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The Open Group Conference Panel Explores How the Big Data Era Now Challenges the IT Status Quo
 

The Open Group Conference Panel Explores How the Big Data Era Now Challenges the IT Status Quo

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Transcript of a BriefingsDirect podcast from The Open Group Conference in January on how big data forces changes in architecting the enterprise.

Transcript of a BriefingsDirect podcast from The Open Group Conference in January on how big data forces changes in architecting the enterprise.

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    The Open Group Conference Panel Explores How the Big Data Era Now Challenges the IT Status Quo The Open Group Conference Panel Explores How the Big Data Era Now Challenges the IT Status Quo Document Transcript

    • The Open Group Conference Panel Explores How the BigData Era Now Challenges the IT Status QuoTranscript of a BriefingsDirect podcast from The Open Group Conference in January on how bigdata forces changes in architecting the enterprise.Listen to the podcast. Find it on iTunes. Watch the video. Sponsor: The OpenGroupDana Gardner: Hello, and welcome to a special BriefingsDirect thought leadership interview series coming to you in conjunction with The Open Group Conference recently held in Newport Beach, California. Im Dana Gardner, Principal Analyst at Interarbor Solutions, and Ill be your host and moderator throughout these business transformation discussions. The conference itself is focusing on big data the transformation we need to embrace today. [Disclosure: The Open Group is a sponsor of this and other BriefingsDirect podcasts.]Were here now with a panel of experts to explore how big data changes the status quo forarchitecting the enterprise. Well learn how large enterprises should anticipate the effects andimpacts of big data, as well the simultaneous impacts of cloud computing and mobile.It’s been an interesting thread throughout the conference for me to factor where big data beginsand plain old data, if you will, ends. Of course, its going to vary quite a bit from organization toorganization.But Chris Gerty from NASA provided a good example: It’s when you run out of gas with yourold methods, and your ability to deal with the data -- and its not just the size of the data itself. When an enterprise architect and the business architect looked at data a few years ago, they might not have been asaware of these boundaries and the importance of data. They perhaps were thinking that thedatabase administrators and the business intelligence (BI) folks would take care of that, and theyjust had to manage the fruits of the data vis-à-vis applications and integration points.I don’t think that’s the case anymore, and one of the points were going to get into now is wherethe enterprise architect needs to be factoring the impacts of big data.Furthermore, there seems to be the need to do things differently, not just to manage the velocityand the volume and the variety of the data, but to really think about data fundamentally anddifferently. For many companies, data is now a product itself. That data can be monetized.
    • The analysis from the data becomes important to more and more people in the company, so thatyour employees, your partners, and those in your supply chain will be interacting with your data-- and the analysis from your data -- more than before.So I think we need to also think about data differently. And, we need to think about security, riskand governance. If its a boundaryless organization when it comes your data, either as a productor service or a resource, that control and management of which data should be exposed, whichshould be opened, and which should be very closely guarded all need to be factored, determinedand implemented.Expert panelWith that, let me now introduce our expert panel: Robert Weisman, CEO and Chief EnterpriseArchitect at Build The Vision; Andras Szakal, Vice President and CTO of IBMs FederalDivision; Jim Hietala, Vice President for Security at The Open Group, and Chris Gerty, DeputyProgram Manager at the Open Innovation Program at NASA.Chris, let’s start with you. You mentioned that big data to you is not a factor of the size, becauseNASAs dealing with so much. It’s when you run out of steam, as it were, with themethodologies. Maybe you could explain more. When do you know that youve actually run outof steam with the methodologies?Chris Gerty: When we collect data, we have some sort of goal in minds of what we might getout of it. When we put the pieces from the data together, it either maybe doesnt fit as well as you thought or you are successful and you continue to do the same thing, gathering archives of information. At that point, where you realize there might even something else that you want to do with the data, different than what you planned originally, that’s when we have to pivot a little bit and say, "Now I need to treat this as a living archive. Its a it may live beyond me type of thing." At that point, I think you treat it as setting up the infrastructure for being used later, whether it’d be by you or someone else.Thats an important transition to make and might be what one could define as big data.Gardner: Andras, does that square with where you are in your government interactions -- thatdata now becomes a different type of resource, and when you are not able to execute or availyourself of its value, then you know you need to do things differently?Andras Szakal: The importance of data hasn’t changed. The data itself, the veracity of the data,is still important. Transactional data will always need to exist. The difference is that you havecertainly the three or four Vs, depending on how you look at it, but the importance of data is inits veracity, and your ability to understand or to be able to use that data before the datas shelf liferuns out.
    • Some data has a shelf life thats long lived. Other data has very little shelf life, and you woulduse different approaches to being able to utilize that information. Its ultimately not about thedata itself, but it’s about gaining deep insight into that data. So it’s not storing data ormanipulating data, but applying those analytical capabilities to data.Gardner: Bob, weve seen the price points on storage go down so dramatically. Weve seempeople just decide to hold on to data that they wouldn’t have before, simply because they can andthey can afford to do so. That means we need to try to extract value and use that data. From theperspective of an enterprise architect, how are things different now, vis-à-vis this much larger setof data and variety of data, when it comes to planning and executing as architects?Robert Weisman: One of the major issues is that normally organizations are holding two ordersof magnitude more data then they need. It’s an huge overhead, both in terms of the applications architecture that has a code basis, larger than it should be, and also from the technology architecture that is supporting a horrendous number of servers and a whole bunch of technology stuff that they dont need. The issue for the architect is to figure out as what data is useful, institute a governance process, so that you can have data lifecycle management, have a proper disposition,  focus the organization on information data and knowledge thatis basically going to provide business value to the organization, and help them innovate and havea competitive advantage.Cant afford itAnd in terms of government, just improve service delivery, because theres waste right now oninformation infrastructure, and we can’t afford it anymore.Gardner: I suppose big data is part of the problem, dealing with so much in redundancy andduplication through the lifecycle of data and what have you, but the data is also part of thesolution in terms of getting the knowledge about what you should or shouldnt be doing as abusiness. So its difficult to know what to keep and what not to keep.Ive actually spoken to a few people lately who want to keep everything, just because they wantto mine it, and they are willing to spend the money and effort to do that. Jim Hietala, whenpeople do get to this point of trying to decide what to keep, what not to keep, and how toarchitect properly for that, they also need to factor in security. It shouldnt become later in theprocess. It should come early. What are some of the precepts that you think are important inapplying good security practices to big data?Jim Hietala: One of the big challenges is that many of the big-data platforms weren’t built fromthe get-go with security in mind. So some of the controls that youve had available in yourrelational databases, for instance, you move over to the big data platforms and the access controlauthorizations and mechanisms are not there today.
    • Planning the architecture, looking at bringing in third-party controls to give you the securitymechanisms that you are used to in your older platforms, is something that organizations aregoing to have to do. It’s really an evolving and emerging thing at this point.Gardner: There are a lot of unknown unknowns out there, as we discovered with our tweet chatlast week. Some people think that the data is just data, and you apply the same security to it. Doyou think that’s the case with big data? Is it just another follow-through of what you always didwith data in the first place?Hietala: I would say yes, at a conceptual level, but its like what we saw with virtualization. When there was a mad rush to virtualize everything, many of those traditional security controls didnt translate directly into the virtualized world. The same thing is true with big data. When youre talking about those volumes of data, applying encryption, applying various security controls, you have to think about how those things are going to scale? That may require new solutions from new technologies and that sort of thing.Gardner: Chris Gerty, back to your experiences at NASA. Youve taken the approach of keepingas much of that data and information as open as you can, fostering more research and the abilityfor people to do things with the data that you may never have been visioned yourselves. When itcomes to that governance, security, and access control, are there any lessons that youve learnedthat you are aware of in terms of the best of openness, but also with the ability to manage thespigot?Gerty: Spigot is probably a dangerous term to use, because it implies that all data is treated thesame. The sooner that you can tag the data as either sensitive or not, mostly coming from theperson or team thats developed or originated the data, the better.Kicking the canOnce you have it on a hard drive, once you get crazy about storing everything, if you dontknow where it came from, youre forced to put it into a secure environment. And thats justkicking the can down the road. It’s really a disservice to people who might use the data in auseful way to address their problems.We constantly have satellites that are made for one purpose. They send all the data down. It’scontrolled either for security or for intellectual property (IP), so someone can write a paper.Then, after the project doesn’t get funded or it just comes to a nice graceful close, there is thatextra step, which is almost a responsibility of the originators, to make it useful to the rest of theworld.
    • Gardner: Let’s look at big data through the lens of some other major trends right now. Let’s startwith cloud. You mentioned that at NASA, you have your own private cloud that youre using alot, of course, but youre also now dabbling in commercial and public clouds. Frankly, the pricepoints that these cloud providers are offering for storage and data services are pretty compelling.So we should expect more data to go to the cloud. Bob, from your perspective, as organizationsand architects have to think about data in this hybrid cloud on-premises off-premises, movingback and forth, what do you think enterprise architects need to start thinking about in terms ofmanaging that, planning for the right destination of data, based on the right mix of otherrequirements?Weisman: Its a good question. As you said, the price point is compelling, but the security andprivacy of the information is something else that has to be taken into account. Where is thatinformation going to reside? You have to have very stringent service-level agreements (SLAs)and in certain cases, you might say its a price point that’s compelling, but the risk analysis that Ihave done means that Im going to have to set up my own private cloud.Right now, everybodys saying is the public cloud is going to be the way to go. Vendors are goingto have to be very sensitive to that and many are, at this point in time, addressing a lot of theneeds of some of the large client basis. So it’s not one-size-fits-all and it’s more than just a pricefor service. Architecture can bring down the price pretty dramatically, even within an enterprise.Gardner: Andras, theres this mash up of cloud and big-data trends, the in-memory approaches,where we are no longer taking batches of data, cleansing it, and deduping it and bringing it into awarehouse, going through batch. Were still doing that of course, but it seems that for a numberof different applications of data and analytics, in-memory technology particularly, if you cancontrol that in a cloud environment, private cloud or otherwise, it’s starting to change the gamefor that fast, real-time feedback loop benefit.Its a roundabout way of asking if the cloud and big data come together in a way that’s intriguingto you and in what ways?Szakal: Actually it’s a great question. We could take the rest of the 22 minutes talking on this one question. I helped lead the President’s Commission on big data that Steve Mills from IBM and -- I forget the name of the executive from SAP -- led. We intentionally tried to separate cloud from big data architecture, primarily because we dont believe that, in all cases, cloud is the answer to all things big data. You have to define the architecture thats appropriate for your business needs. However, it also depends on where the data is born. Take many of the investments IBM has made into enterprise market management, for example, Coremetrics,several of these services that we now offer for helping customers understand deep insight intohow their retail market or supply chain behaves.
    • Born in the cloudAll of that information is born in the cloud. But if youre talking about actually using cloud asinfrastructure and moving around huge sums of data or constructing some of these solutions onyour own, then some of the ideas that Bob conveyed are absolutely applicable.I think it becomes prohibitive to do that and easier to stand up a hybrid environment formanaging the amount of data. But I think that you have to think about whether your data is real-time data, whether its data that you could apply some of these new technologies like Hadoop to,Hadoop MapReduce-type solutions, or whether its traditional data warehousing.Data warehouses are going to continue to exist and theyre going to continue to evolvetechnologically. Youre always going to use a subset of data in those data warehouses, and itsgoing to be an applicable technology for many years to come.Gardner: So suffice it to say, an enterprise architect who is well versed in both cloudinfrastructure requirements, technologies, and methods, as well as big data, will probably be inquite high demand. That specialization in one or the other isn’t as valuable as being able to cross-pollinate between them as it were.Szakal: Absolutely. Its enabling our architects and finding deep individuals who have thisunique set of skills, analytics, mathematics, and business. Those individuals are going to be thefuture architects of the IT world, because analytics and big data are going to be integrated intoeverything that we do and become part of the business processing.Gardner: Well, that’s a great segue to the next topic that I am interested in, and its aroundmobility as a trend and also application development. The reason I lump them together is that Iincreasingly see developers being tasked with mobile first.When you create a new app, you have to remember that this is going to run in the mobile tier andyou want to make sure that the requirements, the UI, and the complexity of that app don’t gobeyond the ability of the mobile app and the mobile user. This is interesting to me, because datanow has a different relationship with apps.We used to think of apps as creating data and then the data would be stored and it might be usedor integrated. Now, we have applications that are simply there in order to present the data and wehave the ability now to present it to those mobile devices in the mobile tier, which means it goesanywhere, everywhere all the time.Let me start with you Jim, because it’s security and risk, but its also just rethinking the way weuse data in a mobile tier. If we can do it safely, and that’s a big IF, how important should it be fororganizations to start thinking about making this data available to all of these devices and justpour out into that mobile tier as possible?
    • Hietala: In terms of enabling the business, it’s very important. There are a lot of benefits thataccrue from accessing your data from whatever device you happen to be on. To me, it is thatquestion of "if," because now there’s a whole lot of problems to be solved relative to the datafloating around anywhere on Android, iOS, whatever the platform is, and the organization beingable to lock down their data on those devices, forgetting about whether it’s the organizationdevice or my device. There’s a set of issues around that that the security industry is just startingto get their arms around today.Mobile abilityGardner: Chris, any thoughts about this mobile ability that the data gets more valuable themore you can use it and apply it, and then the more you can apply it, the more data you generatethat makes the data more valuable, and we start getting into that positive feedback loop?Gerty: Absolutely. Its almost an appreciation of what more people could do and get to theproblem. Were getting to the point where, if its available on your desktop, you’re going to find away to make it available on your device.That same security questions probably need to be answered anyway, but making it mobilecompatible is almost an acknowledgment that there will be someone who wants to use it. So letme go that extra step to make it compatible and see what I get from them. Its more of a culturalbenefit that you get from making things compatible with mobile.Gardner: Any thoughts about what developers should be thinking by trying to bring the fruits ofbig data through these analytics to more users rather than just the BI folks or those that are goodat SQL queries? Does this change the game by actually making an application on a mobiledevice, simple, powerful but accessing this real time updated treasure trove of data?Gerty: I always think of the astronaut on the moon. Hes got a big, bulky glove and he mighthave a heads-up display in front of him, but he really needs to know exactly a certain piece ofinformation at the right moment, dealing with bandwidth issues, dealing with the environment,foggy helmet wherever.Its very analogous to what the day-to-day professional will use trying to find out that quick e-mail he needs to know or which meeting to go to -- which one is more important -- and it allcomes down to putting your developer in the shoes of the user. So anytime you can getinteraction between the two, that’s valuable.Gardner: Bob?Weisman: From an enterprise architecture point of view my background is mainly defense andgovernment, but defense mobile computing has been around for decades. So youve always beendealing with that.
    • The main thing is that in many cases, if theyre coming up with information, the wholepresentation layer is turning into another architecture domain with information visualization andalso with your security controls, with an integrated identity management capability.Its like you were saying about astronaut getting it right. He doesnt need to know everythingthat’s happening in the world. He needs to know about his heads-up display, the stuff thatsrelevant to him.So its getting the right information to person in an authorized manner, in a way that he canvisualize and make sense of that information, be it straight data, analytics, or whatever. Thepresentation layer, ergonomics, visual communication are going to become very important in thefuture for that. There are also a lot of problems. Rather than doing it at the application level,youre doing it entirely in one layer.Governance and securityGardner: So clearly the implications of data are cutting across how we think about security,how we think about UI, how we factor in mobility. What we now think about in terms ofgovernance and security, we have to do differently than we did with older data models.Jim Hietala, what about the impact on spurring people towards more virtualized desktopdelivery, if you dont want to have the date on that end device, if you want solve some of theissues about control and governance, and if you want to be able to manage just how much datagets into that UI, not too much not too little.Do you think that some of these concerns that we’re addressing will push people to look evenharder, maybe more aggressive in how they go to desktop and application virtualization, as theysay, keep it on the server, deliver out just the deltas?Hietala: That’s an interesting point. I’ve run across a startup in the last month or two that isdoing is that. The whole value proposition is to virtualize the environment. You get virtual goldimages. You dont have to worry about whats actually happening on the physical device and youknow when the devices connect. The security threat goes away. So we may see more of that as asolution to that.Gardner: Andras, do you see that that some of the implications of big data, far fetched as it maybe, are propelling people to cultivate their servers more and virtualize their apps, their data, andtheir desktop right up to the end devices?Szakal: Yeah, I do. I see IBM providing solutions for virtual desktop, but I think it was really asecurity question you were asking. Youre certainly going to see an additional number ofvirtualized desktop environments.
    • Ultimately, our network still is not stable enough or at a high enough bandwidth to really makethat useful exercise for all but the most menial users in the enterprise. From a security point ofview, there is a lot to be still solved.And part of the challenge in the cloud environment that we see today is the proliferation ofvirtual machines (VMs) and the inability to actually contain the security controls within thosemachines and across these machines from an enterprise perspective. So were going to see moresolutions proliferate in this area and to try to solve some of the management issues, as well as thesecurity issues, but were a long ways away from that.Gardner: Okay, I am going to put you on the spot a little bit, because I want you to provide to ussome examples of how you think big data is being used in a way thats fundamentally differentthan traditional data.If you dont have permission to name these people dont, but you can just describe the use case.Lets just start with you Chris. You probably have quite a few in your own organization, but arethere any ways that youre aware of that people are using big data that illustrate howfundamentally different and powerful this is going to be?Most compellingGerty: We have several small projects that have come out of the events that we’ve worked on.The International Space Apps Challenge I mentioned before. These are mostly in thevisualization realm, but its the problems that go beyond those events that are really the mostcompelling. I’ll briefly touch on one.A challenge that we’ve put out in the last Space Apps Challenge was to write an app that wouldallow someone to use NASA data to allow a farmer anywhere in the world to have an iPhone appor iPad app and say. "I live here. What should I grow? What could make me the most money andhelp my village the most?"The team that worked on it quickly realized that even great satellite data didnt work for theirapplication. There are too many other factors. There was the local economy, the runoff levels,and things that they just didnt have access to from the NASA data. So they decided that this wasmore than a just weekend project and they wanted to build that data set that they needed, so thatthey could finally make the product.They found other collaboration mechanisms to continue the project after the Spaces AppsChallenge. They’ll be returning this year to the second one that we do in April with an entirelydifferent view on the world, because they actually have some data sets now that theyve beenbuilding up. They made some mechanism to capture it from the local environment.
    • Gardner: So that’s a great reminder that we’re not just talking about big data, but we’re talkingabout multiple big data and which ones you can pull together -- joined or otherwise -- to collateand produce big-data analysis results for something very, very interesting.Gerty: Big data, by itself, isnt magical. It doesnt have the answers just by being big. If you needmore, you need to pry deeper into it. That’s the example. They realized early enough that theywere able to make something good.Gardner: Chris, that’s a very good cause, but in a purely commercial sense, as we see morecompanies doing cloud ecosystem and partnership activities, when they start to share their datawith that big "if" of secured and provisioned properly with other people in their markets, in theirbusinesses, very powerful and interesting things can happen. Jim Hietala, any thoughts aboutexamples that illustrate where we’re going and why this is so important.Hietala: Being a security guy, I tend to talk about scare stories, horror stories. One example fromlast year that struck me. One of the major retailers here in the U.S. hit the news for havingpredicted, through customer purchase behavior, when people were pregnant.They could look and see, based upon buying 20 things, that if youre buying 15 of these and yourpurchase behavior has changed, they can tell that. The privacy implications to that are somewhatconcerning.An example was that this retailer was sending out coupons related to somebody being pregnant.The teenage girl, who was pregnant hadnt told her family yet. The father found it. There wasalarm in the household and at the local retailer store, when the father went and confronted them.Privacy implicationsThere are privacy implications from the use of big data. When you get powerful newtechnology in marketing peoples hands, things sometimes go awry. So Id throw that out just as acautionary tale that there is that aspect to this. When you can see across peoples buyingtransactions, things like that, there are privacy considerations that we’ll have to think about, andthat we really need to think about as an industry and a society.Gardner: Just because you can do something, doesnt necessarily mean you should.Allen Brown: Can I put some of the questions in and see how you can do with them? The firstone is more of a bit of a security question, but also concerns things like thoughts on self-protecting data, like the Jericho Forum issues, and another one that says, in terms of security, thatbig data may not have strong confidentiality and availability requirements, but for collaboration,doesnt integrity nearly always need to considered. Other examples are that there is no integrityrequirement.
    • Gardner: Jim, I think it’s best directed to you to start. These are issues about controlledmanagements. Any thoughts?Hietala: Ill get straight to the integrity piece. The integrity of the data, whether it’s on olderplatforms or big data, is certainly an issue. When folks are using big data, that data has to haveintegrity, and there has to be adequate controls protect the data. So I think that is kind of afundamental thing for big data as well.Gardner: Anyone else on these issues of protection?Gerty: It’s not only a matter of data protection. Its what we do with the data. Big data is a termthat is kind of heading towards the end of its usefulness, because its not the data and how large itis thats useful. Its actually how we apply these deep analytics solutions, for example Watson.You saw the Watson win on Jeopardy, but now Watson is a product that’s being used to helpsome customers diagnose disease and work with the insurance companies.How you actually utilize that data to derive value through this deep analytics solution is througha new set of artificial-intelligence applications called cognitive computing. So cognitivecomputing, how you drive all of this information, and how you apply it in the context of itsusefulness to privacy and security is going to be huge in the following years.Gardner: Allen, other questions from the audience or online?Brown: Interoperability is the focus of a couple of questions. One is asking if you can addressthe expected interoperability issues across semantics of big data. The other part of it asks what’sthe unique challenge or problems that unstructured, big data from Twitter, Facebook, and so onpresent?Gardner: This might be an area where the concepts work for traditional data, and it might stillbe the case that is we have to pull all these different data types, structured and unstructured,together to work in some holistic fashion. Bob, any thoughts about big data, correlating ofdifferent data is that different from the past? Is there something new?Weisman: Im looking at techniques that were pioneered 20-30 years ago on the artificialintelligence, knowledge base system side, and are still is relevant today. As a matter of facttheyre more relevant than theyve ever been. There is lot opportunity, but it doesn’t foregohaving a good interoperability architecture, understanding where your contacts are, and beingable to integrate data. Right now most of analytics is kiboshed, because they spent all their timedoing data integration, versus analytics, and it’s a great waste of a lot of peoples times.So if you architect this from the get-go, get the proper metadata, which will address some of theintegrity, and understand the concept of data quality which is what’s coming through, that will goa long way to resolving some of these issues, but the architecture is going to be key, as isrigorous planning.
    • More usableGardner: Andras, same question. Is there something new or different about treating data inorder to make it more useable?Szakal: Big data is coming to us in all sorts of forms and formats. It’s coming from differentsources. We dont really know the validity. The validity is determined by the application of theanalytics solution. Youll have to have some internal process, some governance process, todetermine whether youre getting the validity of the data that you expect.When I was working as a graduate student for the psychology department as the SPSSprogrammer, people would bring their work to me. They would try to apply analytics to makeany point they possibly could. Its the old story about making statistics mean anything you want.But you have to be very careful about how you do that, because it’s going to have a huge impacton your business.Gardner: Jim, in the realm of privacy and security, any thoughts about what types ofunstructured content you may or may not want to bring in? Is this something now that you needto consider, picking and choosing of data types with an overview or lens towards security andprivacy issues?Hietala: In terms of unstructured content, there’s a whole lot of work to be done there tounderstand the growth of that stuff in average enterprise and whats really in unstructured contentstores. A lot of that is ending up in collaboration platforms today, and most organizations don’thave a great understanding of what’s really in there.It’s the regulated data in there, sensitive data in there. That’s an area where there’s work to bedone by most enterprises to understand that unstructured content and the risk that it represents tothe business.Gardner: We haven’t got into it,, but another factor is the whole social sphere of data, andinformation that is being generated constantly.Brown: The next question is a concern about whether its causing a disruption to objectorientation. Object-oriented data is encapsulated by the application, and making big data sharedseems to break this approach. What are your thoughts on that?Gardner: All right, from an architectural standpoint were treating data a little bit differently,separating it entirely from an application or service.Hietala: We just did a study that of this exact same question and problem. We found that theresno official programming model of the big-data world or in the cloud, although it is all about theclient and integration with services. But there are all sorts of programming models out there. Iwould say that you apply the one that’s got the best and most appropriate approach.
    • Information centricWeisman: It’s starting to put the emphasis back on the information syllable and informationtechnology. Object orientation was meant to basically support an information-centric approach,and now it’s being used much more as a service-centric approach. Now we’re going to go back toa much more information-centric, information-engineering approach and a lot of the architectureenabled by big data.Gardner: Maybe you could just expand that a little bit for me? Does that mean we have adifferent type of application? That is to say that data is the application? What were theimplications of what you just said?Weisman: When object orientation first came out, the idea was to take the data and buildservices around it. Now, we have services that pass data back and forth. Most organizations havehundreds of applications with encapsulated data within them, and they can’t share it. Often thesame information is found in hundreds of applications, which causes a huge security headache.Now we should be looking at getting much more information centric which is the core ofinformation technology, information related technology.Gardner: So really its a flip architecturally, when you think about maintaining a pool resourceof information, and applications are either newly built to expose and leverage, or all yourexisting applications also have to bring into and connect to and integrate. Fair enough?Weisman: I think it’s a separation between process-centric services and information-centricservices and harmonizing those. That will probably be the best bang for the buck.Gardner: So now were into IT transformation and business transformation, and you have torethink your data center and your entire apparatus for supporting your storage. People are goingto get into that anyway for some of the reasons we talked about, but again, we could look at bigdata and say this is an accelerator to some of those transformation efforts.Brown: Something that has been troubling me is around the data architecture. Mike Walker, nowat Dell, on the live stream, is asking what specific guidance and best practices can you give toenterprise data architects to properly architect their information architectures.Weisman: Were talking that this afternoon. There’s going to be an entire track or two tracks ondata architecture, which will be providing the guidance and it’s big-data centric.Gerty: Youre still going to be able to identify the service that provides the authoritative sourcefor a set of data and marry that with other information, as necessary, whether it be sentimentanalysis or what not, but youre always going to have to be able to point to that authoritativesource.
    • Brown: Well, data architectures can be highly structured and big data can be somewhatunstructured. How do you marry the two?Authoritative recordsGerty: How do you marry the two? Transactional systems are still very important. You have tobe able to identify the authoritative records. Big data usually comes in multiple sources frommultiple, different venues. The best example of the use of big data is around sentiment analysis,taking feeds from Twitter, Facebook, and these multiple sources, and then being able to analyzethe information to the context of the authoritative sources. So your analytics have to take all ofthis into consideration.Brown: Okay, we are just out of time. I just want to get a quick comment on these two other livestreams. How are companies dealing with the shortage of big data scientists? Are they trainingcurrent employees?Gardner: A key question is who is actually spearheading this? Who is in the best position to bequalified? Under whose auspices do these big data initiatives fall? Let’s start with you Chris. Anyinsight as to how youve done it at NASA?Gerty: I would draw a parallel from when I was in Mission Control and pretty highly trained.They wipe your brain and fill it up with everything you need to know, but we werent reallyenabled to make those decisions, until we went through the data, page by page, and looked ateach individual blip. If you can automate those, then you need less of whomever it is whos doingthe job.Automation there would have helped us immensely to make those decisions on the fly, ratherthan going over pages and pages of data from our batteries charging. Its not maybe that you needmore data scientists, but you need the right data scientists. Then you need to be able to leverageoff of other people’s data scientists. Thats why open source is so attractive to us. You only needto do it once and then you can go off of it.Gardner: Jim Hietala, the people that should be doing this, their qualification certification,organizational structure, any thoughts?Hietala: Its way too early to certify people in this category right now. We really need individualswho went to graduate school to understand the proper application of analytics and mathematics.Those individuals would be highly valuable and prized, especially as they learn to how to applythat knowledge to your business.Gardner: It’s tough to find the people who have deep and the wide expertise. Last word you,Bob?
    • Weisman: We have to take a look at career development within the CIO ranks. Making sense ofdata requires good business knowledge and too many people are being isolated within the CIOrank. They should be circulating throughout the companies, so they know what the company isdoing, and then come back in. Its much more valuable.There are some programs now that are joint ventures between the computer science departmentsand the business schools, and I think those are at the graduate level. As Andras was saying, theycould provide people in their early 30s that can really do a fantastic job, and we really starttaking advantage of this.Brown: Thats all we have time for. I think youve done a marvelous job, thank you very much.Gardner: We’ve been talking with a panel of experts on how big data changes the status quo forarchitecting the enterprise. Weve heard how large enterprises should better anticipate andprepare for the effects and impacts of big data, as well the simultaneous impacts of cloudcomputing and mobile.This special BriefingsDirect discussion comes to you in conjunction with The Open GroupConference in Newport Beach, California. Id like to thank our panel: : Robert Weisman, CEOand Chief Enterprise Architect at Build The Vision; Andras Szakal, Vice President and CTO ofIBMs Federal Division; Jim Hietala, Vice President for Security at The Open Group, and ChrisGerty, Deputy Program Manager at the Open Innovation Program at NASA.This is Dana Gardner, Principal Analyst at Interarbor Solutions; your host and moderator throughthese thought leadership interviews. Thanks again for listening and come back next time.Listen to the podcast. Find it on iTunes. Watch the video. Sponsor: The OpenGroupTranscript of a BriefingsDirect podcast from The Open Group Conference in January on how bigdata forces changes in architecting the enterprise. Copyright The Open Group and InterarborSolutions, LLC, 2005-2013. All rights reserved.You may also be interested in: • Big Data Success Depends on Better Risk Management Practices Like FAIR, Say The Open Group Panelists • The Open Group Keynoter Sees Big-Data Analytics Bolstering Quality, Manufacturing, Processes • The Open Group Trusted Technology Forum is Leading the Way to Securing GLobal IT Supply Chains • Corporate Data, Supply Chains Remain Vulnerable to Cyber Crime Attacks Says Open Group Conference Speaker • Open Group Conference Speakers Discuss the Cloud: Higher Risk or Better Security?
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