
on the cost side, it's important with data to consider the whole cost. So this is sort of a theme.
End-to-end costs
There is a cost in a variety of managing and teasing out the useful insights that aren't
necessarily in the sticker price. When considering a data solution, people should consider the
end-to-end costs. What's really the cost per insight, as opposed to the cost per terabyte or the cost
per whatever.
We certainly feel that Vertica has been our best solution. We've been customers for over three
years. So it's quite a long relationship. I couldn’t imagine going back to a multi-day query or
something like that.

9. Gardner: So on that important new metric of cost per insight, do you see that linear going up or
going down? Is there a trajectory or predictability for that?
Fishman: One thing that Davide mentioned is that he's forecasting how much data he will be
putting into Vertica. I'm a forecaster myself by trade. Back in 2010, we were doing some
estimates of where we would be by the end of 2011 in terms of our data volumes. This is a pretty
simple extrapolation, and I got it wrong by at least an order of magnitude.
What we found is that when you start to get real insights from data, you want to get a little bit
more, collect it maybe here or there. Also, as our product was growing, we faced some real
exponential growth on the data and adopted clever solutions for maximizing that metric that we
care about -- cost per insight, or minimizing the cost for insight.
Gardner: But you're not willing to predict if that's going to go up or down based on your
efficiency and the use of the technology?
Fishman: There are many things going on simultaneously. So tripping over really valuable
insights can happen a lot more easily than when you're more naïve about it. Essentially, you're
facing headwinds in that. Finding insights become harder. At the same time, you have larger data
volumes and some economies of scale there. So there are a lot of things simultaneously
interacting, but clearly one thing to drive down that metric is best-in-breed tools.
Gardner: Of course, best to get the information of the people who can use it than to simply look
to cut cost.
Fishman: Of course. If you view analytics as a cost center, that's the wrong view. It should be
aimed at optimizing revenue streams. We micro-optimize the product, we micro-optimize sales
and marketing, the business. Analytics is about improving everybody at their job, making data
allow people to be more effective.
Gardner: Well, great. I'm afraid we will have to leave it there. We've been learning about how
various organizations are developing the means to far better analyze their customers, and these
are some impressive organizations with very large sets of customers and data that go along with
that.
We've seen how they deployed in HP Vertica Analytics Platform to provide better analytics to
their internal users, and then, in some cases, back out to the very customers that they are
gathering data from. So a big thank you to our guests. We've been joined by Rob Winters,
Director of Reporting and Analytics at Spil Games based in Amsterdam. Thanks so much.
Winters: Thank you.
Gardner: And we've also been joined by Davide Conforti, the Business Intelligence Director at
Jobrapido in Milan. Thank you, David.

10. Conforti: Thank you, guys. It's been a pleasure.
Gardner: And also Pete Fishman. He is the Director of Analytics at Yammer in San Francisco.
Thanks, Pete.
Fishman: My pleasure. Thank you very much.
Gardner: And thanks all to, to you all audience for joining us for this special HP Discover
Performance Podcast coming to you from the HP Vertica Big Data Conference in Boston.
I'm Dana Gardner, Principal Analyst at Interarbor Solutions, your host for this ongoing series of
HP Sponsored Discussions. Thanks again for joining us, and do come back next time.
Listen to the podcast. Find it on iTunes. Sponsor: HP
Transcript of a BriefingsDirect podcast on how firms are using HP Vertica to gain more and
faster insight from customer actions and interaction. Copyright Interarbor Solutions, LLC,
2005-2013. All rights reserved.
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