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


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Transcript of a BriefingsDirect podcast on how Dell Software is working with companies to manage internal and external data in all its forms.

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

  1. 1. Agnostic Tool Chain Key to Fixing the Broken State of Dataand Information ManagementTranscript of a BriefingsDirect podcast on how Dell Software is working with companies tomanage internal and external data in all its forms.Listen to the podcast. Find it on iTunes. Sponsor: Dell SoftwareDana Gardner: Hi, this is Dana Gardner, Principal Analyst at Interarbor Solutions, and yourelistening 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. Well 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 taskof solving data complexity by embracing an agnostic, end-to-end tool chain approach to overalldata and information management.Here now to share his insights with us on where the information management market has beenand where its going, were joined by Matt Wolken. He is the Executive Director and GeneralManager for Information Management at Dell Software. Welcome, Matt.Matt Wolken: Dana, thanks for having me. I appreciate it.Gardner: From your perspective, what are the biggest challenges that businesses need to solvenow, when it comes to data and information management? What are the big hurdles that theyrefacing?Wolken: Its an interesting question. When we look at customers today, were noticing how theirenvironments 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 aroundthe company, either in a database on the backside of an application, the customer relationshipmanagement (CRM) application, the enterprise resource planning (ERP) application, or in datamarts around the company. The challenge was how to bring all this together to create a singlecohesive view of the company?
  2. 2. That was yesterdays 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 datawarehouse where all of the data was for a complete answer about your company.What were 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 isnt on premise any more. Itsnot even owned by the company. Its at your software-as-a-service (SaaS) provider for CRM,your SaaS provider for ERP, or your travel or HR provider. So data again becomes siloed, notonly by vendor and data type, but also by location. This is the complexity of today, as we noticeit.Cohesive viewAll of this data is spread about, and the challenge becomes how do you understand andotherwise consume that data or create a cohesive view of your company? Then there is still theadditional social data in the form of Twitter or Facebook information that you wouldnt have hadin prior years. And its that environment, and the complexity that comes with it, that we reallywould like to help customers solve.Gardner: When it comes to this so-called data dichotomy, is it oversimplified to say its internaland external, or is there perhaps a better way to categorize these larger sets that organizationsneed to deal with?Wolken: Theres 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? Whats the business benefit for me examining this data? Is there a new segment I can codify and therefore market to? Is there a campaign thats currentlyrunning that is not getting a good response rate, and if so, do I want to switch to anothercampaign or otherwise improve it midstream to drive more real value in terms of revenue to thecompany?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
  3. 3. focused. How do I get sanctioned data off of approved systems to understand the officialcompany point of view in terms of operations?That second goal is not a bad goal. Thats still a goal thats needed, and IT is still required tocreate that sanctioned data, that master data, and the approved, official sources of data. But thereis this other piece of data, this other outcome thats 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? Thats moreoperationally revenue-oriented, whereas the internal operations are around cost orientation andoperations.So where you get executive dashboards for internal consumption off of BI or intelligence for thebusiness, the business units themselves are about visualization, exploration, and understandingand driving new insights.Its a change in both focus and direction. It sometimes ends up in a conflict between the groups,but it doesnt really have to be that way. At least, we dont think it does. Thats something that wetry to help people through. How do you get the sanctioned data you need, but also bring in thisthird-party data and unstructured data and add nuance to what you are seeing about yourcompany.Gardner: Just as 10 or 15 years ago the problem to solve was the silos of data within theorganization, is there any way in traditional technology offerings that allows this dichotomy to bejoined now, or do we need a different way in which to create insights, using both that internaland external type of information?Wolken: There are certainly ways to get to anything. But if youre still amending program afterprogram or technology after technology, you end up with something less than the best path, andthere might be new and better ways of doing things.Agnostic tool chainThere are lots of ways to take a data warehouse forward in todays environment, manipulateother forms of data so it can enter a data warehouse or relational data warehouse, and/or go theother way and put everything into an unstructured environment, but theres also another way toapproach 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 hidecomplexity and all of the issues underneath the tool itself. The tool has intelligence tocomprehend 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, somethingthat can handle text, a solution that handles semi-structured or structured, or even a highperformance analytical engine for that matter, what if the tool chain abstracts much of that
  4. 4. 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.Thats the strategy we’re pursuing at Dell. We’re defining a set of tools, not the underlyingtechnologies or proliferation of technologies, but the tools themselves, so that the day-to-dayoperations are hidden from the complexity of those underlying sources of vendor, data type, andlocation.Thats how we really came at it -- from a tool-chain perspective, as opposed to deployingadditional technologies. We’re looking to enable customers to leverage those technologies for asmoother, more efficient, and more effective operation.Gardner: Am I right then in understanding that this is at more of a meta level, above theunderlying technologies, but that, in a sense, makes the whole greater than the sum of the parts ofthose technologies?Wolken: That’s a fair way of looking at it. Lets just take data integration as a point. I cansometimes go after certain siloed data integration products. I can go after a data product that goesafter cloud resources. I can get a data product that only goes after relational. I can get anotherdata product to extract or load into Hive or Hadoop. But what if I had one that could do all ofthat? 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 seeand describe everything below it, or can I actually manipulate it as well? So in that sense, its areal tool to actually manipulate and cause the effective change in the environment.Gardner: Id like to go into more of the challenges, but before we do that, what are the stakeshere? What do you get if you do this right? If you can, in fact, manage across various technologytypes and formats, across relational and unstructured data, internal and external data sources andproviders.Are we talking iterative change, a step change, or is it something that is a bit larger and that wemight have some other examples of companies when they do this well can really demonstratesomething perhaps quite unique in terms of a new level of accomplishment?Institutional knowledgeWolken: There are a couple of ways we think about it, one of which is institutionalknowledge. Previously, if you brought in a new tool into your environment to examine a newdatabase type, you would probably hire a person from the outside, because you needed to findthat skill set already in the market in order to make you productive on day one.
  5. 5. Instead of applying somebody who knows the organization, the data, the functions of thebusiness, you would probably hire the new person from the outside. Thats generally retoolingyour organization.Or, if you switch vendors, that causes a shift as well. One primary vendor stack is probably aknowledge and domain of one of your employees, and if you switch to another vendor stack orrequire another vendor stack in your environment, youre probably going to have to retool yetagain and find new resources. So thats one aspect of human knowledge and intelligence aboutthe business.There is a value to sharing. Its a lot harder to share across vendor environments and dataenvironments if the tools cant bridge them. In that case, you have to have third-party ways tobridge those gaps between the tools. If you have sharing that occurs natively in the tool, then youdont have to cross that bridge, you dont have the delay, and you dont have the complexity to getthere.So there is a methodology within the way you run the environment and the way employeescollaborate that is also accelerated. We also think that training is something that can benefit fromthis agnostic approach.But also, generically, if youre using the same tools, then things like master data management(MDM) challenges become more comprehensive, if the tool chain understands where that MDMis coming from, and so on.You also codify how and where resources are shared. So if you have a person who has toprovision data for an analyst, and they are using one tool to reach to relational data, another toreach into another type of data, or a third-party tool to reach into properties and SaaSenvironments, then you have an ineffective process.Youre reaching across domains and youre not as effective as you would be if you could do thatall with one tool chain.So those are some of the high-level ideas. Thats why we think theres value there. If you go backto what would have existed maybe 10 or 15 years ago, you had one set of staff who used one setof tools to go back against all relational data. It was a construct that worked well then. We justthink it needs to be updated to account for the variance within the nuances that have come to thefore 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 drivenand information driven, rather than a hunch, intuition, or gut instinct. Also, theres an ability tofind 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 dothey outweigh the cost more than previous investments in data would have?Investment cycles
  6. 6. Wolken: It all depends on how you go about it. There are lots of stories about people who goon these long investment cycles into some massive information management strategy changewithout feeling like they got anything out of it, or at least were productive or paid back the fee.Theres a different strategy that we think can be more effective for organizations, which is topursue smaller, bite-size chunks of objective action that you know will deliver some concretebenefit to the company. So rather than doing large schemes, start with smaller projects andpursue them one at a time incrementally -- projects that last a week and then you have 52projects 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 toincrease competitiveness. For starters, can you see nuances in the data? Is there a tool that givesyou the capability to see something you couldnt see before? So thats more of an analytical ordiscovery capability.Theres also a capability to just manage a given data type. If I can see the data, I can takeadvantage 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 durationof observation to action. In this case, Ill talk about social sentiment for a moment. If you cancreate systems that can listen to how your brand is being talked about, how your product is beingtalked about in the environment of social commentary, then the feedback that youre getting canoccur in real time, as the comments are being posted.Now, you might think youll get that anyway. I would have gotten a letter from a customer twoweeks from now in the postal system that provided me that same feedback. That’s true, butsometimes that two weeks can be a real benefit.Imagine a marketing campaign thats currently running in the East, with a companion program inthe West thats slightly different. Lets say its a two-week program. It would be nice if, during thefirst week, you could be listening to social media and find out that the campaign in the West isnot performing as well as the one in the East, and then change your investment thesis around theprogram -- cancel the one thats not performing well and double down on the one thatsperforming well.Theres a feedback mechanism increase that also can then benefit from handling data in a modernway or using more modern resources to get that feedback. When I say modern resources,generally thats pointing towards unstructured data types or textual data types. Again, if you cancomprehend and understand those within your overall information management status, you nowalso have a feedback mechanism that should increase your responsiveness and therefore makeyour business more competitive as well.Gardner: I think the whole concept of the immediacy to feedback, applied across variousaspects of business -- planning, production, marketing, go-to market, research, and to uses -- then
  7. 7. thats been the Holy Grail of business for a long time. Its just been very difficult to do. Now, weseem to be getting closer to the ability to do it at scale and at reasonable cost. So, these are veryinteresting times.Now, given that these payoffs could be so substantial, whats preventing people from getting tothis Holy Grail? Whats between them and the realization?Its the complexityWolken: I think its complexity of the environment. If you only had relational systems insideyour company previously, now you have to go out and understand all of the various systems youcan buy, qualify those systems, get pure feedback, have some proofs of concept (POCs) indevelopment, come in and set all these systems up, and that just takes a little bit of time. So themore 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. Thats the part where we think the tool chaincan help. But as far as understanding the environment, having someone who can help you walkthrough the choices and solutions and come up with one that is best suited to your needs, that’swhere 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 whatit is today, and work to figure out where the best areas of collaboration can be, where tools canadd 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? Thats something that we have triedto 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 youve never had a data warehouse before, and you are in that stage, thencreating your first one is kind of daunting, both from a price perspective, as well as complexityperspective or know-how. The same thing can occur on really any aspect -- textual data,unstructured data, or social sentiment.Each one of those can appear daunting if you dont have a skill set, or dont have somebodywalking you through that process who has done it before. Otherwise, its trying to put your handson 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 youre a smaller firm thathas a limited number of staff and theres this new demand from the line of business, because theywant to go off in a different direction and have more understanding that they couldnt get out ofexisting systems.How do you go out and attain that knowledge without duplicating the team, finding new vendortools, and adding complexity to your environment, maybe even adding additional data sources,
  8. 8. and therefore more data-storage requirements. Those are some of the major challenges --complexity, cost, knowledge, and know-how.Gardner: Its interesting that you mentioned mid-market organizations. Some of theseinfrastructure and data investments were perhaps completely out of their reach until a new wayto 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 thisto be distributed more down market? Why are mid-market organizations now more able to availthemselves of some of these values and benefits than in the past?Mid-market skillsWolken: As the products are well-known, there is more trained staff that understands the morecommon technologies. There are more codified ways of doing things that a business can takeadvantage of, because theres a large skill set, and most of the employees may already have thatskill 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 thecoming years, as well.Dell overall has maintained the status that we started with when Michael Dell started recreatingPCs in his dorm room from standard product components to bring the price down. That model ofmaking technology attainable to larger numbers of people has continued throughout Dell’shistory, 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. Oneexample would be what we call Quickstart Data Warehouse. It was designed to democratize datato a lower price point, to bring the price and complexity down to a much lower space, so thatmore 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 wequalified 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 45minutes with little more than directing a couple of IP addresses. You plug the box in, and itcomes up in 45 minutes, without you having to have knowledge about how to stand up, integrate,and qualify hardware and software together for an outcome we call a data warehouse.Another thing we did was include Boomi, which is a connector to automatically go out andconnect to the data sources that you have. Its the mechanism by which you bring data into it.
  9. 9. And lastly, we included services, in case there were any other questions or problems you had toset it up.If you have a limited staff, and if you have to go out and qualify new resources and things youdont understand, and then set them up and then actually run them, that’s a major challenge.Were trying to hit all of the steps, and the associated costs -- time and/or personnel costs – andremove them as much as we can.Its 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 tofirms that either didn’t have that luxury of that expertise link sitting around the office, or whofound that the price point was a little too high.Gardner: You mentioned this concept of the tool chain several times. Id like to hear a bit moreabout why that approach works, and even more detail about what I understand to be importantelements of it -- being agnostic to the data type, holistic management, complete view, and then ofcourse integrate it.In addition to the package, it sounds from your earlier comments that you want to be able toapproach these daunting issues iteratively, so that you can bite off certain chunks. What is itabout the tool chain that accomplishes both a comprehensive value, but also allows it to beadopted 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 timeis that were able to look at history, observe how other people have done things over time, andthen invest in the market with the realization that maybe something has changed here and maybea new approach is needed.Different point of viewWhereas the industry has typically gone down the path of each new technology oradvancement 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 differentpoint 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 alldatabase 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 alot 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 thatwe’ve acquired, or have created inside the company, were made to step into an existing
  10. 10. environment, recognize that there were other products already in the environment, and recognizethat 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 weeven came into the picture, so we’re focused on figuring out how we snap into what they alreadyhave in place, as opposed to a rip-and-replace strategy or a platform strategy that requires all ofthe components to be replaced or removed in order for the new platform to take its place.What that means is tools should be agnostic, and they should be able to snap into an environmentand work with other tools. Each one of the products in the tool chain we’ve assembled wasdesigned from that point of view.But beyond that, we’ve also assembled a tool chain in which the entirety of the chain deliversvalue as a whole. We think that every point where you have agnosticism or every point whereyou 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 abilityto keep sanctioned data and a set of tools and systems that comprehend it. The idea being that theentirety of the tool chain provides you with advantages above and beyond what the individualcomponents bring.Now, were perfectly happy to help a customer at any point where they have difficultly and anypoint where our tools can help them, whether its at the hardware layer, from the traditional Dellway, at the application layer, considering a data warehouse or otherwise, or at the tool layer. Butwe feel that as more and more of the portfolio – the tool chain – is consumed, more and moreefficiency is enabled.Gardner: It sounds as if rather than look at the ecosystem that’s in place in an organization as adetriment, youre trying to make that into an asset, and then even looking further to new productsavailable to bring that in. So I guess partnering becomes important.Already-made investmentWolken: Everything is an already-made investment in the company. If the premise to rip andreplace is from the get-go, then youre really removing the institutional knowledge, the trainingof the staff, and the investment into the product, not to mention maybe the integration work.Thats not something we wanted to start out with. We wanted to recognize and leverage what wasthere 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 anenvironment and how do you add value to it, not how do you cause replacement or destruction ofan existing environment in order to provide benefit.
  11. 11. Gardner: We have been talking about the tool chain in terms of its value for analytics andintelligence about the business and bringing in more types of data and information from externalsources.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 recoverystrategy, perhaps looking towards more storage efficiency. Is there an intramural benefit from theIT 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’sreally what we think about it as, then where does data first show up in a company? That’s insideof a database on the backside of an application most likely.And where is it last used inside of a company? That would generally be just before retirement orlong-term retention of the data. Then the question becomes how do you manipulate andotherwise utilize the data for the maximum benefit in the middle?When we looked at that, one of the problems that you uncover is that theres a lot of data beingreplicated in a lot of places. One of the advantages that weve put together in the tool chain wasto use virtualization as a capability, because you know where data came from and you know thatit was sanctioned data. Theres no reason to replicate that to disk in another location in thecompany, if you can just reach into that data source and pull that forward for a data analyst toutilize.You can virtually represent that data to the user, without creating a new repository for thatperson. So youre saving on storage and replication costs. So if you’re looking for where is thereefficiency in the lifecycle of data and how can you can cut some of those costs, that’s somethingthat jumps right out.Doing that, you also solve the problem of how to make sure that the data that was provisionedwas sanctioned. By doing all of these things, by creating a virtual view, then providing that viewback to the analyst, youre really solving multiple pieces of the puzzle at the same time. It reallyenables you to look at it from an information-management point of view.Gardner: Thats interesting, because you can not only get better business outcome benefits andanalytics benefits, but you can simplify and reduce your total cost of ownership from the ITperspective. Thats kind of another Holy Grail out there, to be able to do more with less.One of the advantagesWolken: Thats what we think one of the advantages can be, and certainly, as you have theadvantage to stand on the shoulders of people who have come before you and look at how theenvironment’s changed, you can notice some of these real minor changes and bring them
  12. 12. forward. Thats what we want to do with IT as partners and with the solution that we bringforward.Gardner: How should enterprises and mid-market firms get started? Are there some proveninitiation points, methods, or cultural considerations when one wants to move from thattraditional siloed platform and integrate them along the way, an approach more towards thisintegrated, comprehensive tool-chain approach?Wolken: There are different ways you can think about it. Generally, most companies aren’t justout there asking how they can get a new tool chain. Thats not really the strategy most people arethinking about. What they are asking is how do I get to the next stage of being an intelligentcompany? How do I improve my maturity in business intelligence? How would I get from Excelspreadsheets without a data warehouse to a data warehouse and centralized intelligence orsanctioned data?Each one of these challenges come from a point of view of, how do I improve my environmentbased upon the goals and needs that I am facing? How do I grow up as a company and get to bemore of a data-based company?Somebody else might be faced with more specific challenges, such a line of business is nowasking me for Twitter data, and we have no systems or comprehension to understand that. Thatsreally the point where you ask, whats going to be my strategy as I grow and otherwise improvemy business intelligence environment, which is morphing every year for most customers.Thats the way that most people would start, with an existing problem and an objective or a goalinside the company. Generically, over time, the approach to answering it has been you buy a newtechnology from a new vendor who has a new silo, and you create a new data mart or datawarehouse. But this is perpetuating the idea that technology will solve the problem. You end upwith more technologies, more vendor tools, more staff, and more replicated data. We think thisapproach has become dated and inefficient.But if, as an organization, you can comprehend that maybe there is some complexity that can beremoved, while youre making an investment, then you free yourself to start thinking about howyou can build a new architecture along the way. Its about incremental improvement as well astangible 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 waythey manage the information lifecycle, I would say you sort of lean into it in a way.If youre really after a performance metric and you feel like there is a performance issue in anenvironment, at Dell we have a number of resources that actually benchmark and understand theperformance and where bottlenecks are in systems.So we can look at either application performance management issues, where we understand theapplication layer, or we have a very deep and qualified set of systems around databases and datawarehouse performance to understand where bottlenecks are either in SQL language or
  13. 13. elsewhere. There are a number of tools that we have to help identify where a bottleneck or issuemight be from just a pure performance perspective as well.Strategic positionGardner: That might be a really good place to start -- just to learn where your performanceissues are and then stake out your strategic position based on a payback for improving on yourcurrent infrastructure, but then setting the stage for new capabilities altogether.Wolken: Sometimes there’s an issue occurring inside the database environment. Sometimes itsat the integration layer, because integration isn’t happening as well as you think. Sometimes itsat 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 notperforming well, the latter parts of the chain can’t perform either.And so at each step, weve looked at how you ensure the performance of the data. How do youensure the performance of the integration environment? How do you ensure the performance ofthe data warehouse as well? We think if each component of the tool chain in working as well as itshould be, then that’s when you enable the entirety of your solution implementation to trulydeliver value.Gardner: Great. Im afraid we well have to leave it there. Were about out of time. Youve beenlistening to a sponsored BriefingsDirect podcast discussion on better understanding thechallenges businesses need to solve when it comes to improved data and informationmanagement.And we have seen how organizations, not only need to manage all of their internal data thatprovides intelligence about the businesses, but also increasingly the reams of external data thatenables them to improve on whole new business activities like discovering additional customersand driving new and additional revenue.And weve learned more about how new levels of automation and precision can be applied to thetask 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 GeneralManager 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 ouraudience for joining us, and do come back next time.Listen to the podcast. Find it on iTunes. Sponsor: Dell Software
  14. 14. Transcript of a BriefingsDirect podcast on how Dell Software is working with companies tomanage internal and external data in all its forms. 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 • Data explosion and big data demand new strategies for data management, backup and recovery, say experts • Ocean Observatories Initiative: Cloud and Big Data come together to give scientists unprecedented access to essential climate insights • Case Study: Strategic Approach to Disaster Recovery and Data Lifecycle Management Pays Off for Australias SAI Global • Virtualization Simplifies Disaster Recovery for Insurance Broker Myron Steves While Delivering Efficiency and Agility Gains Too