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. Download the transcript. 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 on where the information management
market has been and where it's going, we're joined by
Matt Wolken, Executive Director and General Manager for Information Management at
Dell Software. Welcome, Matt. [Disclosure: Dell Software is a sponsor of BriefingsDirect podcasts.]
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 human resources (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?
How do I go out and use data to derive a better outcome for my business?
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.
We’re looking to enable customers to leverage those technologies for a smoother, more efficient, and more effective operation.
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.
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.
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.
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.
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.
Those are some of the major challenges -- complexity, cost, knowledge, and know-how.
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.
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.
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.
We’ve also assembled a tool chain in which the entirety of the chain delivers value as a whole.
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.
Doing that, you also solve the problem of how to make sure that the data that was provisioned was sanctioned.
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.
It's about incremental improvement as well as tangible improvement for
each and every step of the information management process.
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.
At each step, we've looked at how you ensure the performance of the data.
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. Download the transcript. 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.
You may also be interested in:
We
have to save all the informations, and while saving the information, we
also interact the other systems, for example the National Identity
System, through telecom systems, public switched telephone network (PSTN) systems. 
