Agnostic Tool Chain Key to Fixing the Broken State of Data and Information Management

Agnostic Tool Chain Key to Fixing the Broken State of Data
and Information Management
Transcript of a BriefingsDirect podcast on how Dell Software is working with companies to
manage internal and external data in all its forms.

Listen to the podcast. Find it on iTunes. Sponsor: Dell Software

Dana Gardner: Hi, this is Dana Gardner, Principal Analyst at Interarbor Solutions, and you're
listening to BriefingsDirect.

Today, we present a sponsored podcast discussion on better understanding the biggest challenges
businesses need to solve when it comes to data and information management.

We'll examine how a data dichotomy has changed the face of information
management. This dichotomy means that organizations, both large and small, not
only need to manage all of their internal data that provides intelligence about their
businesses, but they also need to manage the reams of increasingly external big
data that enables them to discover new customers and drive new revenue.

Lastly, our discussion will focus on bringing new levels of automation and precision to the task
of solving data complexity by embracing an agnostic, end-to-end tool chain approach to overall
data and information management.

Here now to share his insights with us on where the information management market has been
and where it's going, we're joined by Matt Wolken. He is the Executive Director and General
Manager for Information Management at Dell Software. Welcome, Matt.

Matt Wolken: Dana, thanks for having me. I appreciate it.

Gardner: From your perspective, what are the biggest challenges that businesses need to solve
now, when it comes to data and information management? What are the big hurdles that they're
facing?

Wolken: It's an interesting question. When we look at customers today, we're noticing how their
environments have significantly changed from maybe 10 or 15 years ago.

About 10 or 15 years ago, the problem was that data was sitting in individual databases around
the company, either in a database on the backside of an application, the customer relationship
management (CRM) application, the enterprise resource planning (ERP) application, or in data
marts around the company. The challenge was how to bring all this together to create a single
cohesive view of the company?

That was yesterday's problem, and the answer was technology. The technology was a single,
large data warehouse. All of the data was moved to it, and you then queried that larger data
warehouse where all of the data was for a complete answer about your company.

What we're seeing now is that there are many complexities that have been added to that situation
over time. We have different vendor silos with different technologies in them. We
have different data types, as the technology industry overall has learned to
capture new and different types of data -- textual data, semi-structured data, and
unstructured data -- all in addition to the already existing relational data. Now,
you have this proliferation of other data types and therefore other databases.

The other thing that we notice is that a lot of data isn't on premise any more. It's
not even owned by the company. It's at your software-as-a-service (SaaS) provider for CRM,
your SaaS provider for ERP, or your travel or HR provider. So data again becomes siloed, not
only by vendor and data type, but also by location. This is the complexity of today, as we notice
it.

Cohesive view

All of this data is spread about, and the challenge becomes how do you understand and
otherwise consume that data or create a cohesive view of your company? Then there is still the
additional social data in the form of Twitter or Facebook information that you wouldn't have had
in prior years. And it's that environment, and the complexity that comes with it, that we really
would like to help customers solve.

Gardner: When it comes to this so-called data dichotomy, is it oversimplified to say it's internal
and external, or is there perhaps a better way to categorize these larger sets that organizations
need to deal with?

Wolken: There's been a critical change in the way companies go about using data, and you
brought it out a little bit in the intro. There are some people who want to use
data for an outcome-based result. This is generally what I would call the line-
of-business concern, where the challenge with data is how do I derive more
revenue out of the data source that I am looking at?

What's the business benefit for me examining this data? Is there a new segment
I can codify and therefore market to? Is there a campaign that's currently
running that is not getting a good response rate, and if so, do I want to switch to another
campaign or otherwise improve it midstream to drive more real value in terms of revenue to the
company?

That’s the more modern aspect of it. All of the prior activities inside business intelligence (BI) --
let’s flip those words around and say intelligence about the business -- was really internally

focused. How do I get sanctioned data off of approved systems to understand the official
company point of view in terms of operations?

That second goal is not a bad goal. That's still a goal that's needed, and IT is still required to
create that sanctioned data, that master data, and the approved, official sources of data. But there
is this other piece of data, this other outcome that's being warranted by the line of business,
which is, how do I go out and use data to derive a better outcome for my business? That's more
operationally revenue-oriented, whereas the internal operations are around cost orientation and
operations.

So where you get executive dashboards for internal consumption off of BI or intelligence for the
business, the business units themselves are about visualization, exploration, and understanding
and driving new insights.

It's a change in both focus and direction. It sometimes ends up in a conflict between the groups,
but it doesn't really have to be that way. At least, we don't think it does. That's something that we
try to help people through. How do you get the sanctioned data you need, but also bring in this
third-party data and unstructured data and add nuance to what you are seeing about your
company.

Gardner: Just as 10 or 15 years ago the problem to solve was the silos of data within the
organization, is there any way in traditional technology offerings that allows this dichotomy to be
joined now, or do we need a different way in which to create insights, using both that internal
and external type of information?

Wolken: There are certainly ways to get to anything. But if you're still amending program after
program or technology after technology, you end up with something less than the best path, and
there might be new and better ways of doing things.

Agnostic tool chain

There are lots of ways to take a data warehouse forward in today's environment, manipulate
other forms of data so it can enter a data warehouse or relational data warehouse, and/or go the
other way and put everything into an unstructured environment, but there's also another way to
approach things, and that’s with an agnostic tool chain.

Tools have existed in the traditional sense for a long time. Generally, a tool is utilized to hide
complexity and all of the issues underneath the tool itself. The tool has intelligence to
comprehend all of the challenges below it, but it really abstracts that from the user.

We think that instead of buying three or four database types, a structured database, something
that can handle text, a solution that handles semi-structured or structured, or even a high
performance analytical engine for that matter, what if the tool chain abstracts much of that

complexity? This means the tools that you use every day can comprehend any database type,
data structure type, or any vendor changes or nuances between platforms.

That's the strategy we’re pursuing at Dell. We’re defining a set of tools, not the underlying
technologies or proliferation of technologies, but the tools themselves, so that the day-to-day
operations are hidden from the complexity of those underlying sources of vendor, data type, and
location.

That's how we really came at it -- from a tool-chain perspective, as opposed to deploying
additional technologies. We’re looking to enable customers to leverage those technologies for a
smoother, more efficient, and more effective operation.

Gardner: Am I right then in understanding that this is at more of a meta level, above the
underlying technologies, but that, in a sense, makes the whole greater than the sum of the parts of
those technologies?

Wolken: That’s a fair way of looking at it. Let's just take data integration as a point. I can
sometimes go after certain siloed data integration products. I can go after a data product that goes
after cloud resources. I can get a data product that only goes after relational. I can get another
data product to extract or load into Hive or Hadoop. But what if I had one that could do all of
that? Rather than buying separate ones for the separate use cases, what if you just had one?

Metadata, in one way, is a descriptor language, if I use it in that sense. Can I otherwise just see
and describe everything below it, or can I actually manipulate it as well? So in that sense, it's a
real tool to actually manipulate and cause the effective change in the environment.

Gardner: I'd like to go into more of the challenges, but before we do that, what are the stakes
here? What do you get if you do this right? If you can, in fact, manage across various technology
types and formats, across relational and unstructured data, internal and external data sources and
providers.

Are we talking iterative change, a step change, or is it something that is a bit larger and that we
might have some other examples of companies when they do this well can really demonstrate
something perhaps quite unique in terms of a new level of accomplishment?

Institutional knowledge

Wolken: There are a couple of ways we think about it, one of which is institutional
knowledge. Previously, if you brought in a new tool into your environment to examine a new
database type, you would probably hire a person from the outside, because you needed to find
that skill set already in the market in order to make you productive on day one.

Instead of applying somebody who knows the organization, the data, the functions of the
business, you would probably hire the new person from the outside. That's generally retooling
your organization.

Or, if you switch vendors, that causes a shift as well. One primary vendor stack is probably a
knowledge and domain of one of your employees, and if you switch to another vendor stack or
require another vendor stack in your environment, you're probably going to have to retool yet
again and find new resources. So that's one aspect of human knowledge and intelligence about
the business.

There is a value to sharing. It's a lot harder to share across vendor environments and data
environments if the tools can't bridge them. In that case, you have to have third-party ways to
bridge those gaps between the tools. If you have sharing that occurs natively in the tool, then you
don't have to cross that bridge, you don't have the delay, and you don't have the complexity to get
there.

So there is a methodology within the way you run the environment and the way employees
collaborate that is also accelerated. We also think that training is something that can benefit from
this agnostic approach.

But also, generically, if you're using the same tools, then things like master data management
(MDM) challenges become more comprehensive, if the tool chain understands where that MDM
is coming from, and so on.

You also codify how and where resources are shared. So if you have a person who has to
provision data for an analyst, and they are using one tool to reach to relational data, another to
reach into another type of data, or a third-party tool to reach into properties and SaaS
environments, then you have an ineffective process.

You're reaching across domains and you're not as effective as you would be if you could do that
all with one tool chain.

So those are some of the high-level ideas. That's why we think there's value there. If you go back
to what would have existed maybe 10 or 15 years ago, you had one set of staff who used one set
of tools to go back against all relational data. It was a construct that worked well then. We just
think it needs to be updated to account for the variance within the nuances that have come to the
fore as the technology has progressed and brought about new types of technology and databases.

Gardner: As for business benefits, we hear a lot about businesses being increasingly data driven
and information driven, rather than a hunch, intuition, or gut instinct. Also, there's an ability to
find new customers in much more cost-effective ways, taking advantage of the social networks,
for example. So when you do this well, what are typically some of the business paybacks, and do
they outweigh the cost more than previous investments in data would have?

Investment cycles

Wolken: It all depends on how you go about it. There are lots of stories about people who go
on these long investment cycles into some massive information management strategy change
without feeling like they got anything out of it, or at least were productive or paid back the fee.

There's a different strategy that we think can be more effective for organizations, which is to
pursue smaller, bite-size chunks of objective action that you know will deliver some concrete
benefit to the company. So rather than doing large schemes, start with smaller projects and
pursue them one at a time incrementally -- projects that last a week and then you have 52
projects that you know derive a certain value in a given time period.

Other things we encourage organizations to do deal directly with how you can use data to
increase competitiveness. For starters, can you see nuances in the data? Is there a tool that gives
you the capability to see something you couldn't see before? So that's more of an analytical or
discovery capability.

There's also a capability to just manage a given data type. If I can see the data, I can take
advantage of it. If I can operate that way, I can take advantage of it.

Another thing to think about is what I would call a feedback mechanism, or the time or duration
of observation to action. In this case, I'll talk about social sentiment for a moment. If you can
create systems that can listen to how your brand is being talked about, how your product is being
talked about in the environment of social commentary, then the feedback that you're getting can
occur in real time, as the comments are being posted.

Now, you might think you'll get that anyway. I would have gotten a letter from a customer two
weeks from now in the postal system that provided me that same feedback. That’s true, but
sometimes that two weeks can be a real benefit.

Imagine a marketing campaign that's currently running in the East, with a companion program in
the West that's slightly different. Let's say it's a two-week program. It would be nice if, during the
first week, you could be listening to social media and find out that the campaign in the West is
not performing as well as the one in the East, and then change your investment thesis around the
program -- cancel the one that's not performing well and double down on the one that's
performing well.

There's a feedback mechanism increase that also can then benefit from handling data in a modern
way or using more modern resources to get that feedback. When I say modern resources,
generally that's pointing towards unstructured data types or textual data types. Again, if you can
comprehend and understand those within your overall information management status, you now
also have a feedback mechanism that should increase your responsiveness and therefore make
your business more competitive as well.

Gardner: I think the whole concept of the immediacy to feedback, applied across various
aspects of business -- planning, production, marketing, go-to market, research, and to uses -- then

that's been the Holy Grail of business for a long time. It's just been very difficult to do. Now, we
seem to be getting closer to the ability to do it at scale and at reasonable cost. So, these are very
interesting times.

Now, given that these payoffs could be so substantial, what's preventing people from getting to
this Holy Grail? What's between them and the realization?

It's the complexity

Wolken: I think it's complexity of the environment. If you only had relational systems inside
your company previously, now you have to go out and understand all of the various systems you
can buy, qualify those systems, get pure feedback, have some proofs of concept (POCs) in
development, come in and set all these systems up, and that just takes a little bit of time. So the
more complexity you invite into your environment, the more challenges you have to deal with.

After that, you have to operate and run it every day. That's the part where we think the tool chain
can help. But as far as understanding the environment, having someone who can help you walk
through the choices and solutions and come up with one that is best suited to your needs, that’s
where we think we can come in as a vendor and add lots of value.

When we go in as a vendor, we look at the customer environment as it was, compare that to what
it is today, and work to figure out where the best areas of collaboration can be, where tools can
add the most value, and then figure out how and where can we add the most benefit to the user.

What systems are effective? What systems collaborate well? That's something that we have tried
to emulate, at least in the tool space. How do you get to an answer? How do you drive there?
Those are the questions we’re focused on helping customers answers.

For example, if you've never had a data warehouse before, and you are in that stage, then
creating your first one is kind of daunting, both from a price perspective, as well as complexity
perspective or know-how. The same thing can occur on really any aspect -- textual data,
unstructured data, or social sentiment.

Each one of those can appear daunting if you don't have a skill set, or don't have somebody
walking you through that process who has done it before. Otherwise, it's trying to put your hands
on every bit of data and consume what you can and learning through that process.

Those are some of the things that are really challenging, especially if you're a smaller firm that
has a limited number of staff and there's this new demand from the line of business, because they
want to go off in a different direction and have more understanding that they couldn't get out of
existing systems.

How do you go out and attain that knowledge without duplicating the team, finding new vendor
tools, and adding complexity to your environment, maybe even adding additional data sources,

and therefore more data-storage requirements. Those are some of the major challenges --
complexity, cost, knowledge, and know-how.

Gardner: It's interesting that you mentioned mid-market organizations. Some of these
infrastructure and data investments were perhaps completely out of their reach until a new way
to approach the problems through the tool chain, through cloud, through other services and on-
demand offerings.

What is it now about the new approach to these problems that you think allows the fruits of this
to be distributed more down market? Why are mid-market organizations now more able to avail
themselves of some of these values and benefits than in the past?

Mid-market skills

Wolken: As the products are well-known, there is more trained staff that understands the more
common technologies. There are more codified ways of doing things that a business can take
advantage of, because there's a large skill set, and most of the employees may already have that
skill set as you bring them into the company.

There are also some advantages just in the way technologies have advanced over the years.
Storage used to be very expensive, and then it got a little cheaper. Then solid-state drives (SSD)
came along and then that got cheaper as well. There are some price point advantages in the
coming years, as well.

Dell overall has maintained the status that we started with when Michael Dell started recreating
PCs in his dorm room from standard product components to bring the price down. That model of
making technology attainable to larger numbers of people has continued throughout Dell’s
history, and we’re continuing it now with our information management software business.

We’re constantly thinking about how we can reduce cost and complexity for our customers. One
example would be what we call Quickstart Data Warehouse. It was designed to democratize data
to a lower price point, to bring the price and complexity down to a much lower space, so that
more people can afford and have their first data warehouse.

We worked with our partner Microsoft, as well as Dell’s own engineering team, and then we
qualified the box, the hardware, and the systems to work to the highest peak performance. Then,
we scripted an upfront install mechanism that allows the process to be up and running in 45
minutes with little more than directing a couple of IP addresses. You plug the box in, and it
comes up in 45 minutes, without you having to have knowledge about how to stand up, integrate,
and qualify hardware and software together for an outcome we call a data warehouse.

Another thing we did was include Boomi, which is a connector to automatically go out and
connect to the data sources that you have. It's the mechanism by which you bring data into it.

And lastly, we included services, in case there were any other questions or problems you had to
set it up.

If you have a limited staff, and if you have to go out and qualify new resources and things you
don't understand, and then set them up and then actually run them, that’s a major challenge.
We're trying to hit all of the steps, and the associated costs -- time and/or personnel costs – and
remove them as much as we can.

It's one way vendors like Dell are moving to democratize business intelligence a little further,
bring it to a lower price point than customers are accustomed too and making it more available to
firms that either didn’t have that luxury of that expertise link sitting around the office, or who
found that the price point was a little too high.

Gardner: You mentioned this concept of the tool chain several times. I'd like to hear a bit more
about why that approach works, and even more detail about what I understand to be important
elements of it -- being agnostic to the data type, holistic management, complete view, and then of
course integrate it.

In addition to the package, it sounds from your earlier comments that you want to be able to
approach these daunting issues iteratively, so that you can bite off certain chunks. What is it
about the tool chain that accomplishes both a comprehensive value, but also allows it to be
adopted on a fairly manageable path, rather than all at once?

Wolken: One of the things we find advantageous about entering the market at this point in time
is that we're able to look at history, observe how other people have done things over time, and
then invest in the market with the realization that maybe something has changed here and maybe
a new approach is needed.

Different point of view

Whereas the industry has typically gone down the path of each new technology or
advancement of technology requires a new tool, a new product, or a new technology solution,
we’ve been able to stand back and see the need for a different approach. We just have a different
point of view, which is that an agnostic tool chain can enable organizations to do more.

So when we look at database tools, as an example, we would want a tool that works against all
database types, as opposed to one that works against only a single vendor or type of data.

The other thing that we look at is if you walk into an average company today, there are already a
lot of things laying around the business. A lot of investment has already been made.

We wanted to be able to snap in and work with all of the existing tools. So, each of the tools that
we’ve acquired, or have created inside the company, were made to step into an existing

environment, recognize that there were other products already in the environment, and recognize
that they probably came from a different vendor or work on a different data type.

That’s core to our strategy. We recognize that people were already facing complexity before we
even came into the picture, so we’re focused on figuring out how we snap into what they already
have in place, as opposed to a rip-and-replace strategy or a platform strategy that requires all of
the components to be replaced or removed in order for the new platform to take its place.

What that means is tools should be agnostic, and they should be able to snap into an environment
and work with other tools. Each one of the products in the tool chain we’ve assembled was
designed from that point of view.

But beyond that, we’ve also assembled a tool chain in which the entirety of the chain delivers
value as a whole. We think that every point where you have agnosticism or every point where
you have a tool that can abstract that lower amount of complexity, you have savings.

You have a benefit, whether it’s cost savings, employee productivity, or efficiency, or the ability
to keep sanctioned data and a set of tools and systems that comprehend it. The idea being that the
entirety of the tool chain provides you with advantages above and beyond what the individual
components bring.

Now, we're perfectly happy to help a customer at any point where they have difficultly and any
point where our tools can help them, whether it's at the hardware layer, from the traditional Dell
way, at the application layer, considering a data warehouse or otherwise, or at the tool layer. But
we feel that as more and more of the portfolio – the tool chain – is consumed, more and more
efficiency is enabled.

Gardner: It sounds as if rather than look at the ecosystem that’s in place in an organization as a
detriment, you're trying to make that into an asset, and then even looking further to new products
available to bring that in. So I guess partnering becomes important.

Already-made investment

Wolken: Everything is an already-made investment in the company. If the premise to rip and
replace is from the get-go, then you're really removing the institutional knowledge, the training
of the staff, and the investment into the product, not to mention maybe the integration work.
That's not something we wanted to start out with. We wanted to recognize and leverage what was
there and provide value to that already existing environment.

One of the core values that we were looking at from a design point is how do you fit into an
environment and how do you add value to it, not how do you cause replacement or destruction of
an existing environment in order to provide benefit.

Gardner: We have been talking about the tool chain in terms of its value for analytics and
intelligence about the business and bringing in more types of data and information from external
sources.

It also sounds to me as if this sets you up for a lifecycle benefits, not just on the business benefits,
but also on the IT benefits, for things like a better backup and recovery, a better disaster recovery
strategy, perhaps looking towards more storage efficiency. Is there an intramural benefit from the
IT side to doing this in the fashion you have been describing as well?

Wolken: We looked at the strategy and said if you manage this as a data lifecycle, and that’s
really what we think about it as, then where does data first show up in a company? That’s inside
of a database on the backside of an application most likely.

And where is it last used inside of a company? That would generally be just before retirement or
long-term retention of the data. Then the question becomes how do you manipulate and
otherwise utilize the data for the maximum benefit in the middle?

When we looked at that, one of the problems that you uncover is that there's a lot of data being
replicated in a lot of places. One of the advantages that we've put together in the tool chain was
to use virtualization as a capability, because you know where data came from and you know that
it was sanctioned data. There's no reason to replicate that to disk in another location in the
company, if you can just reach into that data source and pull that forward for a data analyst to
utilize.

You can virtually represent that data to the user, without creating a new repository for that
person. So you're saving on storage and replication costs. So if you’re looking for where is there
efficiency in the lifecycle of data and how can you can cut some of those costs, that’s something
that jumps right out.

Doing that, you also solve the problem of how to make sure that the data that was provisioned
was sanctioned. By doing all of these things, by creating a virtual view, then providing that view
back to the analyst, you're really solving multiple pieces of the puzzle at the same time. It really
enables you to look at it from an information-management point of view.

Gardner: That's interesting, because you can not only get better business outcome benefits and
analytics benefits, but you can simplify and reduce your total cost of ownership from the IT
perspective. That's kind of another Holy Grail out there, to be able to do more with less.

One of the advantages

Wolken: That's what we think one of the advantages can be, and certainly, as you have the
advantage to stand on the shoulders of people who have come before you and look at how the
environment’s changed, you can notice some of these real minor changes and bring them

forward. That's what we want to do with IT as partners and with the solution that we bring
forward.

Gardner: How should enterprises and mid-market firms get started? Are there some proven
initiation points, methods, or cultural considerations when one wants to move from that
traditional siloed platform and integrate them along the way, an approach more towards this
integrated, comprehensive tool-chain approach?

Wolken: There are different ways you can think about it. Generally, most companies aren’t just
out there asking how they can get a new tool chain. That's not really the strategy most people are
thinking about. What they are asking is how do I get to the next stage of being an intelligent
company? How do I improve my maturity in business intelligence? How would I get from Excel
spreadsheets without a data warehouse to a data warehouse and centralized intelligence or
sanctioned data?

Each one of these challenges come from a point of view of, how do I improve my environment
based upon the goals and needs that I am facing? How do I grow up as a company and get to be
more of a data-based company?

Somebody else might be faced with more specific challenges, such a line of business is now
asking me for Twitter data, and we have no systems or comprehension to understand that. That's
really the point where you ask, what's going to be my strategy as I grow and otherwise improve
my business intelligence environment, which is morphing every year for most customers.

That's the way that most people would start, with an existing problem and an objective or a goal
inside the company. Generically, over time, the approach to answering it has been you buy a new
technology from a new vendor who has a new silo, and you create a new data mart or data
warehouse. But this is perpetuating the idea that technology will solve the problem. You end up
with more technologies, more vendor tools, more staff, and more replicated data. We think this
approach has become dated and inefficient.

But if, as an organization, you can comprehend that maybe there is some complexity that can be
removed, while you're making an investment, then you free yourself to start thinking about how
you can build a new architecture along the way. It's about incremental improvement as well as
tangible improvement for each and every step of the information management process.

So rather than asking somebody to re-architect and rip and replace their tool chain or the way
they manage the information lifecycle, I would say you sort of lean into it in a way.

If you're really after a performance metric and you feel like there is a performance issue in an
environment, at Dell we have a number of resources that actually benchmark and understand the
performance and where bottlenecks are in systems.

So we can look at either application performance management issues, where we understand the
application layer, or we have a very deep and qualified set of systems around databases and data
warehouse performance to understand where bottlenecks are either in SQL language or

elsewhere. There are a number of tools that we have to help identify where a bottleneck or issue
might be from just a pure performance perspective as well.

Strategic position

Gardner: That might be a really good place to start -- just to learn where your performance
issues are and then stake out your strategic position based on a payback for improving on your
current infrastructure, but then setting the stage for new capabilities altogether.

Wolken: Sometimes there’s an issue occurring inside the database environment. Sometimes it's
at the integration layer, because integration isn’t happening as well as you think. Sometimes it's
at the data warehouse layer, because of the way the data model was set up. Whatever the case,
we think there is value in understanding the earlier parts of the chain, because if they’re not
performing well, the latter parts of the chain can’t perform either.

And so at each step, we've looked at how you ensure the performance of the data. How do you
ensure the performance of the integration environment? How do you ensure the performance of
the data warehouse as well? We think if each component of the tool chain in working as well as it
should be, then that’s when you enable the entirety of your solution implementation to truly
deliver value.

Gardner: Great. I'm afraid we we'll have to leave it there. We're about out of time. You've been
listening to a sponsored BrieﬁngsDirect podcast discussion on better understanding the
challenges businesses need to solve when it comes to improved data and information
management.

And we have seen how organizations, not only need to manage all of their internal data that
provides intelligence about the businesses, but also increasingly the reams of external data that
enables them to improve on whole new business activities like discovering additional customers
and driving new and additional revenue.

And we've learned more about how new levels of automation and precision can be applied to the
task of solving data complexity and doing that to a tool chain of agnostic and capability.

I want to thank our guest. We have been here with Matt Wolken; Executive Director and General
Manager for Information Management Software at Dell Software. Thanks so much, Matt.

Wolken: Thank you so much as well.

Gardner: This is Dana Gardner, Principal Analyst at Interarbor Solutions. Thanks again to our
audience for joining us, and do come back next time.

Listen to the podcast. Find it on iTunes. Sponsor: Dell Software

Transcript of a BriefingsDirect podcast on how Dell Software is working with companies to
manage internal and external data in all its forms. Copyright Interarbor Solutions, LLC,
2005-2013. All rights reserved.

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