Want a Data-Driven Culture? Start Sorting Out the BI and Big Data Myths Now
Want a Data-Driven Culture? Start Sorting Out the BI andBig Data Myths NowTranscript of a BrieﬁngsDirect podcast on current misconceptions about big data and howorganizations should approach a big-data project.Listen to the podcast. Find it on iTunes. Sponsor: DellDana Gardner: Hi, this is Dana Gardner, Principal Analyst at Interarbor Solutions, and yourelistening to BrieﬁngsDirect.Today, we present a sponsored podcast discussion on debunking some majormyths around big data. It used to be that data was the refuse of businessapplications, a necessary cleanup chore for audit and compliance sake. Butnow, as analytics grow in importance for better running businesses and inknowing 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 tomanage and exploit that sea of data has changed rapidly too. But that doesnt 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 were here to pose some questions, many of them the stuff of myth, and then ﬁnd betteranswers to why making data and big data the progeny of innovative insight is critical for morecompanies.To help identify and debunk the myths around big data so that you can enjoy the value of thoseanalytics better, please join me in welcoming our guest. Were here with Darin Bartik. Hes theExecutive 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 aboutthat is it’s a hot topic and it’s of more interest to more people nowadays, but we seem to haveveered away from the practical and maybe even the impactful. Are people losing sight of thebusiness value by getting lost in speeds and feeds and technical jargon? Is there some sort of adisconnect between the providers and consumers of big data?Bartik: Im sure were going to get into a couple of different areas today, but you hit the nail onthe head with the ﬁrst question. We are experiencing a disconnect between the technical side of
big data and the business value of big data, and that’s happening because we’re digging toodeeply into the technology.With a term like big data, or any one of the trends that the informationtechnology industry talks about so much, we tend to think about the technicalside of it. But with analytics, with the whole conversation around big data,what weve been stressing with many of our customers is that it starts with abusiness discussion. It starts with the questions that youre trying to answerabout the business; not the technology, the tools, or the architecture of solvingthose problems. It has to start with the business discussion.That’s a pretty big ﬂip. The traditional approach to business intelligence (BI) and reporting hasbeen 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 adisconnect between the business goals and the IT methods.So youre right. There has been a disconnect, and that’s what Ive been trying to talk a lot aboutwith customers -- how to refocus on the business issues you need to think about, especially in themid-market, where you maybe don’t have as many resources at hand. It can be pretty confusing.Part of the hype cycleThe other thing you asked is, “Are vendors confusing people?" Without disparaging thevendors like us, or anyone else, that’s part of the problem of any hype cycle. Many peoplejumped on the bandwagon of big data. Just like everyone was talking cloud.Everyone was talking virtualization, bring your own device (BYOD), andso forth.Everyone jumps on these big trends. So its very confusing for customers,because there are many different ways to come at the problem. This is why Ikeep bringing people back to staying focused on what the real opportunityis. It’s a business opportunity, not a technical problem or a technicalchallenge that we start with.Gardner: Right. We don’t want to lose the track of the ends because the means seem to be sodaunting. We want to keep our focus on the ends and then ﬁnd the means. Before we go into ourmyths, tell me a little bit, Darin, about your background and how you came to be at Dell.Bartik: Ive been a part of Dell Software since the acquisition of Quest Software. I was a part ofthat organization for close to 10 years. Ive been in technology coming up on 20 years now. Ispent a lot of time in enterprise resource planning (ERP), supply chain, and monitoring,performance management, and infrastructure management, especially on the Microsoft side ofthe world.
Most recently, as part of Quest, I was running the database management area -- a business verywell-known for its products around Oracle, especially Toad, as well as our SQL Servermanagement capabilities. We leveraged that expertise when we started to evolve into BI andanalytics.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 inthe ever-expanding world of information management. Were part of the Dell Software Group,which is a big piece of the strategy for Dell over all, and Im excited to be here.Gardner: Great. Even the name "big data" stirs up myths right from the get-go, with "big" beinga very relative term. Should we only be concerned about this when we have more data than wecan manage? What is the relative position of big data and what are some of the myths around thesize issue?Bartik: That’s the perfect one to start with. The ﬁrst word in the deﬁnition is actually part of theproblem. "Big." What does big mean? Is there a certain threshold of petabytes that you have toget to? Or, if youre 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, its really a trend that has happened as a resultof 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 contributingto the proliferation of data.Its added a lot more data to our universe, but the real opportunity is to look for small elements ofsmall datasets and look for combinations and patterns within the data that help answer thosebusiness questions that I was referencing earlier.Its not necessarily a scale issue. What is a scale issue is when you get into some of the morecomplicated analytical processes and you need a certain data volume to make it statisticallyrelevant. 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 challengeThat may not be huge data volumes. You mentioned mid-market earlier. When we think aboutsome organizations moving from gigabytes to terabytes, or doubling data volumes, that’s a bigdata challenge in and of itself.Analyzing big data wont necessarily contribute to your solving your business problems if yourenot starting with the right questions. If youre just trying to store more data, that’s not really theproblem that we have at hand. That’s something that we can all do quite well with current storagearchitectures and the evolving landscape of hardware that we have.
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 thehouse. As you point out, some statistically relevant data might be enough. If you can extrapolateand you have enough to do that, ﬁne, but there might be other areas where you actually want toget every little bit of possible data or information relevant, because you dont know what yourelooking for. They are the unknown unknowns. Perhaps theres some mythology about all data. Itseems 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 youre trying to pull out operational expenseand 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, manufacturingline, and operational data. Thats a very different type of data and a very different type ofapproach than looking at it in terms of sales and marketing.If youre a retailer looking for a new set of customers or new markets to enter in terms ofgeographies, youre going to want to look at maybe census data and buying-behavior data of thedifferent geographies. Maybe you want datasets that are outside your organization entirely. Youmay not have the data in your hands today. You may have to pull it in from outside resources. Sotheres a lot of variability and prioritization that all starts with that business issue that youretrying to address.Gardner: Perhaps its better for the business to identify the important data, rather than the ITpeople saying it’s too big or that big means we need to do something different. It seems like abusiness 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 tabletogether to tackle this challenge, the better. And it does start with the executive management inthe organization trying to think about things from that business perspective, rather than startingwith the IT infrastructure management team. Gardner: What’s our second myth?Bartik: Id think about the idea of people and the skills needed to address this concept of bigdata. There is the term "data scientist" that has been thrown out all over the place lately. There’s alot of discussion about how you need a data scientist to tackle big data. But “big data” isntnecessarily the way you should think about what you’re trying to accomplish. Instead, thinkabout things in terms of being more data driven, and in terms of getting the data you need toaddress the business challenges that you have. That’s not always going to require the skills of adata scientist.
Data scientists rareIsuspect that a lot of organizations would be happy to hear something like that, because datascientists are very rare today, and theyre very expensive, because they are rare. Only certaingeographies and certain industries have groomed the true data scientist. Thats a unique blendbetween a data engineer and someone like an applied scientist, who can think quite differentlythan 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 outand hire a data scientist. There are other ways to tackle it. That’s where youre going to combinepeople who can do the programming around your information, around the data managementprinciples, and the people who can ask and answer the open-minded business questions. Itdoesn’t all have to be encapsulated into that one magical person that’s known now as the datascientist.Gardner: So rather than thinking we need to push the data and analytics and the ability tovisualize and access this through a small keyhole, which would be those scientists, the PhDs, thewhite lab coats, perhaps there are better ways now to make those visualizations and allow peopleto craft their own questions against the datasets. That opens the door to more types of peoplebeing 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 intovery sophisticated algorithms and computations for which a data scientist may be the one to dothat heavy lifting. But for many organizations and customers that we talk to everyday, it’ssomething where theyre taking on their ﬁrst project and they are just starting to ﬁgure out how toaddress this opportunity.For that, you can use a lot of the people that you have inside your organization, as wellpotentially consultants that can just help you break through some of the old barriers, such asthinking 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 ofprogramming and some open-minded thinking, done with a team-oriented approach, rather thanthat single keyhole person, is more than enough to accomplish your objectives.Gardner: It seems also that youre identifying confusion on the part of some to equate big datawith 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 deﬁnethem and separate them?Bartik: Thats a common myth. If you think about BI in its traditional, generic sense, it’s aboutgaining more intelligence about the business, which is still the primary beneﬁt of the opportunity
this trend of big data presents to us. Today, I think theyre distinct, but over time, they will cometogether and become synonymous.I equate it back to one of the more recent trends that came right before big data, cloud. In thebeginning, most people thought cloud was the public-cloud concept. What’s turned out to be trueis 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.Theyve kind of come together. So while cloud and traditional data centers are the newinfrastructure, it’s all still infrastructure. The same is true for big data and BI, where BI, in thegeneral sense of how can we gain intelligence and make smarter decisions about our business,will include the concept of big data.Better decisionsSo while well 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 beworking back to that general purpose of making better decisions.One of the reasons theyre still different today is because we’re still breaking some of thetraditional mythology and beliefs around BI -- that BI is all about standard reports and standarddashboards, driven by IT. But over time, as people think about business questions ﬁrst, instead ofthinking 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 allthe studies Ive seen dont show all that much conﬁdence and satisfaction in the way BI deliversthe analytics or the insights that people are looking for. So I suppose its a work in progress whenit comes to BI as well.Bartik: Two points on that. There has been a lot of disappointment around BI projects in thepast. Theyve taken too long, for one. Theyve never really been ﬁnished, which of course, is aproblem. 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 maywant it to.One of the things in front of us today is a way of thinking about it differently. Not only is thereso much data, and so much opportunity now to look at that data in different ways, but there isalso a requirement to look at it faster and to make decisions faster. So it really does break the oldway of thinking.Slowness is unacceptable. Standard reports dont come close to addressing the opportunity infront us, which is to ask a business question and answer it with the new way of thinkingsupported by pulling together different datasets. That’s fundamentally different from the way weused to do it.
People are trying to make decisions about moving the business forward, and theyre being forcedto do it faster. Historical reporting just doesnt cut it. It’s not enough. They need something that’smuch closer to real time. It’s more important to think about open-ended questions, rather thanjust say, "What revenue did I make last month, and what products made that up?" There are newopportunities to go beyond that.Gardner: I suppose it also requires more discipline in keeping your eye on the ends, rather thangetting lost in the means. That also is a segue to our next myth, which is, if I have the technologyto do big data, then Im doing big data, and therefore Im done.Bartik: Just last week, I was meeting with a customer and they said, "Okay, we have ourHadoop cluster set up and weve loaded about 10 terabytes of sample data into this Hadoopcluster. So weve started our big data project."When I hear something like that, I always ask, "What question are you trying to answer? Whydid 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. Theyre not starting with the questions andthe business problems ﬁrst.Not the endgameYou said as far as making sure that you keep your eye on the endgame, the endgame is not tospin up a new technology, or to try a new tool. Hadoop has been one of those things wherepeople have started to use that and they think that theyre off and running on a big-data project. Itcan be part of it, but it isnt where you want to start, and it isn’t the endgame.The endgame is solving the business problem that youre out there trying to address. It’s eitherlowering costs inside the business, or it’s ﬁnding a new market, ﬁguring out why this customerset loves our products and why some other customer set doesn’t. Answering those questions isthe endgame, not starting a new technology initiative.Gardner: When it comes to these technology issues, do you also ﬁnd, Darin, that there is a lackof creativity as to where the data and information resides or exists and thinking not so muchabout being able to run it, but rather acquire it? Is there a dissonance between the data I have andthe 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 greatway 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 newdatasets in order to answer the question that’s in front of us, then, again, youre going in with apredisposition to a myth.
You can start with data that you already have. You just may not have been looking at the data thatyou already have in the way that’s required to answer the question in front of you. Or you maynot 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, butso many people think that big data is a storage problem. I would argue its 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, withouthaving the resources at hand, to shoot from the hip and second-guess. People who are good atthat and businesses that have been successful have depended on some luck and intuition. In orderto take advantage of big data, which should lead you to not having to make educated guesses, butto have really clear evidence, you can apply the same principle. Its more how you get big data inplace, 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 rightinformation and ﬁnd out where the data takes us.Bartik: Youve hit on one of the biggest things that’s in front of us over the next three to ﬁveyears -- 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 thebusiness. The business told us exactly what they wanted, and we tried to give that to them fromthe IT side of the fence.Data-driven organizationBut being successful today is less about intuition and more about being a data-drivenorganization, and, for that to happen, I cant stress this one enough, you need executives who areready to make decisions based on data, even if the data may be counterintuitive to what their gutsays and what their 25 years of experience have told them.Theyre in a position of being an executive primarily because they have a lot of experience andhave 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 interactingwith us via different devices. Just think of the different ways that the markets are changing. Somuch of that historical precedence no longer really matters. You have to look at the data that’s infront of us.Because things are moving so much faster now, new markets are being penetrated and newregions are open to us. Were so much more of a global economy. Things move so much fasterthan they used to. If youre depending on gut feeling, youll be wrong more often than youll be
right. You do have to depend on as much of a data-driven decision as you can. The only way todo that is to rethink the way youre using data.Historical reports that tell you what happened 30 days ago dont help you make a decision aboutwhats coming out next month, given that your competition just introduced a new product today.Its just a different mindset. So that cultural shift of being data-driven and going out and usingdata to answer questions, rather than using data to support your gut feeling, is a very big shiftthat 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 theteams that will succeed with big data initiatives, as opposed to those that have to do it from thebottom up.Gardner: Listening to you Darin, I can tell one thing that isn’t a product of hype is just howimportant this all is. Getting big data right, doing that cultural shift, recognizing trends based onthe evidence and in real-time as much as possible is really fundamental to how well manybusinesses will succeed or not.So its not hype to say that big data is going to be a part of your future and its important. Letsmove towards how you would start to implement or change or rethink things, so that you can notfall prey to these myths, but actually take advantage of the technologies, the reduction in costsfor many of the infrastructures, and perhaps extend and exploit BI and big data problems.Bartik: Its fair to say that big data is not just a trend; its a reality. And its an opportunity formost organizations that want to take advantage of it. It will be a part of your future. Its eithergoing to be part of your future, or its going to be a part of your competition’s future, and youregoing to be struggling as a result of not taking advantage of it.The ﬁrst step that I would recommend -- Ive said it a few times already, but I dont think it cantbe said too often -- is pick a project thats going to address a business issue that youve beenunable to address in the past.What are the questions that you need to ask and answer about your business that will really moveyou forward?" Not just, "What data do we want to look at?" Thats not the question.What business issue?The question is what business issue do we have in front of us that will take us forward thefastest? Is it reducing costs? Is it penetrating a new regional market? Is it penetrating a newvertical 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 letstake the next step, which is getting data and thinking about the team to analyze it and thetechnologies to deploy. But thats the ﬁrst step – deciding what we want to do as a business.
That sets you up for that cultural shift as well. If you start at the technology layer, if you start atthe level of lets deploy Hadoop or some type of new technology that may be relevant to theequation, youre starting backwards. Many people do it, because its easier to do that than it is tostart 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 youre going on a road trip and you get yourself a Ferrari, butyou havent really decided where youre going to go yet, so you didn’t know that you actuallyneeded a Ferrari.Bartik: Yeah. And its not easy to get a tent inside a Ferrari. So you have to decide where youregoing ﬁrst. Its a very good analogy.Gardner: What are some of the other ways when it comes to the landscape out there? There arevendors who claim to have it all, everything you need for this sort of thing. It strikes me that thisis more of an early period and that you would want to look at a best-of-breed approach or anecosystem 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 differenttechnology approaches today. Based on the type of questions that youre trying to answer,whether its more of an operational issue, a sales market issue, HR, or something else, there aregoing to be different directions that you can go in, in terms of the approaches and thetechnologies used.I encourage the executives, both on the line-of-business side as well as the IT side, to go to someof the events that are the "un-conferences," where we talk about the big-data approach and thetechnologies. Go to the other events in your industry where theyre talking about this and learnwhat your peers are doing. Learn from some of the mistakes that theyve been making or some ofthe successes that theyve been having.Theres a lot of success happening around this trend. Some people certainly are falling into thepitfalls, but get smart by going to your peers and going to your industry inﬂuencer groups andlearning more about how to approach this.Technical approachesThere 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 aredifferent analytical approaches that get more to the open-ended investigation of data. There aremany tools and many products out there that can help you do that.
Dell has certainly gone down this road and is investing quite heavily in this area, with bothstructured and unstructured data analysis, as well as the storage of that data. Were happy toengage in those conversations as well, but there are a lot of resources out there that really helpcompanies understand and ﬁgure out how to attack this problem.Gardner: In the past, with many of the technology shifts, weve seen a tension and a need fordecision around best-of-breed versus black box, or open versus entirely turnkey, and Im surethats going to continue for some time.But one of the easier ways or best ways to understand how to approach some of those issues isthrough some examples. Do we have any use cases or examples that youre aware of, of actualorganizations that have had some of these problems? What have they put in place, and what hasworked for them?Bartik: Ill give you a couple of examples from two very different types of organizations, neitherof which are huge organizations. The ﬁrst one is a retail organization, Guess Jeans. The businessissue they were tackling was, “How do we get more sales in our retail stores? How do we geteach individual thats coming into our store to purchase more?”We sat down and started thinking about the problem. We asked what data would we need tounderstand what’s happening? We needed data that helps us understand the buyer’s behavioronce they come into the store. We dont need data about what they are doing outside the storenecessarily, so lets 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 ofthe people in the store and geospatial locations inside the store, based on their behavior. Wetracked 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 customerinto a dressing room, youre going to be about 50 percent more likely to close transactions withthem.So rather than trying to give incentives to come into the store or give discounts once they get intothe store, they moved towards helping the store clerks, the people who ran the store andinteracted with the customers, focus on getting those customers into a dressing room. That itselfis a very different answer than what they might have thought of at ﬁrst. It seems easy after youthink about it, but it really did make a signiﬁcant business impact for them in rather short order.Now, theyre also thinking about other business challenges that they have and other ways ofanalyzing 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 ishaving students drop out or reduce their class load. The fewer classes they take, or if theydropout entirely, it obviously goes right to the top and bottom line of the organization, because itreduces tuition, as well as the other extraneous expenses that students incur at the university.
Finding indicatorsThe University of Kentucky went on an effort to reduce students dropping out of classes ordropping 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 millionof top-line revenue to the university. So this was pretty important. And in the end, they were ableto 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 universityscore mission: to make sure that the students stayed in school and got the best education, andthats 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, andagain, neither are huge organizations that have seas of data. But what they did are much moremanageable and much more tangible examples many of us can kind of apply to our ownbusinesses.Gardner: Those really demonstrate how asking the right questions is so important.Darin, were almost out of time, but I did want to see if we could develop a little bit more insightinto the Dell Software road map. Are there some directions that you can discuss that wouldindicate how organizations can better approach these problems and develop some of theseinnovative insights in business?Bartik: A couple of things. Weve been in the business of data management, databasemanagement, and managing the infrastructure around data for well over a decade. Dell hasassembled a group of companies, as well as a lot of organic development, based on theirexpertise in the data center for years. What we have today is a set of capabilities that helpcustomers take more of a data-type agnostic view and a vendor agnostic view to the way theyreapproaching data and managing data.You may have 15 tools around BI. You may have tools to look at your Oracle data, maybe newsets of unstructured data, and so forth. And you have different infrastructure environments set upto house that data and manage it. But the problem is that its not helping you bring the datatogether and cross boundaries across data types and vendor toolset types, and thats the challengethat were trying to help address.Weve introduced tools to help bring data together from any database, regardless of where it maybe sitting, whether its a data warehouse, a traditional database, a new type of database such asHadoop, or some other type of unstructured data store.
We want to bring that data together and then analyze it. Whether youre looking at more of atraditional structured-data approach and youre exploring data and visualizing datasets that manypeople may be working with, or doing some of the more advanced things around unstructureddata and looking for patterns, we’re focused on giving you the ability to pull data fromanywhere.Using new technologiesWere investing very heavily, Dana, into the Hadoop framework to help customers do a coupleof key things. One is helping the people that own data today, the database administrators, dataanalysts, the people that are the stewards of data inside of IT, advance their skills to start usingsome of these new technologies, including Hadoop.Its 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 experiencewith structured data, and move them over into the unstructured data world as well.The other thing is that were helping customers manage data in a much more pragmatic way. Soif they are starting to use data that is in the cloud, via Salesforce.com or Taleo, but they also havedata on-prem sitting in traditional data stores, how do we integrate that data without completelychanging their infrastructure requirements? With capabilities that Dell Software has today, wecan 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 youre taking a stepwiseapproach. We dont expect customers to pull out their entire BI and data-managementinfrastructure and rewrite it from scratch on day one. Thats not practical. Its not something wewould recommend. Take a stepwise approach. Maybe change the way youre integrating data.Change the way youre storing data. Change, in some perspective, the way youre analyzing databetween IT and the business, and have those teams collaborate.But you dont have to do it all at one time. Take that stepwise approach. Tackle it from thebusiness problems that youre trying to address, not just the new technologies we have in front ofus.Theres much more to come from Dell in the information management space. It will be veryinteresting for us and for our customers to tackle this problem together. Were excited to make ithappen.Gardner: Well, great. Im afraid well have to leave it there. Weve been listening to a sponsoredBrieﬁngsDirect podcast discussion on debunking some major myths around big data use andvalue. Weve seen how big data is not necessarily limited by scale and that the issues around itdont always have to supersede the end for your business goals.
Weve also learned more about levels of automation and how Dell is going to be approaching themarket. So I appreciate that. With that, well have to end it and thank our guest.Weve been here with Darin Bartik. Hes the Executive Director of Products in the InformationManagement 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 ouraudience for joining and listening, and dont forget to come back next time.Listen to the podcast. Find it on iTunes. Sponsor: DellTranscript of a BrieﬁngsDirect podcast on current misconceptions about big data and howorganizations should approach a big-data project. Copyright Interarbor Solutions, LLC,2005-2013. All rights reserved.You may also be interested in:• For Dells Quest Software, BYOD Puts Users First and with ITs Blessing• New Levels of Automation and Precision Needed to Optimize Backup and Recovery inVirtualized Environments• Data explosion and big data demand new strategies for data management, backup andrecovery, say experts• Ocean Observatories Initiative: Cloud and Big Data come together to give scientistsunprecedented access to essential climate insights• Case Study: Strategic Approach to Disaster Recovery and Data Lifecycle ManagementPays Off for Australias SAI Global