Transcript of a BriefingsDirect podcast on how HP is developing products and platforms to help businesses deal with the demands of big data in a competitive environment.

1. HP Updates and Expands its HAVEn Portfolio as Businesses
Seek Transformation from More Data and Better Analysis
Transcript of a BriefingsDirect podcast on how HP is developing products and platforms to help
businesses deal with the demands of big data in a competitive environment.
Listen to the podcast. Find it on iTunes. Sponsor: HP
Dana Gardner: Hello, and welcome to the next edition of the HP Big Data podcast series. I’m
Dana Gardner, Principal Analyst at Interarbor Solutions, your moderator for this
ongoing sponsored discussion on how data is analyzed and used to advance the
way you live and work.
Gardner
Once again, we're showcasing thought-leaders and companies worldwide that
are capturing myriad knowledge, gaining ever deeper analysis, and rapidly and
securely making those insights available to more people on their own terms.
Our next big-data innovation discussion highlights how the latest version of HP HAVEn
produces new business analytics value and strategic returns. So please now join me in
welcoming our guests today.
We’re here with Girish Mundada. He is the Chief Technology Officer for HP HAVEn. Welcome,
Girish.
Girish Mundada: Thanks, Dana. Good to be here with you.
Gardner: And we’re here with Dan Wood, Worldwide Solution Marketing Lead for Big Data at
HP software. Welcome, Dan.
Dan Wood: Hello, Dana,
Gardner: Good to have you with us. Dan, let me start with you first. It seems that we’re in a
fascinating time because analytics and big data are now so top of mind. What was once relegated
to a fairly small group of data scientists and analysts as reporting tools -- and I am thinking about
business intelligence (BI) -- has really now become a comprehensive capability that’s proving
essential to nearly any business strategy.
So Dan, from your perspective, what’s behind this eagerness now to gain big-data capabilities
and exploit analytics so broadly?
Wood: You’re right, Dana, and it’s because we're starting to see some very clear quantification of
the value and the benefits of big data. It’s fair to say that big data is probably the hottest topic in
the industry at the moment.

2. There’s a lot of talk across all forms of media about big data right now, but what’s happened is
that credible publications like the "Harvard Business Review", for example, have started to put
solid numbers around the benefits that enterprises can get if they can get their hands around bigdata analytics and apply it for the business challenges.
For example, Harvard Business Review is saying that, on average, data-driven organization will
be five percent more productive and six percent more profitable than their competitors.
Worth chasing after
Think about that. A six-percent distinct profitability increase would double the stock price for a
lot of organizations. So there really is a prize worth chasing after.
What we’re seeing, Dana, is much more widespread interest across the
organization and not just within IT. We’re seeing line-of-business leaders
understanding and, in many organizations, actually starting to benefit from bigdata analytics.
They’re able to analyze the call logs in a call center, better understand the
clickstreams on a website, and better understand how customers are using
products. All of these are ways of analyzing large amounts of data and directly tying it to specific
line-of-business problems.
Wood
That’s where we are right now, Dana. Industries around the world are going through
transformational projects using big data to gain that competitive advantage.
Gardner: It’s interesting too, Dan, that they’re not just taking these as individual data sets and
handling them individually, but increasingly, they’re starting to combine them and find
relationships and things that they really couldn't have done before.
Wood: Absolutely. It’s just the idea of 360-degree view of their internal operations or of their
external customer trends and needs, and it’s come by combining data sets.
For example, they’re combining social media analytics on customers with the call logs into the
call center, with internal systems of record around the customer relationship management (CRM)
and ongoing customer transactions. It’s by combining all those insights that the real big-data
opportunity reveals itself.
Gardner: And the sources for those insights and data, of course, are across almost any type of
information asset. It’s not a just structured data or data that your application standard is around,
but it’s getting all the data all the time.
Wood: That’s right. In some ways, this industry label of big data is perhaps not the most helpful,
because it’s not just the volume of data that is the challenge and the opportunity for the business.

3. It’s the variety of sources, as you’ve alluded to, and also the velocity at which that data is
moving.
The business needs to get hold of these multiple sources of data and immediately be able to
apply the analytics, get the insights, and make the business decisions. Dana, this really is why
still the vast majority of that data that’s available to an enterprise remains dark.
Unused and unexploited
It’s unused and unexploited. Organizations, with their traditional analytics systems, are
struggling to get the meaning and insights from all these data types that we mentioned. These
include unstructured information, such as social media sentiment, voice
recording, potentially even video recordings. and the structured and semistructured things like log files and center data. For many organizations, getting
the information quickly enough out of their CRM and enterprise resource
planning (ERP) systems is a challenge as well.
Gardner: So we see that there’s a great desire to do this and there are great returns
on being able to do this well. We talked about some of the general challenges. What specifically
is holding people up?
Is this an issue of cost complexity or skills? Why aren’t companies able to move beyond this
small fraction of the available information to which they could be applying such important
insight and analytics?
Wood: It’s a complexity and a skill challenge, as you mentioned. The systems they have today,
Dana, typically aren’t set up to able to analyze this vast amounts of unstructured information and
also to be able to analyze the structured data at a speed that is needed by the organization.
Think about the need to analyze immediately a clickstream from an online shopping application
or a pay-to-use application that an organization has. That is, a rapid-scale analysis of a large
amount of structured data. Typically, the analytic systems that organizations have had aren’t able
to cope with that or with that unstructured human information.
This is why HP has created the HAVEn Big Data Platform, and Girish will talk in more detail
about this, that brings together the analytics engine that is needed to address this issue.
Just as importantly, there’s the ecosystem around HAVEn, which includes HP experts and
services and services from partners, to bring those skills that are needed to turn this data
collection into useful information.
And there are skills around data scientists, as well, skills around understanding the right
questions the line of business needs to be asking and understanding actually how to visualize and
represent that data.

4. Gardner: Let’s go over to Girish. Based on what we have talked about in terms of some of these
serious challenges, what were some of the guiding principles that you were thinking of when
HAVEn was being put together and refined?
Talking to customers
Mundada: As I mentioned when we spoke earlier, HAVEn came together not by creating it in
a dark room somewhere in the back office. It came together by talking to customers. On a regular
basis, I meet with some of our largest customers worldwide, getting input from
them, and they're telling us what their current problems are.
Let me see if I can describe the landscape in a typical organization, and we can go
from there. You'll see why we created HAVEn.
Let’s visualize four different waves of data. Back in early '60s,'70s, even part of
the '80s, mainframes were the primary way to process data, and we used them for
operationalizing certain parts of data processing, where data was extremely highvalue. If you look at the cost of the systems, it was phenomenal.
Mundada
Then came the next wave in the ‘80s, where we went into what I call client-server computing,
and we already know several companies that we created in this space.
I’ve lived in Silicon Valley for almost 30 years now, and a whole bunch of new companies were
born in this space. I worked for a company, Postgres, which became Illustra, then became
Informix, and became IBM. If you look at that entire wave of OLTP technologies, we created
data-processing technologies designed to solve basic business problems.
Application software was created: CRM, supplier relationship management (SRM), you name it.
Many companies that did consulting around that were created. That was that second wave after
the mainframe.
Then came the third wave, where we took this data from all these transactional systems, brought
them together to find out some basic analysis, which we now call business analytics, to find out
who is my most profitable customer, what are they buying, why are they buying, and things of
that nature.
We created companies for that wave, and many technologies. Exadata, Teradata, Netezza, and a
whole bunch of companies and applications were born in that space.. That wave lasted for quite a
while.
What we're seeing now is that from 2003 onwards, something very fundamental has happened.
At least, that’s the way I‘ve been seeing this. If you look at the three Vs that Dan has described --

5. volume, velocity, and variety -- we’re talking about volumes that are growing exponentially. In
the past, they were growing linearly. That creates a very different kind of requirement.
More importantly, if you look at the variety that Dan mentioned, that’s really the key driver in
my mind. People are now routinely bringing in machine data, human data, and your traditional
structured warehouses, all of them together.
If you visualize a bar graph, you would see that 10 percent of the data that we now can monetize
is coming from traditional sources, whereas 90 percent of the data that we need to monetize is
now sitting in machine data and human data.
High velocity analytics
What we're trying to do with HAVEn is create a combined platform, where you can combine
these three different data types and do very high-velocity analytics.
As a simple example, if you look at Apache Web Server logs, that data is used historically by the
security people to see if anybody is breaking in. That data was being used by operational people
to see if machines aren’t overloaded.
More importantly the digital marketing guys now want to look at that data to see who's coming
to my website, what they’re buying, what they’re not buying, why they’re buying, and which
geographies they’re coming from. Then, they want to combine all these data sets with their
existing structured data to make sense out of it.
Today, it's a mess in the market. When we talk to our partners and customers, they’re saying that
they have point solutions for each of these things, and if you want to combine that data, it’s really
hard. That’s why we had to create HAVEn.
HAVEn is the fourth wave. HAVEn is specifically about big data, the fourth wave. If you look at
HP’s portfolio, we sell products and services across each of these waves, and the fastest growing
wave right now is the big-data wave. It’s growing at about 35 percent a year, according to
Gartner, and that's why we're excited about it.
Gardner: Now we know why you created it and what it’s supposed to do. Tell us a little bit more
about what’s included in HAVEn and why it is that you’ve been able to create a combination of
product and platform that accomplishes this very difficult task that you’ve described?
Mundada: That’s another very interesting thing. If you look at what’s required now to process
big data in its entirety, one product no longer can do it all. There is a very famous paper written
by some university professors that’s titled “One size does not fit all.” It proves that different data
structures are able to solve different kinds of data problems far more efficiently.

6. One way to think about big data is to think of it as a pile of dirt. It’s a big pile. In that pile, there’s
gold, silver, platinum, iron, and other metals you don’t even know. If the cost of mining that data
is high, obviously you’re going to go after only the platinum and some known objects that you
care about, because that’s all you can afford.
HAVEn is about bringing that cost of processing down to a very, very low level so you can go
after more metals. That means you have to bring together a set of technologies to be able to solve
this. If you look at the last three years, HP has made very significant amounts of investments in
the big-data space.
Best of breed
We bought companies that were best of breed to try to solve specific problems. We bought
Autonomy, Vertica, ArcSight, Fortify, TippingPoint, 3PAR Data, and Knightsbridge.
Now, we have a set of technologies to be able to combine them into a unique experience. Think
of it almost like Microsoft Office. Before you had Microsoft Office, you would buy a word
processor from one company, a spreadsheet from another company, and presentation software
from a third company.
Let’s say you wanted to create a simple table. If you had created it in a word processor or even a
spreadsheet, you couldn’t mix and match that. It was impossible to mix and match very different
types.
Then, Microsoft came to the table and said, “Look, here’s a simplified solution.” If you want to
create a table, go ahead and create it in PowerPoint. Or if you want to create more complicated
thing, put it in Excel. Then, take that Excel and put it in PowerPoint. Or, you can put the whole
thing into a Word document. That was the beauty of what Microsoft did.
We’re trying to do something similar for big data, make it very easy for people to combine all
these different engines and the different data types and write simple applications on it.
Gardner: What also is going on, other than product acquisitions, is recognizing the industry
standards and the H in HAVEn, being a representative of Hadoop, is an indication of that. Tell
me, beyond the products, what is binding them together, and why being an open and standard
space has its important role here too.
Mundada: Let’s look at HAVEn as a platform. HAVEn is really two different concepts. There’s
the HAVEn data platform, which we’ll talk about now, and there’s a HAVEn ecosystem, which
I’ll mention in a minute.
HAVEn means Hadoop, Autonomy, Vertica, Enterprise Security, and “n” applications. That’s the
acronym. So let’s look at one of these pieces, and why we need an architecture like this.

7. As I said, today you need to combine different sets of data techniques to solve different
problems, and they have to work seamlessly. That’s what we did with HAVEn. I’ve been with
HAVEn from day zero, before the project concept started, and I can tell you why and how we
added these pieces and how we’re trying to integrate them better.
If you look at Hadoop as an ecosystem part of that HAVEn, our story with Hadoop at HP is that
Hadoop is an integral part of HAVEn. We see a lot of our customers and partners betting on
Hadoop and we think it’s a good thing to keep Hadoop open and non-proprietary.
Leading vendors
We also today work with all leading Hadoop vendors, so we have shipping appliances as well
as reference architectures for both Cloudera and Hortonworks, and we’re working now with
MapR to create similar infrastructure. That’s our Hadoop’s story.
We’ve also found that our customers are saying they want some flexibility in Hadoop. Today,
they may want one vendor, and tomorrow, they may decide to go to another vendor for whatever
business reasons they choose to. They want to know if we can provide a simple management tool
that works across multiple Hadoop distributions.
As an example, we had to extend our Business Service Management (BSM) portfolio, so we can
manage Hadoop, Vertica, hardware, storage, and networking all from within one environment.
This is simply operationalizing it. Having a standardized set of hardware that matches multiple
Hadoop distributions was another thing we had to do. There are many such enterprise-class
innovations that you’ll see coming from HP.
But more than that, we also found that Hadoop is really good for certain kinds of applications
today, and obviously, the community will extend that. You will see more and more innovations
coming from that community and ecosystem.
Today, there are several areas where there are holes in Hadoop, or maybe they’re not as strong as
commercial products. One such area that you see is SQL. The SQL phase of Hadoop is going to
be one of the key differentiators across the different Hadoop packaging.
In that area, we have a technology called Vertica, which is the V part of HAVEn, and you’ll see
companies like Facebook, using a combination of both Hadoop and Vertica.
The classic use case we see is that people will bring all kinds of raw data, put it into Hadoop, and
do some batch processing there. Hadoop is great as a file system, a batch processing
environment. But then they’ll take pieces of that data and want to do deep analytics on it, like a
regression analytics, and they will put it into Vertica.
Vertica is, is an analytic database platform, and I will break up those three words. It’s a database.
It looks and feels like a database. It has SQL on it, open database connectivity (ODBC), and Java

8. database connectivity (JDBC) connectivity. You can run all kinds of tools on it, the ones you are
used to, Tableau, Pentaho, and Informatica. So from that perspective it’s a regular database.
What’s different is that it’s custom built for the fourth wave. It’s an analytic database, and by
that, I mean the underlying algorithms are completely designed from the ground up. Michael
Stonebraker who created the key products in the first wave and the second wave -- Ingres and
Postgres -- also created this at MIT from the ground up.
Data today
The intuition was that if you look at the processing of data today, it’s gone from having 10-20
columns per row to possibly thousands of columns. A social media company, for example, might
have 10,000 piece of information on me, and while they do processing, it’s going more linear. It’s
going regression oriented in a sense. You might say “Girish, age x, lives here, and likes y. What’s
the likelihood somebody else may like it?”
It’s meant for that kind of deep analytical process, a column-oriented structure. In those kinds of
applications, this database technology tends to be magnitudes faster, tens of times faster. That’s
one example of Hadoop and Vertica, and we can talk more about other pieces Autonomy and
Enterprise Security with you.
Gardner: So we see that there’s a platform that you put together. There’s an ecosystem that’s
supporting that. There are these binding standards that make the ecosystem and the platform
more symbiotic or allow the progress to take place across them. But other people are doing the
same thing. What’s making HAVEn different. What is it about HAVEn that you think is going to
be a winner in the marketplace?
Mundada: There are two different answers to it. Let me talk about how we’ve taken just not the
SQL piece of Hadoop, but how we extend it with other parts of HP that are unique to HAVEn.
It’s the breadth of it. Let’s see how we extend this simple combination of Hadoop and Vertica.
I said it’s an analytic database platform. If you look at that platform piece of it, with Vertica,
we’re able to drop in other codes that are user defined and user written. For example, you can
drop in R language routines, Java, C++, or C language routines directly into the database. Now,
we’re now able to combine the richness of our other portfolio.
Autonomy, which is the A part of HAVEn, is a unique technology. It's one of a kind. Some of the
largest governments and some of the largest organizations in the world, such as banks and
financial institutions, have this in production in what it's meant for, human information
processing, which is audio, video, and text.
As an example, you could take a video stream and ask simple questions. Tell me if an object is
moving from point A to point B or tell me what’s in the object. Is it a human? Is it a car? Can you
read number plates automatically?

9. And you could do some really sophisticated applications. Taking a car, we have cases where
police cars have video cameras mounted on the side, and as they’re driving by in a parking lot,
they can take photos of the number plates and compare it to stolen cars.
Crime detection
Imagine being able to take that technology and combining it automatically, through simple
SQL-like or simple REST API-like commands with SQL, with your existing data and creating
very sophisticated applications to understand your customer or for crime detection and things
like that.
Now let’s bring in the third of part of the puzzle, the E part, which is Enterprise Security. That’s
also unique. We have an entire portfolio, both for security as well as for operations management.
If you look at enterprise security and if you look at the Gartner Magic Quadrant, HP’s product set
has been in the leader space for several years in a row. They are the number one vendor in that
area.
Now, think about our portfolio of ArcSight, Fortify, Tipping Point, and other ESP products.
Imagine being able to take the data-collection algorithms of those, bringing it into this common
platform of HAVEn, combining it with other structured and unstructured data with just simple
commands. That’s something we can do uniquely.
Operations management is another area where we have hundreds of these machine logs. We can
collect them, break them open into modular pieces, and create new applications. You can go look
at our website, Operations Analytics, where with a simple slider, you can go back and forth in
time to millions of log files as if they were structured data.
We can do that uniquely, because we have that entire collection. Our BSM portfolio has been on
the market for 30 years. It’s one of the leaders. This is the open-view platform and this is one of
the things we can do uniquely at HP, bring all these things together.
That’s the breadth of our portfolio, but it simply doesn’t stop at this platform level. Remember, I
said that there are two concepts. There is a platform, and then there is the ecosystem. Let’s look
at the platform level first.
We have the whole HAVEn. We have the connectors and we ship these 700 connectors out of the
box. With simple commands, you can bring in social-media data in every language written. You
can bring in machine logs and structured logs. That’s the platform.
Let’s extend it further into the ecosystem part. The next thing that people were saying was, “We
want to use something very open. We have our own visualization tools. We have our own extract,

10. transform, load (ETL) tools that we’re used to. Can you just make them work?" And we said,
"Sure.”
That’s one of the things that we’re able to do now. With simple SQL, we can essentially write
simple queries across structured and unstructured data, Using Tableau, or any other tool that you
like, we can access this data through our connectors, but, more importantly, it let’s you hook in
your existing ETL tools into this, completely transparently.
Breadth and openness
So that’s the openness of the platform. It’s the breadth and the openness of the platform.
Breadth is not just about the software platform, but it’s about HP’s strength to bring together
hardware, software, and services.
Even with the platform, the HAVEn components in the middle, the connectors, and being able to
match them with matching hardware, our customers are asking, “Can you give us matching
hardware for Hadoop, so we don’t have to spend time setting it up?” That’s one of things that
HP can uniquely do, but more importantly we have appliances for Vertica, for example, which
are standardized.
If you look at the other side, our customers are also saying, “We understand that HP wants to
provide us all this, but we like openness and we like other partners.” So we said, “Fine, we’ll
leave this entire ecosystem open.” Our software will work with HP hardware and we can
optimize, but we also commit to working on everybody else’s hardware.
Our cloud story is that we’ll work on Amazon, as well as OpenStack. For example, if you want
to build a hybrid cloud, where part of your data resides on HP or your private environment using
OpenStack, that’s fine. If you want to put it in Amazon or Rackspace, no problem. We’ll help you
bridge all these. These are the kinds of enterprise-cloud innovations that HP is able to do, and
we’re open to this.
So to answer your question very succinctly, if there were three things I would pick where HP is
different, one is our breadth of our portfolio. We have very large breadth that we've brought
together.
It’s the openness of the platform. HP is known to be a very open company. If you look our
Hadoop story, we have an example. We didn’t create a proprietary Hadoop. We kept it open. If
you look at our visualization, we didn’t go and force a visualization technology on you. We kept
it open.
More importantly, if there is one key thing that you want to take home from what we've done
with HAVEn, it's not about feeds and not about speeds. It's about business value.

11. The reason we created HAVEn was to create that iPhone-like environment or Android-like
environment, where the vision is that you should be able to go to a website, say you have
standardized on the HAVEn platform, and then, be able to point and click and download an
application.
The end part of HAVEn is really the business value of it and that’s how we see HAVEn as
unique. There is nobody else, as far as we know, that has that end vision, where you can build the
applications yourself using standard tools, SQL, ODBC, REST API, JDBC,or you can buy readymade software that HP software has created.
We have packages across service, operations, and digital marketing. Or you can go with a
partner. The partner could be HP Enterprise Services, Accenture, Capgemini, or any of those big
partners. That’s something unique about the HP big-data ecosystem that doesn’t exist anywhere
else today.
Applications
Gardner: Girish, there is this other, I suppose, product of the first two -- the breadth and the
openness -- and that is this ecosystem that runs with applications. Applications are something
that take advantage of the platform, the capabilities, the breadth and depth of the data, and
information.
I wonder if you could explain a little bit more about the application side of it, perhaps through
examples of what people are already doing with these applications, and how they’re using them
in their business setting?
Mundada: That’s actually one of the most exciting parts of my job. As I said, I meet literally a
hundred customers a month. I'm traveling across the continents, and the use cases of big data that
I see are truly phenomenal. It really keeps you very motivated to keep doing more.
Let's look at a very broad level of why these things matter. Big data is not just about monetary
profits. It's really about what I call profits. It doesn’t have to be monetary. If you look at a simple
example, we have medical companies using data, using our technologies, to dramatically speed
up drug discovery hundreds of times more than they were able to with Hadoop.
That translates into just saving lives. At our Discover show, we saw that a very innovative
organization is using our technology to look at bio-diversity and save wildlife in the Amazon.
That’s unique, but those are like edge cases. If you look at a regular enterprise, what they want to
do at a very high level falls into three categories: Applications that HP itself is building,
applications that partners are building, and applications that customers themselves are building.
Let's start with the ones that HP is building. Today HP is shipping several applications, and I’ll
talk about a few of them. Even before I talk about these applications, let's look at why people

12. generally want to do this. They’re saying that they want to either increase revenues, so that’s
affecting the top line, or they want to decrease costs, so they can increase the bottom line. Third
is that they want to improve products and services. Those are really the three broad categories at
a very, very high level.
As I said, HAVEn isn’t about speeds and feeds. It's about really creating business value in a
hurry, so you get there before your competitors can.
From that perspective, there are three applications I’ll mention. In terms of increasing revenue,
we have a product that we ship called Digital Marketing Hub, and it combines the power of
Autonomy and Vertica to analyze all of your customer analytics.
You’re able to take your call center logs, your social media feeds, your emails, your phone
interactions and find out what the customer is really is saying, what they want and don't want,
and then, being able to optimize that interaction with the customer to create more revenue.
More precise answers
For example, when a customer calls knowing what they want, obviously you can tell them
more precise things. That’s one example.
Let's look at another example, where you want to decrease your bottom line or decrease your
costs. Operational Analytics is another software product we ship. We’re able to drive down costs
of debugging network troubles by 80 percent by combining all these logs from machines on a
very frequent basis.
We can look at this and say. "At this second, every machine was okay. A second later, machines
have gone down." I can look exactly at the incremental logs that showed up, using a simple pen
like a pointer, going through SQL-like data. That’s unique.
Those are the kinds of applications we’re able to create. It's not just these two. The other thing
people want is improve products and services. We have something called Service Anywhere,
where as you're calling, or as you're typing in commands and saying you want to find
information about that, the system is able to understand the meaning of what you’re saying.
Notice that this is not keyword search. This is meaning, where it's able to go through existing
case reports from customers, look at existing resolutions, and then say, “Okay, this might solve
your problem automatically.”
Imagine what that impacts. Your customers are happy, because the answers are quicker. We call
this ticketless ID, but more important, look at some other interesting ways of how this affects a
company.

13. For example, I was in Europe last couple of weeks ago. I was talking to a very large telco there,
and they said, “We have something like 20,000 call-center operators who are taking calls from
customers. Each call volume might take six minutes and some of them are repeat calls. That’s
really our problem.”
We worked out something that roughly could save them two minutes per call. That translates to
about a $100 million net saving per year. That’s really phenomenal. Those are one kind of
application that HP built.
Now imagine a customer wanting to build the same application themselves. That’s the beauty of
the HAVEn platform. On the same platform, you can buy HP built applications or you can build
your own.
Let's look NASCAR as an example. They did something very similar for customer analytics.
They are able to -- while the race is happening -- understand audio, television channels, radio,
broadcast, and social media and bring that all together as if it's one unique piece of data.
Then, they’re able to use that data in really innovative ways to further their sport and to create
more promotional dollars for just not themselves, but even the participants. That’s unique -being able to analyze mass scale human data.
Looking to the future
Gardner: Well, we've learned a lot about the market, the demand, why big data makes so
much sense. There is very large undertaking by HP around HAVEn and what it’s getting in terms
of openness, platforms, breadth, and these great examples of applications, but we need to look to
the future.
What's coming next in terms of HAVEn 2.0 or HAVEn 1.5. Dan, maybe you could update us on
how things are progressing, what you have in mind for the next versions of these products and,
therefore, the whole increasing as sum of the parts increases.
Wood: Dana, we've just announced HAVEn 2.0. The way Girish explained HAVEn there in
terms of the platform and the ecosystem and continuous innovation now is around both of those
pieces. It's really important to us to be driving the ecosystem, as well as the platform. So I’ll
speak to HAVEn 2.0 and one of the feature that’s the focus in driving HP forward.
In terms of the platform, there are the analytics engines that we have. Girish mentioned they
were best in class at the time that HP acquired them, and we continue to invest in R and D across
Autonomy, IDOL, Vertica, and the ArcSight Logger product. We recently announced new
versions of all three of those, improving the analytics capability and the usability and, just as
importantly, increasing the interoperability.

14. For example, we now have integration of the ArcSight Logger with the Autonomy IDOL engine
for analyzing unstructured human information. A really great use case of this is Logger was
previously enabling IT to understand data movements and potential threats and the risks in the
organization.
For example, if I were sending 50 percent of my email to a competitor, you could combine that
capability with the unstructured information analysis in Autonomy and understand by that the
information layer exactly what’s in that email, 50 percent of which is going to a competitor.
Let’s start putting that together and getting a powerful view of what an individual is doing and
whether it’s a risky individual in the organization, integrating those HAVEn engines and putting
more effort on integrating it into the Hadoop environment as well.
For example, we have just announced integration Hadoop connectors for Autonomy. A lot of
people are saying that they’re building this data lake with Hadoop and they want to have the
capability of putting some analytics into the unstructured information that exists in that Hadoop
data lake. Clearly, we’ve also got integration with Vertica in the Hadoop environment as well.
The other key thing within that on the engine is IDOL On Demand. At the moment, on an earlyaccess program, we’re making the IDOL engine available to developers as a cloud-based
offering, This is to encourage the independent developer community to take components of
IDOL with that social media analytics, whether it’s video or audio recognition, and start building
that into their own applications.
We believe the power of HAVEn will come from the combination of HP-provided applications
and also third-party applications on top.
Early-access program
We’re facilitating that with this initial early-access program on IDOL OnDemand, and also,
we’re investing in developer programs to make the whole HAVEn development platform far
easier for partners and independent developers to work with.
We’ve set up a HAVEn developer website, and stay tuned for some really fun events online and
physical events, where we’ll be getting the developer community together.
In terms of those applications that make the whole HAVEn ecosystem come to life, Girish has
mentioned some of them that we have announced over the last few weeks. So I’ll give you a
quick recap on those.
We have the Operations Analytics and Service Anywhere apps, both aimed at the CIO. And we
have the Digital Marketing Hub from HP aimed at marketing leaders in the organizations. These
are three applications that HP has packaged on the HAVEn platform.

15. And along with the HAVEn 2.0 announcement, we’re really pleased that six of the leading SI
partners -- Accenture, Capgemini, Deloitte, PwC, Accenture and Wipro -- themselves have put
marketing applications on top of HAVEn. And those guys have gotten fascinating mixtures of
very industry-specific analytics applications and more horizontal apps based on the priorities that
they’re chasing after.
So we’re really excited about that and expect to see many more announcements of partner
applications over the next few months.
The final piece of HAVEn 2.0 to support this whole ecosystem thing is a marketplace that we’ve
launched, where we’re populating our solutions and partner solutions to facilitate the whole
commerce side of those applications taking off in the market.
Gardner: Just to flesh out that last point, when you say a marketplace, is this an app store? Will
some of your partners that are able to create analytics-oriented applications on HAVEn then be
able to sell them? Is this a commerce site or is it a community site only at this point?
Mundada: The original vision of HAVEn was to be able to make it essentially like how you buy
applications on a mobile phone today. Once you have settled on a platform, the eventual vision is
to be able to go there and just download these applications. As Dan said, they’ve launched this
now and you will see much more stuff coming in this area.
Gardner: For those interested in learning more, those who might want to focus on one element
of HAVEn, it’s not all-inclusive. You don’t have to buy it all at once. It comes in parts. There are
on-ramps, and then you can expand. How do you get started? How do you learn about specific
parts of HAVEn? Which combinations would work for you?
One-stop resource
Wood: The first place to go is hp.com/haven. That’s your one-stop resource for information on
this platform, all of the engines that Girish alluded to. You can get the inspiration from some
amazing customer case studies we have on there -- insights from experts like Girish and other
people who are talking in depth about the individual engines.
And as you rightly say, Dana, it’s finding the right on-ramp for yourself. You can look at the
case studies we have, the use cases on big data in particular industries, and take a look at what
the specific pain point you have today. That’s the hp.com/haven website, and that gives you all of
that information.
You can also drill down from there, if you're a developer, and find the tools and resources that
we’ve spoken about to enable you to start building apps on top of HAVEn. That’s one part.
The whole power of HP behind this HAVEn platform is in enabling, from an infrastructure and
services point of view, to start building these big data analytics. A couple of key things here.

16. We started to build fully configured appliances around Hadoop and Vertica. So the Converged
System’s team in HP has launched the CS300, which enables you to have Vertica and Hadoop on
a preconfigured appliance. That’s a great starting point for someone early on in the big-data
analytics life cycle.
To expand on that, the Technology Services team is able to do full consulting on how to optimize
the overall infrastructure from the point of view of processing, sharing, and storing this vast
amount of information that all organizations are coping with today. That will then start to put in
things like 3PAR storage systems and other innovations across the HP hardware business.
Another place where I see customers often needing some help to get started is in understanding
exactly what the questions are that we need to be asking in terms of analytics and exactly what
algorithms and analytics we need to put in place to get going. This is where the Big Data
Discovery experience from HP comes in.
This is provided by the Enterprise Services Group. Those guys have data scientists and industry
experts who can actually help customers go through the design phase for a big-data platform and
than offer the HAVEn infrastructure supported by the ES Services team.
Finally, Dana, come and see us on the road. We’ll be at HP Discover in Las Vegas June 10-12.
We’re putting together several road shows and events across the main regions in Europe, the
Americas, and in Asia Pacific, where we will be taking HAVEn on the road. Take a look at that
hp.com/haven website, and details of the events will be found on there.
Key messages
Mundada: I wanted to add a couple of things that are a bit relevant. There are two key
messages: big data is really important and it’s disrupting business. Your competitors are going to
do it. You have a choice to either lead and do it yourself or you will be forced to follow. It’s one
of those things that are disrupting industries worldwide.
Now, when you think of big data, don’t think of pieces and don’t think of piece parts. It’s not like
you need a separate solution for human information, another for machine logs, and another for
structured data. You almost have to think of it holistically, because there are many kinds of newer
applications that I’m seeing regularly, where you have to bring all these data types together and
create joint applications.
Whichever technologies that you choose and settle on, think of that Microsoft Office-like
experience. You want to combine integrated solution across the entire stack and there aren’t that
many available in the market today. So whoever you work with, make sure that you’re able to
handle that entire piece as one giant puzzle.

17. Gardner: Very good. I’m afraid we we'll have to leave it there. You’ve been listening to an
executive-level discussion highlighting how the latest version of HP HAVEn produces new
business analytics value and strategic return. We have seen how big-data capabilities and
advanced business analytics have now really become essential to nearly any business activity.
This discussion marks the latest episode in the ongoing HP Big Data podcast series, where
leading edge adopters of data-driven business strategies share their success stories and where the
transformative nature of big data takes center stage.
Please join me now in thanking today’s guests. We’ve been talking with Girish Mundada, Chief
Technology Officer for HP HAVEn. Thanks so much, Girish.
Mundada: Thanks.
Gardner: And also Dan Wood, Worldwide Solution Marketing Lead for Big Data at HP
Software. Thank you, Dan.
Wood: Thanks, Dana.
Gardner: To learn more about how businesses anywhere can best capture knowledge, gain
deeper analysis, and rapidly and securely make those insights available to more people on their
terms, visit the HP HAVEn Resource Center at hp.com/haven.
I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your moderator for this ongoing
sponsored journey into how data is analyzed and used to advance the way we live and work.
Thanks so much for listening and do come back next time.
Listen to the podcast. Find it on iTunes. Sponsor: HP
Transcript of a BriefingsDirect podcast on how HP is developing products and platforms to help
businesses deal with the demands of big data in a competitive environment. Copyright Interarbor
Solutions, LLC, 2005-2014. All rights reserved.
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