Transcript of a BriefingsDirect podcast on current misconceptions about big data and how organizations should approach a big-data project.

1. Want a Data-Driven Culture? Start Sorting Out the BI and
Big Data Myths Now
Transcript of a BriefingsDirect podcast on current misconceptions about big data and how
organizations should approach a big-data project.
Listen to the podcast. Find it on iTunes. Sponsor: Dell
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 debunking some major
myths around big data. It used to be that data was the refuse of business
applications, a necessary cleanup chore for audit and compliance sake. But
now, as analytics grow in importance for better running businesses and in
knowing and predicting dynamic market trends and customer wants in real-
time, data itself has become the killer application.
As the volumes and types of value data are brought to bear on business analytics, the means to
manage and exploit that sea of data has changed rapidly too. But that doesn't mean that the so-
called big data is beyond the scale of mere business mortals or too costly or complex for mid-
size companies to master. [Disclosure: Dell is a sponsor of BriefingsDirect podcasts.]
So we're here to pose some questions, many of them the stuff of myth, and then find better
answers to why making data and big data the progeny of innovative insight is critical for more
companies.
To help identify and debunk the myths around big data so that you can enjoy the value of those
analytics better, please join me in welcoming our guest. We're here with Darin Bartik. He's the
Executive Director of Products in the Information Management Group at Dell Software.
Welcome, Darin.
Darin Bartik: Thanks, Dana. Good to be with you.
Gardner: We seem to be at an elevated level of hype around big data. I guess a good thing about
that is it’s a hot topic and it’s of more interest to more people nowadays, but we seem to have
veered away from the practical and maybe even the impactful. Are people losing sight of the
business value by getting lost in speeds and feeds and technical jargon? Is there some sort of a
disconnect between the providers and consumers of big data?
Bartik: I'm sure we're going to get into a couple of different areas today, but you hit the nail on
the head with the first question. We are experiencing a disconnect between the technical side of

2. big data and the business value of big data, and that’s happening because we’re digging too
deeply into the technology.
With a term like big data, or any one of the trends that the information
technology industry talks about so much, we tend to think about the technical
side of it. But with analytics, with the whole conversation around big data,
what we've been stressing with many of our customers is that it starts with a
business discussion. It starts with the questions that you're trying to answer
about the business; not the technology, the tools, or the architecture of solving
those problems. It has to start with the business discussion.
That’s a pretty big flip. The traditional approach to business intelligence (BI) and reporting has
been one of technology frameworks and a lot of things that were owned more by the IT group.
This is part of the reason why a lot of the BI projects of the past struggled, because there was a
disconnect between the business goals and the IT methods.
So you're right. There has been a disconnect, and that’s what I've been trying to talk a lot about
with customers -- how to refocus on the business issues you need to think about, especially in the
mid-market, where you maybe don’t have as many resources at hand. It can be pretty confusing.
Part of the hype cycle
The other thing you asked is, “Are vendors confusing people?" Without disparaging the
vendors like us, or anyone else, that’s part of the problem of any hype cycle. Many people
jumped on the bandwagon of big data. Just like everyone was talking cloud.
Everyone was talking virtualization, bring your own device (BYOD), and
so forth.
Everyone jumps on these big trends. So it's very confusing for customers,
because there are many different ways to come at the problem. This is why I
keep bringing people back to staying focused on what the real opportunity
is. It’s a business opportunity, not a technical problem or a technical
challenge that we start with.
Gardner: Right. We don’t want to lose the track of the ends because the means seem to be so
daunting. We want to keep our focus on the ends and then find the means. Before we go into our
myths, tell me a little bit, Darin, about your background and how you came to be at Dell.
Bartik: I've been a part of Dell Software since the acquisition of Quest Software. I was a part of
that organization for close to 10 years. I've been in technology coming up on 20 years now. I
spent a lot of time in enterprise resource planning (ERP), supply chain, and monitoring,
performance management, and infrastructure management, especially on the Microsoft side of
the world.

3. Most recently, as part of Quest, I was running the database management area -- a business very
well-known for its products around Oracle, especially Toad, as well as our SQL Server
management capabilities. We leveraged that expertise when we started to evolve into BI and
analytics.
I started working with Hadoop back in 2008-2009, when it was still very foreign to most people.
When Dell acquired Quest, I came in and had the opportunity to take over the Products Group in
the ever-expanding world of information management. We're part of the Dell Software Group,
which is a big piece of the strategy for Dell over all, and I'm excited to be here.
Gardner: Great. Even the name "big data" stirs up myths right from the get-go, with "big" being
a very relative term. Should we only be concerned about this when we have more data than we
can manage? What is the relative position of big data and what are some of the myths around the
size issue?
Bartik: That’s the perfect one to start with. The first word in the definition is actually part of the
problem. "Big." What does big mean? Is there a certain threshold of petabytes that you have to
get to? Or, if you're dealing with petabytes, is it not a problem until you get to exabytes?
It’s not a size issue. When I think about big data, it's really a trend that has happened as a result
of digitizing so much more of the information that we all have already and that we all produce.
Machine data, sensor data, all the social media activities, and mobile devices are all contributing
to the proliferation of data.
It's added a lot more data to our universe, but the real opportunity is to look for small elements of
small datasets and look for combinations and patterns within the data that help answer those
business questions that I was referencing earlier.
It's not necessarily a scale issue. What is a scale issue is when you get into some of the more
complicated analytical processes and you need a certain data volume to make it statistically
relevant. But what customers first want to think about is the business problems that they have.
Then, they have to think about the datasets that they need in order to address those problems.
Big-data challenge
That may not be huge data volumes. You mentioned mid-market earlier. When we think about
some organizations moving from gigabytes to terabytes, or doubling data volumes, that’s a big
data challenge in and of itself.
Analyzing big data won't necessarily contribute to your solving your business problems if you're
not starting with the right questions. If you're just trying to store more data, that’s not really the
problem that we have at hand. That’s something that we can all do quite well with current storage
architectures and the evolving landscape of hardware that we have.

4. We all know that we have growing data, but the exact size, the exact threshold that we may cross,
that’s not the relevant issue.
Gardner: I suppose this requires prioritization, which has to come from the business side of the
house. As you point out, some statistically relevant data might be enough. If you can extrapolate
and you have enough to do that, fine, but there might be other areas where you actually want to
get every little bit of possible data or information relevant, because you don't know what you're
looking for. They are the unknown unknowns. Perhaps there's some mythology about all data. It
seems to me that what’s important is the right data to accomplish what it is the business wants.
Bartik: Absolutely. If your business challenge is an operational efficiency or a cost problem,
where you have too much cost in the business and you're trying to pull out operational expense
and not spend as much on capital expense, you can look at your operational data.
Maybe manufacturers are able to do that and analyze all of the sensor, machine, manufacturing
line, and operational data. That's a very different type of data and a very different type of
approach than looking at it in terms of sales and marketing.
If you're a retailer looking for a new set of customers or new markets to enter in terms of
geographies, you're going to want to look at maybe census data and buying-behavior data of the
different geographies. Maybe you want datasets that are outside your organization entirely. You
may not have the data in your hands today. You may have to pull it in from outside resources. So
there's a lot of variability and prioritization that all starts with that business issue that you're
trying to address.
Gardner: Perhaps it's better for the business to identify the important data, rather than the IT
people saying it’s too big or that big means we need to do something different. It seems like a
business term rather than a tech term at this point.
Bartik: I agree with you. The more we can focus on bringing business and IT to the table
together to tackle this challenge, the better. And it does start with the executive management in
the organization trying to think about things from that business perspective, rather than starting
with the IT infrastructure management team.
Gardner: What’s our second myth?
Bartik: I'd think about the idea of people and the skills needed to address this concept of big
data. There is the term "data scientist" that has been thrown out all over the place lately. There’s a
lot of discussion about how you need a data scientist to tackle big data. But “big data” isn't
necessarily the way you should think about what you’re trying to accomplish. Instead, think
about things in terms of being more data driven, and in terms of getting the data you need to
address the business challenges that you have. That’s not always going to require the skills of a
data scientist.

5. Data scientists rare
Isuspect that a lot of organizations would be happy to hear something like that, because data
scientists are very rare today, and they're very expensive, because they are rare. Only certain
geographies and certain industries have groomed the true data scientist. That's a unique blend
between a data engineer and someone like an applied scientist, who can think quite differently
than just a traditional BI developer or BI programmer.
Don’t get stuck on thinking that, in order to take on a data-driven approach, you have to go out
and hire a data scientist. There are other ways to tackle it. That’s where you're going to combine
people who can do the programming around your information, around the data management
principles, and the people who can ask and answer the open-minded business questions. It
doesn’t all have to be encapsulated into that one magical person that’s known now as the data
scientist.
Gardner: So rather than thinking we need to push the data and analytics and the ability to
visualize and access this through a small keyhole, which would be those scientists, the PhDs, the
white lab coats, perhaps there are better ways now to make those visualizations and allow people
to craft their own questions against the datasets. That opens the door to more types of people
being able to do more types of things. Does that sum it up a bit?
Bartik: I agree with that. There are varying degrees of tackling this problem. You can get into
very sophisticated algorithms and computations for which a data scientist may be the one to do
that heavy lifting. But for many organizations and customers that we talk to everyday, it’s
something where they're taking on their first project and they are just starting to figure out how to
address this opportunity.
For that, you can use a lot of the people that you have inside your organization, as well
potentially consultants that can just help you break through some of the old barriers, such as
thinking about intelligence, based strictly on a report and a structured dashboard format.
That’s not the type of approach we want to take nowadays. So often a combination of
programming and some open-minded thinking, done with a team-oriented approach, rather than
that single keyhole person, is more than enough to accomplish your objectives.
Gardner: It seems also that you're identifying confusion on the part of some to equate big data
with BI and BI with big data. The data is a resource that the BI can use to offer certain values,
but big data can be applied to doing a variety of other things. Perhaps we need to have a sub-
debunking within this myth, and that is that big data and BI are different. How would you define
them and separate them?
Bartik: That's a common myth. If you think about BI in its traditional, generic sense, it’s about
gaining more intelligence about the business, which is still the primary benefit of the opportunity

6. this trend of big data presents to us. Today, I think they're distinct, but over time, they will come
together and become synonymous.
I equate it back to one of the more recent trends that came right before big data, cloud. In the
beginning, most people thought cloud was the public-cloud concept. What’s turned out to be true
is that it’s more of a private cloud or a hybrid cloud, where not everything moved from an on-
premise traditional model, to a highly scalable, highly elastic public cloud. It’s very much a mix.
They've kind of come together. So while cloud and traditional data centers are the new
infrastructure, it’s all still infrastructure. The same is true for big data and BI, where BI, in the
general sense of how can we gain intelligence and make smarter decisions about our business,
will include the concept of big data.
Better decisions
So while we'll be using new technologies, which would include Hadoop, predictive analytics,
and other things that have been driven so much faster by the trend of big data, we’ll still be
working back to that general purpose of making better decisions.
One of the reasons they're still different today is because we’re still breaking some of the
traditional mythology and beliefs around BI -- that BI is all about standard reports and standard
dashboards, driven by IT. But over time, as people think about business questions first, instead of
thinking about standard reports and standard dashboards first, you’ll see that convergence.
Gardner: We probably need to start thinking about BI in terms of a wider audience, because all
the studies I've seen don't show all that much confidence and satisfaction in the way BI delivers
the analytics or the insights that people are looking for. So I suppose it's a work in progress when
it comes to BI as well.
Bartik: Two points on that. There has been a lot of disappointment around BI projects in the
past. They've taken too long, for one. They've never really been finished, which of course, is a
problem. And for many of the business users who depend on the output of BI -- their reports,
their dashboard, their access to data -- it hasn’t answered the questions in the way that they may
want it to.
One of the things in front of us today is a way of thinking about it differently. Not only is there
so much data, and so much opportunity now to look at that data in different ways, but there is
also a requirement to look at it faster and to make decisions faster. So it really does break the old
way of thinking.
Slowness is unacceptable. Standard reports don't come close to addressing the opportunity in
front us, which is to ask a business question and answer it with the new way of thinking
supported by pulling together different datasets. That’s fundamentally different from the way we
used to do it.

7. People are trying to make decisions about moving the business forward, and they're being forced
to do it faster. Historical reporting just doesn't cut it. It’s not enough. They need something that’s
much closer to real time. It’s more important to think about open-ended questions, rather than
just say, "What revenue did I make last month, and what products made that up?" There are new
opportunities to go beyond that.
Gardner: I suppose it also requires more discipline in keeping your eye on the ends, rather than
getting lost in the means. That also is a segue to our next myth, which is, if I have the technology
to do big data, then I'm doing big data, and therefore I'm done.
Bartik: Just last week, I was meeting with a customer and they said, "Okay, we have our
Hadoop cluster set up and we've loaded about 10 terabytes of sample data into this Hadoop
cluster. So we've started our big data project."
When I hear something like that, I always ask, "What question are you trying to answer? Why
did you load that data in there? Why did you start with Hadoop? Why did you do all this?"
People are starting with the technology first too often. They're not starting with the questions and
the business problems first.
Not the endgame
You said as far as making sure that you keep your eye on the endgame, the endgame is not to
spin up a new technology, or to try a new tool. Hadoop has been one of those things where
people have started to use that and they think that they're off and running on a big-data project. It
can be part of it, but it isn't where you want to start, and it isn’t the endgame.
The endgame is solving the business problem that you're out there trying to address. It’s either
lowering costs inside the business, or it’s finding a new market, figuring out why this customer
set loves our products and why some other customer set doesn’t. Answering those questions is
the endgame, not starting a new technology initiative.
Gardner: When it comes to these technology issues, do you also find, Darin, that there is a lack
of creativity as to where the data and information resides or exists and thinking not so much
about being able to run it, but rather acquire it? Is there a dissonance between the data I have and
the data I need. How are people addressing that?
Bartik: There is and there isn’t. When we look at the data that we have, that’s oftentimes a great
way to start a project like this, because you can get going faster and it’s data that you understand.
But if you think that you have to get data from outside the organization, or you have to get new
datasets in order to answer the question that’s in front of us, then, again, you're going in with a
predisposition to a myth.

8. You can start with data that you already have. You just may not have been looking at the data that
you already have in the way that’s required to answer the question in front of you. Or you may
not have been looking at it all. You may have just been storing it, but not doing anything with it.
Storing data doesn’t help you answer questions. Analyzing it does. It seems kind of simple, but
so many people think that big data is a storage problem. I would argue it's not about the storage.
It’s like backup and recovery. Backing up data is not that important, until you need to recover it.
Recovery is really the game changing thing.
Gardner: It’s interesting that with these myths, people have tended, over the years, without
having the resources at hand, to shoot from the hip and second-guess. People who are good at
that and businesses that have been successful have depended on some luck and intuition. In order
to take advantage of big data, which should lead you to not having to make educated guesses, but
to have really clear evidence, you can apply the same principle. It's more how you get big data in
place, than how you would use the fruits of big data.
It seems like a cultural shift we have to make. Let’s not jump to conclusions. Let’s get the right
information and find out where the data takes us.
Bartik: You've hit on one of the biggest things that’s in front of us over the next three to five
years -- the cultural shift that the big data concept introduces.
We looked at traditional BI as more of an IT function, where we were reporting back to the
business. The business told us exactly what they wanted, and we tried to give that to them from
the IT side of the fence.
Data-driven organization
But being successful today is less about intuition and more about being a data-driven
organization, and, for that to happen, I can't stress this one enough, you need executives who are
ready to make decisions based on data, even if the data may be counterintuitive to what their gut
says and what their 25 years of experience have told them.
They're in a position of being an executive primarily because they have a lot of experience and
have had a lot of success. But many of our markets are changing so frequently and so fast,
because of new customer patterns and behaviors, because of new ways of customers interacting
with us via different devices. Just think of the different ways that the markets are changing. So
much of that historical precedence no longer really matters. You have to look at the data that’s in
front of us.
Because things are moving so much faster now, new markets are being penetrated and new
regions are open to us. We're so much more of a global economy. Things move so much faster
than they used to. If you're depending on gut feeling, you'll be wrong more often than you'll be

9. right. You do have to depend on as much of a data-driven decision as you can. The only way to
do that is to rethink the way you're using data.
Historical reports that tell you what happened 30 days ago don't help you make a decision about
what's coming out next month, given that your competition just introduced a new product today.
It's just a different mindset. So that cultural shift of being data-driven and going out and using
data to answer questions, rather than using data to support your gut feeling, is a very big shift
that many organizations are going to have to adapt to.
Executives who get that and drive it down into the organization, those are the executives and the
teams that will succeed with big data initiatives, as opposed to those that have to do it from the
bottom up.
Gardner: Listening to you Darin, I can tell one thing that isn’t a product of hype is just how
important this all is. Getting big data right, doing that cultural shift, recognizing trends based on
the evidence and in real-time as much as possible is really fundamental to how well many
businesses will succeed or not.
So it's not hype to say that big data is going to be a part of your future and it's important. Let's
move towards how you would start to implement or change or rethink things, so that you can not
fall prey to these myths, but actually take advantage of the technologies, the reduction in costs
for many of the infrastructures, and perhaps extend and exploit BI and big data problems.
Bartik: It's fair to say that big data is not just a trend; it's a reality. And it's an opportunity for
most organizations that want to take advantage of it. It will be a part of your future. It's either
going to be part of your future, or it's going to be a part of your competition’s future, and you're
going to be struggling as a result of not taking advantage of it.
The first step that I would recommend -- I've said it a few times already, but I don't think it can't
be said too often -- is pick a project that's going to address a business issue that you've been
unable to address in the past.
What are the questions that you need to ask and answer about your business that will really move
you forward?" Not just, "What data do we want to look at?" That's not the question.
What business issue?
The question is what business issue do we have in front of us that will take us forward the
fastest? Is it reducing costs? Is it penetrating a new regional market? Is it penetrating a new
vertical industry, or evolving into a new customer set?
These are the kind of questions we need to ask and the dialogue that we need to have. Then let's
take the next step, which is getting data and thinking about the team to analyze it and the
technologies to deploy. But that's the first step – deciding what we want to do as a business.

10. That sets you up for that cultural shift as well. If you start at the technology layer, if you start at
the level of let's deploy Hadoop or some type of new technology that may be relevant to the
equation, you're starting backwards. Many people do it, because it's easier to do that than it is to
start an executive conversation and to start down the path of changing some cultural behavior.
But it doesn’t necessarily set you up for success.
Gardner: It sounds as if you know you're going on a road trip and you get yourself a Ferrari, but
you haven't really decided where you're going to go yet, so you didn’t know that you actually
needed a Ferrari.
Bartik: Yeah. And it's not easy to get a tent inside a Ferrari. So you have to decide where you're
going first. It's a very good analogy.
Gardner: What are some of the other ways when it comes to the landscape out there? There are
vendors who claim to have it all, everything you need for this sort of thing. It strikes me that this
is more of an early period and that you would want to look at a best-of-breed approach or an
ecosystem approach.
So are there any words of wisdom in terms of how to think about the assets, tools, approaches,
platforms, what have you, or not to limit yourself in a certain way?
Bartik: There are countless vendors that are talking about big data and offering different
technology approaches today. Based on the type of questions that you're trying to answer,
whether it's more of an operational issue, a sales market issue, HR, or something else, there are
going to be different directions that you can go in, in terms of the approaches and the
technologies used.
I encourage the executives, both on the line-of-business side as well as the IT side, to go to some
of the events that are the "un-conferences," where we talk about the big-data approach and the
technologies. Go to the other events in your industry where they're talking about this and learn
what your peers are doing. Learn from some of the mistakes that they've been making or some of
the successes that they've been having.
There's a lot of success happening around this trend. Some people certainly are falling into the
pitfalls, but get smart by going to your peers and going to your industry influencer groups and
learning more about how to approach this.
Technical approaches
There are technical approaches that you can take. There are different ways of storing your data.
There are different ways of computing and processing your data. Then, of course, there are
different analytical approaches that get more to the open-ended investigation of data. There are
many tools and many products out there that can help you do that.

11. Dell has certainly gone down this road and is investing quite heavily in this area, with both
structured and unstructured data analysis, as well as the storage of that data. We're happy to
engage in those conversations as well, but there are a lot of resources out there that really help
companies understand and figure out how to attack this problem.
Gardner: In the past, with many of the technology shifts, we've seen a tension and a need for
decision around best-of-breed versus black box, or open versus entirely turnkey, and I'm sure
that's going to continue for some time.
But one of the easier ways or best ways to understand how to approach some of those issues is
through some examples. Do we have any use cases or examples that you're aware of, of actual
organizations that have had some of these problems? What have they put in place, and what has
worked for them?
Bartik: I'll give you a couple of examples from two very different types of organizations, neither
of which are huge organizations. The first one is a retail organization, Guess Jeans. The business
issue they were tackling was, “How do we get more sales in our retail stores? How do we get
each individual that's coming into our store to purchase more?”
We sat down and started thinking about the problem. We asked what data would we need to
understand what’s happening? We needed data that helps us understand the buyer’s behavior
once they come into the store. We don't need data about what they are doing outside the store
necessarily, so let's look specifically at behaviors that take place once they get into the store.
We helped them capture and analyze video monitoring information. Basically it followed each of
the people in the store and geospatial locations inside the store, based on their behavior. We
tracked that data and then we compared against questions like did they buy, what did they buy,
and how much did they buy. We were able to help them determine that if you get the customer
into a dressing room, you're going to be about 50 percent more likely to close transactions with
them.
So rather than trying to give incentives to come into the store or give discounts once they get into
the store, they moved towards helping the store clerks, the people who ran the store and
interacted with the customers, focus on getting those customers into a dressing room. That itself
is a very different answer than what they might have thought of at first. It seems easy after you
think about it, but it really did make a significant business impact for them in rather short order.
Now, they're also thinking about other business challenges that they have and other ways of
analyzing data and other datasets, based on different business challenges, but that’s one example.
Another example is on the higher education side. In universities, one of the biggest challenges is
having students drop out or reduce their class load. The fewer classes they take, or if they
dropout entirely, it obviously goes right to the top and bottom line of the organization, because it
reduces tuition, as well as the other extraneous expenses that students incur at the university.

12. Finding indicators
The University of Kentucky went on an effort to reduce students dropping out of classes or
dropping entirely out of school. They looked at a series of datasets, such as demographic data,
class data, the grades that they were receiving, what their attendance rates were, and so forth.
They analyzed many different data points to determine the indicators of a future drop out.
Now, just raising the student retention rate by one percent would in turn mean about $1 million
of top-line revenue to the university. So this was pretty important. And in the end, they were able
to narrow it down to a couple of variables that strongly indicated which students were at risk,
such that they could then proactively intervene with those students to help them succeed.
The key is that they started with a very specific problem. They started it from the university's
core mission: to make sure that the students stayed in school and got the best education, and
that's what they are trying to do with their initiative. It turned out well for them.
These were very different organizations or business types, in two very different verticals, and
again, neither are huge organizations that have seas of data. But what they did are much more
manageable and much more tangible examples many of us can kind of apply to our own
businesses.
Gardner: Those really demonstrate how asking the right questions is so important.
Darin, we're almost out of time, but I did want to see if we could develop a little bit more insight
into the Dell Software road map. Are there some directions that you can discuss that would
indicate how organizations can better approach these problems and develop some of these
innovative insights in business?
Bartik: A couple of things. We've been in the business of data management, database
management, and managing the infrastructure around data for well over a decade. Dell has
assembled a group of companies, as well as a lot of organic development, based on their
expertise in the data center for years. What we have today is a set of capabilities that help
customers take more of a data-type agnostic view and a vendor agnostic view to the way they're
approaching data and managing data.
You may have 15 tools around BI. You may have tools to look at your Oracle data, maybe new
sets of unstructured data, and so forth. And you have different infrastructure environments set up
to house that data and manage it. But the problem is that it's not helping you bring the data
together and cross boundaries across data types and vendor toolset types, and that's the challenge
that we're trying to help address.
We've introduced tools to help bring data together from any database, regardless of where it may
be sitting, whether it's a data warehouse, a traditional database, a new type of database such as
Hadoop, or some other type of unstructured data store.

13. We want to bring that data together and then analyze it. Whether you're looking at more of a
traditional structured-data approach and you're exploring data and visualizing datasets that many
people may be working with, or doing some of the more advanced things around unstructured
data and looking for patterns, we’re focused on giving you the ability to pull data from
anywhere.
Using new technologies
We're investing very heavily, Dana, into the Hadoop framework to help customers do a couple
of key things. One is helping the people that own data today, the database administrators, data
analysts, the people that are the stewards of data inside of IT, advance their skills to start using
some of these new technologies, including Hadoop.
It's been something that we have done for a very long time, making your C players B players,
and your B players A players. We want to continue to do that, leverage their existing experience
with structured data, and move them over into the unstructured data world as well.
The other thing is that we're helping customers manage data in a much more pragmatic way. So
if they are starting to use data that is in the cloud, via Salesforce.com or Taleo, but they also have
data on-prem sitting in traditional data stores, how do we integrate that data without completely
changing their infrastructure requirements? With capabilities that Dell Software has today, we
can help integrate data no matter where it sits and then analyze it based on that business problem.
We help customers approach it more from a pragmatic view, where you're taking a stepwise
approach. We don't expect customers to pull out their entire BI and data-management
infrastructure and rewrite it from scratch on day one. That's not practical. It's not something we
would recommend. Take a stepwise approach. Maybe change the way you're integrating data.
Change the way you're storing data. Change, in some perspective, the way you're analyzing data
between IT and the business, and have those teams collaborate.
But you don't have to do it all at one time. Take that stepwise approach. Tackle it from the
business problems that you're trying to address, not just the new technologies we have in front of
us.
There's much more to come from Dell in the information management space. It will be very
interesting for us and for our customers to tackle this problem together. We're excited to make it
happen.
Gardner: Well, great. I'm afraid we'll have to leave it there. We've been listening to a sponsored
BriefingsDirect podcast discussion on debunking some major myths around big data use and
value. We've seen how big data is not necessarily limited by scale and that the issues around it
don't always have to supersede the end for your business goals.

14. We've also learned more about levels of automation and how Dell is going to be approaching the
market. So I appreciate that. With that, we'll have to end it and thank our guest.
We've been here with Darin Bartik. He's the Executive Director of Products in the Information
Management Group at Dell Software. Thanks so much, Darin.
Bartik: Thank you, Dana, I appreciate it.
Gardner: This is Dana Gardner, Principal Analyst at Interarbor Solutions. Thanks also to our
audience for joining and listening, and don't forget to come back next time.
Listen to the podcast. Find it on iTunes. Sponsor: Dell
Transcript of a BriefingsDirect podcast on current misconceptions about big data and how
organizations should approach a big-data project. Copyright Interarbor Solutions, LLC,
2005-2013. All rights reserved.
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