Transcript of a Briefings Direct discussion on how GoodData is helping its customers gain new insights into their businesses with data analytics.

1. The Future of Business Intelligence as a Service with
GoodData and HP Vertica
Transcript of a Briefings Direct discussion on how GoodData is helping its customers gain new
insights into their businesses with data analytics.
Listen to the podcast. Find it on iTunes. Get the mobile app for iOS or Android.
Sponsor: HP Enterprise
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 GoodData is exploring
the realms and possibilities for delivering business intelligence (BI) and data
warehousing as a service and how they're exploring new technologies to make that more
seamless across more data types for more types of users.
With that, I'd like to introduce our guests today. We're here with Jeff Morris, Vice President of
Marketing at GoodData. They're based in San Francisco. Welcome, Jeff.
Jeff Morris: Thanks very much, Dana.
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Gardner: We are also here with Chris Selland, Vice President for Business Development at HP
Vertica. Welcome, Chris.
Chris Selland: Thanks, Dana. Great to be here with you both.
Gardner: First, Jeff, for those who might not be that familiar, tell us about GoodData, what you
do and why it's different.
Morris: GoodData is an analytics platform as a service (PaaS). We cover the full spectrum end-
to-end use case of creating an analytic infrastructure as a service and delivering that to our
customers.
Gardner

2. We take on the challenges of collecting the data, whatever it is, structured and unstructured. We
use a variety of technologies as appropriate, as we do that. We warehouse it in
our multitenant massively scalable data warehouse that happens to be powered
by Vertica.
We then combine and integrate it into whatever the customer’s particular key
performance indicators (KPIs) are. We present that in aggregate in our
extensible analytics engine and then present it to the end users through desired
dashboards, reports, or discoverable analytics.
Our business is set up such that about half of our business operates on an
internal use case, typically a sales and marketing and social analytic kind of use case. The other
half of our business, we call "Powered by GoodData." and those customers are embedding the
GoodData technology in their own products. So we have a number of companies creating these
customer-facing data products that ultimately generate new streams of revenue for their business.
40,000 customers
We've been at this since 2007. We're serving about 40,000 customers at this point and
enjoying somewhere around 2.4 million data uploads a week. We've built out the service such
that it's massively scalable. We deliver incredibly fast time to market. Last quarter, about two
thirds of our deployments were delivered within 16 weeks or less.
One of the divisions of HP, in fact, deployed GoodData in less than 6 weeks.
They are giving their first set of KPIs and delivering that value to them. What’s
making us different in the marketplace right now is that we're eliminating all of
the headaches associated with creating your own big-data lake style BI
infrastructure and environment.
What we end up doing is affording you the time to focus on the analytics and
the results that you gain from them—without having to manage the backend operations.
Gardner: What’s interesting to me is that you mentioned PaaS for BI. Instead of developing
applications and then having a production environment that’s seamlessly available to you, you're
creating analytic applications on datasets that we contribute to your platform. Is that right?
Morris: Yes, indeed. The datasets themselves also tend to be born in the cloud. As I said, the
types of applications that we're building typically focus on sales and marketing and social, and e-
commerce related data, all of which are very, very popular, cloud-based data sources. And you
can imagine they're growing like crazy.
We see a leaning in our customer base of integrating some on-premise information, typically
from their legacy systems, and then marrying that up with the Salesforce, or the market data or
Morris

3. social information that they want to integrate and build a full view of their customers -- or a full
exposure of what their own applications are doing.
Gardner: So, you're really providing an excellent example of how Vertica is a cloud born
analytics platform and implementation. That’s kind of interesting.
But I wonder whether any of your clients, maybe not so much in the media, but some of the more
traditional verticals like healthcare, retail, or government, are trying to do this across a hybrid
model. For example, they're doing some BI and they have warehouses on-premises or maybe
other hosting models, but they also want to start to dabble in moving this to the cloud and taking
advantage of what the cloud does best. Are we now on the vanguard of hybrid BI.
Morris: We're getting there, and there are certainly some industries are more cloud friendly than
others right now. Interestingly, the healthcare space is starting to, but they're still nascent. The
financial services industry is still nascent. They're very protective of their information. But
retailers, e-commerce organizations, technology ISVs, and digital media agencies have adopted
the cloud-based model very aggressively.
We're seeing a terrific growth and expansion there and we do see use cases right now where
we're beginning to park the cloud-based environment alongside your more traditional analytics
environments to create that hybrid effect. Often, those customers are recognizing that the speed
at which data is growing in the cloud is driving them to look for a solution like ours.
Gardner: Chris, how unique is GoodData in terms of being all cloud moving towards hybrid,
and does this really provide a poster child, in a sense, for Vertica as a service?
Special relationship
Selland: GoodData is certainly a very special partner and a very special relationship for us. As
you said, Vertica is fundamentally a software platform that was purpose-built for big data that is
absolutely cloud enabled. But GoodData is the best representation of the partner who has taken
our platform and then rolled out service offerings that are specifically designed to solve specific
problems. It's also very flexible and adaptable.
So, it’s a special partnership and relationship. It's a great proof point for the fact that the Vertica
platform absolutely was designed to be running in the cloud for those customers who want to do
it.
As Jeff said though, it really varies greatly by industry. Just in the past few days, we had our
Customer Advisory Board (CAB), and a large majority of the customers in our CAB, which tend
to be some of our largest customers and some pretty well-known industries, were saying how
they will never put their data in the cloud.

4. Never is a very long time, but at the same time, there are other industries that are adopting it very
rapidly. So there is a rate of change that’s going on in the industry. It varies by size of company,
by the type of competitive environment, and by the type of data. And yes, there
is a lot of hybridization going on out there. We're seeing more of the
hybridization in existing organizations that are migrating to the cloud. There's a
lot of new breed companies who started in the cloud and have every intent of
staying there.
But there's a lot of dynamism in this industry, a lot of change, and this is a
partnership that is a true win-win. As I said, it's a very special relationship for
both companies.
Gardner: Jeff, given that we have such variability, vertical by vertical, company by company,
green-field versus an established company will behave differently vis-à-vis their architecture and
their IT implementation. You need to be ready for any and all of that and I suppose Vertica does
as well.
We're hearing also more than just Vertica here. We're talking about Haven, which includes
Hadoop, Autonomy, security and applications. Is there a path that you see whereby you can try to
be as many things to as many types of customer and vertical industries?
I'm thinking about Hadoop, security, and bringing some of the more enterprise-caliber KPIs and
SLAs, so that some of those folks that are hesitant to move at least some their data in some ways
to the cloud would move in that direction. Is that a vision for you. Maybe you could explain
where you see this going on a hybrid basis.
Morris: Absolutely. The Haven-style architecture is a vision in a direction that we are going. We
do use Hadoop right now for special use cases of expanding and providing structure, creating
structure out of unstructured information for a number of our customers, and then moving that
into our Vertica based warehouse.
The beauty of Vertica in the cloud is the way we have set this up and this also helps address both
the security and the reliability issues that might be a thought of as issues in the cloud. We're triple
clustering each set of instances of our vertical warehouses, so they are always reliable and
redundant.
Daily updates
We, like the biggest enterprises out there, are vigilantly maintaining our network. We update
our network on behalf of our customers on a daily basis, as necessary. We roll out and maintain a
very standardized operating environment, including an open stack-based operating environment,
so that customers never need to even care about what versions of the SSL libraries exist or what
versions of the VPN exist.
Selland

5. We're taking care of all of that really deep networking and things that the most stalwart
enterprise-style IT architects are concerned about. We have to do that too and we have to do it at
scale for this multitenant kind of use case.
As I said, the architecture itself is very Haven-like, it just happens to be exclusively in the cloud
-- which we find interesting and unique for us. As for the Hadoop piece, we don’t use Autonomy
yet, but there are some interesting use cases that we are exploring there. We use Vertica in a
couple of places in our architecture, not only that central data warehouse, but we also use it as a
high-performance storage vehicle for our analytic data marts.
So when our customers are pushing a lot of information through our system, we're tapping into
Vertica’s horsepower in two spots. Then, our analytic engine can ingest and deal with those
massive amounts of data as we start to present it to customers.
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On the Haven architecture side, we're a wonderful example of where Haven ends up in the cloud.
For the applications themselves, the kind of things that customers are creating, might be these
hybrid styles where they're drawing legacy information in from their existing on-premise
systems. Then, they're gathering up, as I said before, their sales and marketing information and
their social information.
The one that we see as a wonderful green field for us is capturing social information. We have
our own social analytic maturity model that we describe to customers and partners on how to
capitalize on your campaigns and how to maximize your exposure through every single social
channel you can think of.
We're very proficient at that, and that's what's really driving the immense sizes of data that our
customers are asking for right now. Where we used to talk in tens of terabytes for a big system,
we're now talking in the world of hundreds, multiple hundreds of terabytes, for a system. Case by
case by case, we're seeing this really take off.
Gardner: It's fine to talk about this as an abstraction, but it's really useful to hear some
examples. Do you have any companies, either named or unnamed, that provide a great use case
example of PaaS, for BI apps that take advantage of some of the attributes of Haven and Vertica?
Morris: One of our oldest and most dear customers is Zendesk. They've recently IPOd. They
have a very successful customer-support application in the cloud. They provide both a freemium
model and degrees of for-fee products to their customers.
And the number one reason why their customers upgrade from freemium to general and then
general to the gold level of product is the analytics that they're supplying inside of there. They
very recently announced a whole series of data products themselves, all powered by GoodData,
as the embedded analytic environment within Zendesk.

6. We have another customer, Service Channel which is a wonderful example of marrying together
two very disparate user communities. Service Channel is a facility’s management enterprise
resource planning (ERP) application. They bring together the facility managers of your favorite
brick-and-mortar retailers with the suppliers who provide those retail facilities service, janitorial
services, air-conditioning guy, the plumbers.
Disparate customers
Marrying disparate types of customers, they create their own data products as well, where they
are integrating third-party information like weather data. They score their customers, both the
retailers as well as the suppliers, and benchmark them against each other. They compare how
well one vendor provides service to another vendor and they also compare how much one of the
retailers spends on maintaining their space.
Of course, Apple gets incredibly high marks. RadioShack, right now, as they transition their
stores, not so much. Service Channel knew this information long before the industry did, because
they're watching spend. They, too, are starting to create almost a bidding network.
When they integrated their weather data into the environment, they started tracking and saying,
"Apple would like to gain first right of refusal on the services that they need." So if Apple’s air
conditioning goes out, the service provider comes in and fixes the air-conditioning sooner than
Best Buy and all of their competitors. And they'll bid up for that. So they've created almost a
marketplace. As I said before, these data products are really quite an advantage for us.
Gardner: Looking a bit to the future, we've heard the interest in moving from predictive to
prescriptive analytics. It seems to me that that’s really a factor of the quality of the data in getting
data from different sources and bring it together, something you can do in a cloud more easily or
more efficiently than server by server, or cluster by cluster.
What kind of services should we envision as the analytics as a business model unfolds in the
cloud and you can start to do joins across different types of data for an industry, rather than just
an enterprise? Is there an opportunity to get that prescriptive value as a provider with the past
capability? It sounds very exciting and interesting. What's coming down the pike?
Morris: Most definitely, we're seeing a number of great opportunities, and many are created and
developed by the technologies we've chosen as our platform. We love the idea of creating not
only predictive, but prescriptive, types of applications in use cases on top of the GoodData
environment. We have customers that are doing that right now and we expect to see them
continue to do that.
What I think will become really interesting is when the GoodData community starts to share
their analytic experiences or their analytic product with each other. We feel like we're creating a
central location where analysts, data scientists, and our regular IT can all come together and
build a variety of analytic applications, because the data lives in the same place. The data lives in

7. one central location, and that’s an unusual thing. In most of the industry your data is still siloed.
Either you keep it to yourself on-premise or your vendors keep it to themselves in the cloud and
on-premise.
But we become this melting pot of information and of data that can be analytically evaluated and
processed. We love the fact that Vertica has its own built-in analytic functions right in the
database itself. We love the fact that they run our predictive language without any other issue and
we see our customers beginning to build off of that capability.
My last point about the power of that central location and the power of GoodData is that our
whole goal is to free time for those data scientists and those IT people to actually perform
analytics and get out of the business of maintaining the systems that make analytics available, so
that you can focus on the real intellectual capital that you want to be creating.
Identifying trends
Gardner: So, Chris, to cap this off, I think we've identified some trends. We have PaaS for BI.
We have hybrid BI. We have cloud data joins and ecosystems that create a higher value
abstraction from data. Any thoughts about how this comes together, and does this fit into the
vision that you have at Vertica and that you're seeing in other parts of your business.
Selland: We're very much only at the front end of the big data/analytics revolution. I ultimately
don’t think we are going to be using the term "big data" in 10 years.
I often compare big data today to eBusiness 10, 12 years ago. Nobody uses that term anymore,
but that was when everything was going online, and now everything is online, and the whole
world has changed. The same thing is happening with analytics today.
In Colin's keynote, if you saw it, he cited some statistics, talking about how with a hundred times
more data we can actually get 10,000 times, or it might have been a 100,000 times more insight.
And that's true, but it's not just the amount of data; it's the ability to cross correlate. That's the
whole vision of what Jeff was just talking about that GoodData is trying to do.
It's the vision of Haven, to bring in all types of data and to be able to look at it more holistically.
Oe of my favorite examples, just to make that concrete, is that there is an airline we were talking
to not long ago. They were having a customer service issue. They were having a lot of their
frequent flyers -- and not just their frequent flyers, but all of their passengers -- tweeting angrily
about them and they were trying to analyze the social media data to figure out how to make this
stop and how to respond.
In a totally separate part of the organization, they had a predictive maintenance project, almost an
Internet-of-things (IoT) type of project, going on. They were looking at data coming off the fleet
and trying to do better job of keeping their flights on time.

8. If you think about this, you say, "Duh." There was a correlation between the fact that they were
having service problems and that the flights were late with the fact that the passengers were
angry. Suddenly, they realized that maybe by focusing less on the social data in this case, or
looking at that as the symptom as opposed to cause, they were able to solve the problem much
more effectively. That's a very, very simple example.
I cite that because it makes real for people that it's when you really start cross correlating data
you wouldn't normally think belong together, social data and maintenance data, you get true
insights. It's almost a silly simple example, but those types of examples we're going to see much
more. The more of this we can do, the more power we are going to get. I think that the front end
of the revolution is here.
Gardner: And then those insights become empirical and not just intuitive or based on someone's
observation. You've got hard evidence.
Selland: Correct, exactly.
Gardner: All right. I'm afraid we have to leave it there. We have been learning about how
GoodData is creating a platform as a service around business intelligence, built on Vertica in the
cloud. I'd like to thank our guests. We've been joined by Jeff Morris, the Vice President of
Marketing at GoodData. Thank you, Jeff.
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Morris: It's my pleasure. Thanks a lot, Dana.
Gardner: We've also been joined by Chris Selland, the Vice President for Business Development
at HP Vertica. Thank you, Chris.
Selland: Thanks, Dana and thank you, Jeff.
Gardner: And I'd like to thank 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 do come back next time.
Listen to the podcast. Find it on iTunes. Get the mobile app for iOS or Android.
Sponsor: HP Enterprise
Transcript of a Briefings Direct discussion on how GoodData is helping its customers gain new
insights into their businesses with data analytics. Copyright Interarbor Solutions, LLC,
2005-2015. All rights reserved.
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