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Sascom 2q-2013


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The second quarter edition of sascom is now available! Please share links with customers and prospects so that they can: …

The second quarter edition of sascom is now available! Please share links with customers and prospects so that they can:
• Download the apps for iPad or Android.
• Read the issue online.
• Download the PDF.

This issue features DSW VP Harris Mustafa on the cover, and is filled with story after story of how organizations are overcoming obstacles with one vital tool: data visualization. Check it out today to see how:
• Five organizations in five different industries are using data visualization.
• Cosmos Bank execs immediately grasp the big picture with visual analytics.
• XL Insurance Group is looking to visual analytics to help them beat the competition.

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  • 1. sascom2.Q2013®5 WAYSTO USE DATAVISUALIZATIONBIG DATA ATFAMILY DOLLARASSESS YOURSPREADSHEETRISKVP Harris Mustafashares how the shoemega-retailer getsthe right sizes tothe right storeDSW
  • 2. Your data is constantly growing and changing.Now it’s time to see it for all it’s worth.A report from CIO Market Pulse confirms that data visualization helpsbusinesses drive discovery, collaboration and competitive advantage.Get the speed of analytics without the wait for IT-generated reports.Explore billions of rows of data in minutes or seconds, visually presentedin a striking way that brings hidden patterns into plain sight. Then sharereal-time insights via the Web or mobile for faster data payoffs.77%Confirm improved decisionmaking as a top benefit.68%See the potential to collaborate,report and share information.VISUALANALYTICSPlan to use Visual Analyticsin their Big Data strategy.98%Each SAS customer’s experience is unique. Actual results vary depending on the customer’s individual conditions. SAS does not guarantee results, and nothing herein should be construed as constituting an additional warranty. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks ofSAS Institute Inc. in the USA and other countries. © 2013 SAS Institute Inc.All rights reserved. free report.
  • 3.  contentscover storyRight shoes, wrong size?Not at DSW. 14VP Harris Mustafa on how the shoemega-retailer ensures that no customerwalks away empty-handed.Organizations get the picture. Fast. 3 Five industries, five ways to use data visualization.Business analytics 6 Big data or big analytics – what’s the differenceand which is more important? Jim Davis explains.Family Dollar doesn’t discount big data 9In this Q&A, VP Scott Zucker describes howhigh-performance analytics is opening upnew possibilities in retail.Industry outlook 12 Three signs you’re ready to graduate fromspreadsheets to analytics.Tech highlight 18 High-performance data warehousing solutionsabound, but there are things you need to knowbefore implementing one.Become a new breed of CMO 21 Marketing executives who embrace big data willreap big rewards, both personally and professionally,says Wilson Raj.Hitting the mark with big data 23 XL Insurance Group talks about its visualanalytics initiative.Risk insights 28 How can organizations protect themselves from theirown employees? Try these five rules from James Lam.A taxing situation 30Three stories of how government agencies around theworld are tackling tax collection with analytics.Why Brazil’s largest bank isNo. 1 in Latin America 33 Banco do Brasil responds to customer needswith smart, targeted products and services.Best of blogs 36 There’s a lot more to big data than just sheervolume. Author Evan Stubbs explains.
  • 4. 2sas com2.Q2013Contact usFree usanne-lindsay.beall@sas.comVisualizing big dataBig data. It’s still the hottest topic going – and making it pay offcontinues to be a challenge. According to a recent IDG Researchsurvey, organizations’ biggest big data headaches are: 1) lack ofskills needed to run analysis and 2) difficulty in making data availableto users for analysis.In this issue, we’ve got story after story of organizations overcomingthose two obstacles with one vital tool: data visualization.SAS’ data visualization software, aptly named SAS®Visual Analytics,was designed to make big data accessible to all, regardless of technicalbackground. Not an analyst? Not a problem – simply drag and dropdata categories onto a visualization pane. In seconds, billions of recordsare analyzed and intelligent auto-charting displays the best visual foryour data.For more on data visualization, see how five organizations in five differentindustries [page 3] are making it work for them. You also don’t want tomiss Jim Davis explaining the difference between big data and biganalytics [page 6], or Family Dollar VP Scott Zucker on how big dataand high-performance analytics are changing retail [page 9].Thanks for reading,ANNE-LINDSAY BEALLEditor-in-Chief starting pointEDITORIAL DIRECTOR Kelly R. LeVoyerEDITOR-IN-CHIEF Anne-Lindsay BeallMANAGING EDITOR Waynette TubbsBLOG EDITOR Alison BolenCOPY EDITORSAmy Dyson, Chris Hoerter,Amy Madison, Trey WhittentonWEB SUPPORTShane HerrellEDITORIAL CONTRIBUTORSAlison Bolen, Thais Cardosa, Michael Dowding,Maggie Chen, Jim Davis, Ritu Jain, James Lam,Wilson Raj, Philip Russom, Jon Walters, Nancy WilsonCIRCULATIONDiana ChristoffersonART DIRECTION Marchellina WaughGRAPHIC CONTRIBUTORS Jeanne SalasPHOTOGRAPHY John Fernez, Steve MuirSAS World HeadquartersSAS Campus DriveCary, NC 27513 USATel: 919- 677- 8000Fax: 919- 677- 4444US and Canada Sales: 800-727-0025Printed in the USA. ISSN 1531-3986S99364.0413The teamsascom is published quarterly by SAS Institute Inc. Copyright © 2013SAS Institute Inc., Cary, NC, USA. All rights reserved. Limited copies maybe made for internal staff use only. Credit must be given to the publisher.Otherwise, no part of this publication may be reproduced without priorwritten permission of the publisher. SAS and all other SAS Institute Inc.product or service names are registered trademarks or trademarks ofSAS Institute Inc. in the USA and other countries. ® indicates USAregistration. Other brand and product names are trademarks of theirrespective companies. From time to time sascom may contain articleswritten by contributing writers not employed by SAS Institute Inc. Theopinions of these columnists do not necessarily reflect those of SASInstitute. SAS is the leader in business analytics software and services,and the largest independent vendor in the business intelligence market.Through innovative solutions, SAS helps customers at more than 60,000sites improve performance and deliver value by making better decisionsfaster. Since 1976 SAS has been giving customers around the world THEPOWER TO KNOW®. sascom “The SAS Privacy Statement governs SAS’use of data which may be gathered by the digital version of this SASpublication.” Read more: mediaFacebook SASsoftwareTwitter @SASsoftware
  • 5. 2.Q2013sas com3Picture this: billions and billions of rows of data stretching intoinfinity. What does staring at a billion rows of data get you?Not much. Mere mortals can’t get their heads around thatmuch data. That’s where visual analytics comes in.SAS Visual Analytics takes those billions of rows and, in justseconds, displays a visual that allows you to spot trends andopportunities. With one glance, you can explore massiveamounts of data in real time to see connections among datathat you never dreamed were linked, and suddenly you’re seeingyour business in a whole new light. And with an easy point-and-click interface, you don’t have to be a data scientist to use it.So, what does this look like in real life? Read on to find outwhat five organizations in five different industries are doing withSAS Visual Analytics.Organizationsget the picture. FAST.Five industries, five ways to use data visualization
  • 6. 4sas com2.Q2013TELECOMMUNICATIONSVisualize how traffic demandimpacts the networkThe largest telecommunicationsoperator in Italy, Telecom Italia,wanted to extend and reinforcethe monitoring of its mobilenetwork service as part of aprogram to improve the customerexperience. The company neededto define and analyze key perfor-mance indicators for mobilenetwork voice and data traffic,comparing its own performancewith competitors in some levelof detail.However, this is a fast-changingmarket. Telecom Italia must beable to respond quickly with newand improved offers to its custom-ers, and to analyze the impact ofthese offers. Analysis that isvaluable and makes sense todaymay be irrelevant tomorrow. Ontop of this, the volumes of datainvolved are huge.With data visualization, thecompany can compare the per-formance among all competitorsfor key indicators (such as acces-sibility or percentage of droppedcalls) on a single screen. In aninstant, users can see an overviewof areas of competitive strengthsand weaknesses.GOVERNMENTVisualize statistical patternsto understand and serve thepublic betterIf you’re wondering how to applyfor a license, want to know what todo about water seeping into yourflat or would like to report a fallentree, whom do you call? In HongKong, you simply dial 1823 as asingle point of contact for anyquestion relating to public services.You’ll get an answer from liveoperators who respond 24/7. Eachyear, the Hong Kong EfficiencyUnit (HKEU) responds to inquiries,suggestions and complaints thatit receives in roughly 3 millioncalls and 98,000 emails.By bringing together massiveamounts of information to uncoverstatistical trends, patterns andrelationships in the data, datavisualization software allows theHKEU teams to respond appropri-ately. For example, they can nowidentify the root causes of com-plaints, such as peaks in vehicleregistration applications, higher-than-usual water tables or landsubsidence. Data visualizationhelps Hong Kong understand itscitizens, improve service deliveryto the public, make informeddecisions and communicatemore effectively.FINANCIAL SERVICESVisualize customer trends hiddenin social media dataA leading UK-based financialservices organization wanted tomonitor and assess social mediatrends, attitudes and commentsabout its brands and services inreal time. The problem? Data drawnfrom social media conversations isRisk and Capital ManagementFrom large, multinational firms down to small communitybanks, financial institutions of all sizes often struggle tounderstand complex relationships within their portfoliosWireless Call QualitySwitches, cell technology and the specific phone set can all have an impact on call quality. Any one of thesecomponents can also contribute to dropped calls and affect a network operator’s overall service quality andrevenue. SAS Visual Analytics enables you to analyze call quality at a glance.
  • 7. 2.Q2013sas com5qualitative, unstructured and flows in24/7 in massive amounts. The bankneeded to repackage and presentthe data in ways that make sense.With the help of data visualization,the bank has uncovered newinformation on customer behavior,enabling the bank to plan andreact to customers’ needs basedon a detailed understanding ofwhat’s on their minds and currentmarket trends.SMALL-TO-MIDSIZEBUSINESSES (SMB)Visualize data anywhere, anytimeon mobile devicesData visualization is not just forlarge corporations and govern-ments. One SMB that uses SASVisual Analytics is DirectPay, acredit management organizationbased in the Netherlands. Itsservices include debtor financing,credit checks, billing, factoring,forward flow financing andcollection. DirectPay’s decisionmaking depends on effectivelyCustomer AnalysisMany retailers today have stores that span individual countries and the world. With so many locations, productsand customers to manage, retailers need an easy way to analyze business performance on a regional basis, all theway down to an individual customer’s basket. SAS Visual Analytics lets you easily analyze your sales and marketingperformance so you can identify ways to improve how you market to your customers, thus driving more sales.analyzing customer informationso that executives have greaterinsights into risks and opportunities.The challenge for DirectPaywas getting this information intothe right hands at the right time.With data visualization, DirectPaycan now easily create and sharethe results of analytics more ef-fectively, notably with field salesstaff who access the informationvia mobile devices before makingcustomer visits.RETAILVisualize new opportunitieshidden in customer dataSM Marketing Convergence Inc.(SM-MCI), an affiliate of SM RetailGroup, operates one of the larg-est customer loyalty programs inthe Philippines, collecting massivequantities of customer purchaseand spending data. In fact, SM-MCI’s current data exceeds 1billion transactions.With data compiled from itscustomer loyalty program, SM-MCI uses SAS Visual Analytics tounderstand buying patterns andidentify trends, which leads tobetter service – and greatercustomer satisfaction. SM-MCIuses its insight to improve thecustomer experience with relevant,timely offers and promotions.It also can work to acquire newmembers, reduce churn and iden-tify new up-sell opportunities. nlineLearn more about SAS Visual the webinar: Effectively RealizeData Visualization: across holding companies, and to effectivelymanage associated risks. SAS Visual Analytics deliverslightning-fast insights without the complexity.
  • 8. COLUMNbusinessanalyticsJim Davis is Senior Vice Presidentand Chief Marketing Officer for SAS.WHAT KIND OF BIG DATAPROBLEM DO YOU HAVE?The first step is to understand differences indata size, analytic capabilityDoes it seem like almosteverything is a “bigdata” problem rightnow? And nearly every vendoris offering big data or big ana-lytics solutions? Is big analyticsmore important than big data?And what is the difference? I’veencountered this confusion inthe market a lot over the pastyear as I’ve traveled the globetalking to business and govern-ment leaders about big data.In the process of explain-ing the market to others, I’vecome up with a clearer way toillustrate the landscape. Thisexplanation has helped a lot ofbusinesses understand whattype of analytic problems theyactually have, and sometimesit helps them see that theirproblems are more of the biganalytics variety instead of thestandard big data issue.Sometimes, for example, youdon’t have that much data, butit’s still taking you five hours torun a marketing optimizationjob because of the number ofpossible offers. There reallyaren’t a lot of records, but youhave to do multiple passes onthe data, running complex algo-rithms with each step. That’s abig analytics problem – not abig data problem.Let’s dig into thosedifferences a bit further.Our first step is to revisit thedistinction we’ve made overthe years between reactive andproactive analytics. Standardbusiness reports, ad hocreports, OLAP, and even alertsand notifications based onanalytics are in the reactivecategory. Now, reactiveanalytics can still be veryuseful. It’s required for a lotof finance and regulatoryreporting, and it helps businessusers perform ad hoc analysisevery day, but it is ultimatelyinforming you about the past.Proactive analytics – such asoptimization, predictive model-ing, forecasting and statisticalanalysis – is forward-looking.It allows you to identify trends,spot weaknesses or determineconditions for making decisionsabout the future. In addition tooptimization of complex prob-lems with many dependencies,it includes predictive modeling,regression analysis and otheradvanced methods for proac-tive decision making.The next thing we need todefine is big data. Put simply,when you have exceeded thecapacity of conventional data-base systems, you’re dealingwith big data. Before that, it’swhat I like to call “growingdata.” It is still a large amountof data, but it hasn’t hit thelimitations seen with big data.6sas com2.Q2013{ Proactive analytics – such as optimization,predictive modeling, forecasting andstatistical analysis – is forward-looking.It allows you to identify trends, spotweaknesses or determine conditionsfor making decisions about the future.
  • 9. 2.Q2013sas com7Today, we can store lots andlots of data, but processingtimes have become excessivebecause traditional storageenvironments are not conducivefor proactive analytics. Whenyou have reached a point whereprocessing times become unac-ceptable, you may be dealingwith big data sizes, but youmay also be dealing with biganalytics.To better understand the dif-ference, let’s create a chart withreactive and proactive analyticson the Y axis and the size of thedata on the X axis.Now we can see the fourmajor types of software solutionsavailable in the analytics markettoday. They are:BUSINESS INTELLIGENCE (BI).If you are dealing with a largeamount of data and providingreporting capabilities for endusers so they can gain accessto information, summarize dataand even drill down into thatdata themselves, you are deal-ing with business intelligenceapplications. These solutionsprovide a strong look at variousperformance aspects of thecompany in the past. That is BI.That is the lower-left quadrantin Figure 2.BIG DATA BI. Now, when datagets bigger and you’re deal-ing with outside data sourcesor – as more companies arestarting to see – you’re pull-ing in unstructured data, yourproblems are also gettingbigger. It’s taking users toolong to get the information theyneed, or it’s difficult to combinedata sources fast enough toprovide reports the way youused to and you need technol-ogy that allows quick access todata – but you’re still providingreactive analytics. This situationis the most common big datascenario in the market rightnow, and most businesses aretrying to solve it with SQL-based solutions. That is bigdata BI. It is in the lower-rightquadrant of Figure 2.BIG ANALYTICS. As I mentionedbefore, it takes a different kindREACTIVEALERTSOLAPAD HOC REPORTSSTANDARD REPORTSOPTIMIZATIONPREDICTIVE MODELINGFORECASTINGSTATISTICAL ANALYSISPROACTIVEANALYTICCAPABILITYDATA SIZEproactivereactivelarge big dataBIG ANALYTICS BIG DATAANALYTICSBIG DATA BIBIFigure 1 Reactive andproactive analyticsFigure 2 Data size andanalytic competence
  • 10. 8sas com2.Q2013of analytics to support forward-looking decisions. If you’re lookingat customer preferences,markdown optimizations orfraud predictions, you need adifferent type of architecture.These problems typicallyinvolve growing data sizes andproactive analytics. It’s not thesize of the data that’s slowingyou down, it’s the fact thatyou’re making multiple passeson data that may take hoursand hours to get results, andyou’re running advancedanalytic calculations that takelonger to process. For today’sissues, you need those answersin seconds or minutes. This isbig analytics. It is located in theupper-left quadrant of Figure 2.BIG DATA ANALYTICS. Now,what about organizations thathave a whole lot of data andare dealing with proactivedecision making? Here, we’retalking about hundreds of mil-lions of SKUs across multipleretail stores. We’re looking atfuture sources of data, too,such as telematics data in theauto industry, which can beuseful for manufacturers andinsurers. These are the typesof problems most businessesreally haven’t dealt with in thepast. And these aren’t smalldata problems. You don’t wantto summarize that information.Manufacturers want to predictsafety problems before theyaffect customers, while insurancecompanies want to adjustrate plans for the best drivers,for example. This is big dataanalytics. You’ll find it in theupper-right quadrant of Figure 2.My point here is not to saythat one is better than the other,but they each do differentthings and require differentarchitectures. As you look atwhat’s going on the marketand in your business, you mustunderstand the differencebetween each of these fourareas and how the differentproblems can be solved.Analytics continues to be abroad term in the market, butit’s worthwhile to look at theproblems you are trying to solveand then determine where youfall in this landscape. It will helpyou plan your next steps in yourbig data journey. nlineDownload white paper: BigData Meets Big Data from Jim Davis andother SAS executives on theCorner Office blog: more about big dataanalytics:{ Put simply, when you have exceeded the capacity of conventionaldatabase systems, you’re dealing with big data.
  • 11. Can you describe what big data meansin the retail industry?SCOTT ZUCKER: Big data is a paradigm shift for retailersand opens up a new world of possibilities for those retailerswho can manage it. Big data is data that we could neverhave processed and managed just a couple years ago. Ofcourse, it’s relative to your market. For instance, a largebank might not consider our data “big.”Big data allows us to look at product, time and location –our critical analytical levers – at a much lower level than weever did before. We might have looked at class or subclass,at total company, and then at month and sometimes atweek. Now we’re looking at SKU, store and day. As we startgoing down to that level, the amount of information that weneed to manage and analyze goes up exponentially.In the past five years, the amount of data that we manageIn the retail industry, it’s move fast or die. That’s why discount mega-chain Family DollarStores relies on high-performance analytics to shrink data processing times from days tominutes and to speed decision making. Scott Zucker is Vice President of Business Servicesat Family Dollar, which operates more than 7,400 stores in 45 states. Here, Zucker shares hisviews on big data, high-performance analytics, and how coupling both helps his companystay competitive.Family Dollardoesn’t discount big dataVP Scott Zucker on how high-performance analytics is changing retail2.Q2013sas com9
  • 12. 10sas com2.Q2013iterations. Before high-performance analytics, that couldtake weeks or even a month. Now you can get data backin front of management the next day.Not having to spend time managing the analyses or thatprocess opens up time for you to do other things, such asoperationalize analysis. There are certainly efficiencies todoing that. For instance, by not having to duplicate yourdata across multiple data marts, you’re able to reduceyour costs across a myriad of categories such as storage,maintenance, labor, etc. Any time you can transfer supportcosts into innovation costs, that’s a plus. For every dollaryou spend on support, you get zero dollars of value. Soif you can apply that incremental effort toward betteranalyses, reporting, decision making and forecasting,that’s real value.What other benefits are there in shrinking analyticaltimes that used to take days down to less than an hour?ZUCKER: It’s the time savings. All analytical exercises areiterative, and the more complex problems could take six,eight or 10 iterations. When you reduce to almost on-the-flyprocessing, it really makes a significant impact to your abilityto move fast and shorten that time.In retail, time is your enemy, meaning you always want tobe closer to the season when making decisions. Unfortu-has increased by 10 times, and most of that is structureddata. Right now, we’re not integrating unstructured datainto our data model – that’s the next frontier. You can justimagine how combining structured and unstructured dataat that same rate of growth will change the dynamics ofdata management.If you don’t have the tools to deal with big data, you’llbe at a competitive disadvantage.When you have that much flying at you, you really haveto prioritize and pick what you want answered, right?ZUCKER: You have to be very disciplined. In this era of bigdata, we really have to move to solutions that work inmemory or in database computing. If you don’t have thatcapability, there’s no question you will be left behind. Smalldata is gone. Data is just going to get bigger and biggerand bigger, and people just have to think differently abouthow they manage it.What opportunities does prioritizing data at thetransaction level create for retailers?ZUCKER: Profit is made – in other words, you win or lose – atthe store/SKU level. For instance, we used to plan pricingat the store and SKU level for three- to six-month seasonsand hope that the financials worked as anticipated. Now wecan crunch through and analyze huge levels of data on adaily basis and make changes in a much shorter window.Working in collaboration with SAS on a big data issue wewere facing, we recently dropped a process that took36 hours down to less than 45 minutes.That enables me to implement a promotion, and withinone day I could probably get a read on it. It changes myspeed to market dramatically so I can make changesmidweek on that sort of stuff versus monthly. You canmove a lot faster.The difference between exceeding Wall Street expectationsand meeting Wall Street expectations is being able to seethose trends in advance, analyze that data, and react quickly.How has high-performance analytics helped youbecome more agile?ZUCKER: High-performance analytics lets you bring tomarket ideas, services, products and marketing plans muchfaster than you would ever think possible. No one ever doesjust one iteration of an analysis, right? There’s always thefirst iteration that goes to management, and then they wantto look at it another way. We go back and forth for multipleFOUR BENEFITS high-performanceanalytics can bring to retailers Read more in the e-book High-Performance Retail:The Art of the Possible: and answer moreinnovative questions andreceive more precise answers.Now grocery stores cancalculate cross-elasticities ofdemand for thousands andthousands of SKUs.2Increase speedof analysis.Revenue optimization for alarge chain of departmentstores can be completed inless than two hours for theentire organization.3Give real-timedecision makingto retailers acrossthe supply chain.Store managers and associ-ates can have the informationthey need to provide thebest service when customerswalk into the store.4Avoid offer spam.High-performance com-puting can help companiesavoid sending target shop-pers too many messages,and too many of the wrongmessages.
  • 13. as their managers are today. In other words, these front-linefolks are going to have accurate, timely and actionableinformation at their fingertips – information that was usuallyonly available to their managers. Pushing information anddecision making down in the organization tends to flatten alot of things, including organizational hierarchies.I don’t mean to sound like [author] Thomas Friedman,but high-performance analytics will enable companies toempower their people, which in turn flattens existingbusiness models. That type of change will alter the com-petitive landscape in most industries, including retail.High-performance analytics will afford us the ability todo things that we probably, today, rely on companies todo for us. People will be empowered in ways that, frankly,we haven’t even thought of yet. nlineRead more on high-performance analytics:, for many retailers, the long lead times for importedgoods force you to make preliminary spring 2013 decisionsin early 2012. If you can make those decisions in July orAugust when the season’s done and you’ve sold throughmost of your markdowns, then you’re going to make a muchbetter decision than if you have to give sales estimates ofproduct a year in advance.Thinking more broadly, even outside of yourindustry, how could high-performance analyticschange the world?ZUCKER: I listened to a podcast recently featuring [manage-ment expert] Gary Hamel where he talks about the end ofmanagement. He was making a case that because of thedramatic rise in processing power coupled with collaborationtools, front-line team members are going to be as equipped“ Small data is gone. Data is just going to get biggerand bigger and bigger, and people just have tothink differently about how they manage it.” Scott Zucker, Vice President of Business Services, Family Dollar2.Q2013sas com11
  • 14. Ritu Jain is the Global Marketing Manager forSmall and Midsize Business Solutions at SAS.COLUMNindustryoutlook3 SIGNS YOU’RE READY TO GRADUATEFROM SPREADSHEETS TO ANALYTICSSmall and midsize businesses live by spreadsheets,but there are risksMost small and mid-size businesses arefirmly planted in theworld of Excel. And why not?Spreadsheets are easy to use,inexpensive and easily available– making them the preferredanalytical tool for many.But spreadsheets come withtheir own challenges. Designedby individual users for simplecalculations and projections,they can pose a real risk tothe organization when usedfor complex, collaborativeplanning and analyses.The problem is that manycompanies don’t recognizethat they’ve become too bigfor spreadsheets and othersimplistic analytical tools.As a result, they get mired incomplexity, inconsistency,and overall poor and delayeddecision making.To avoid getting boggeddown, watch for the follow-ing signs that indicate you areready for analytics:1When you havetoo much data.Everyone is talking about bigdata. And rightly so. More datais being generated today thanever before in the history ofmankind. But it is not aboutpetabytes or exabytes. Youhave a problem when you can’tderive insights from your datafor timely decision making.Customer data can be yourwindow into your customers’likes, dislikes, behaviors andattitudes. If you can’t use itto build customer intimacy,improve return from yourmarketing spending, orleapfrog the competition,why even collect the data?2When you’re nolonger in tunewith your customers.Customers expect you todeliver personally relevantproduct and service informationvia their preferred channels.Failure to meet their demandscan frustrate them or turn themaway. Conversely, if you can{ The problem is that many companiesdon’t recognize that they’ve becometoo big for spreadsheets and othersimplistic analytical tools.12sas com2.Q2013
  • 15. 2.Q2013sas com13Assess your spreadsheet risk by takingthis online quiz: promotions theway that online fashion retailerGilt Groupe does, you canconvert browsing members topaying customers. Lukewarmresponse to promotions ordisappointing campaignresponse rates may be a signyou’re not connecting withcustomers. You need todetermine if you know yourcustomers well. Are yourproducts, services and messagealigned with your customers’preferences, needs andinterests? If not, it is time toconsider investing in morerobust analytics.3When regulatorycompliancebecomes a resourcedrain.The legislative environmentis becoming increasinglycomplex across all industriesand markets. Federal and stategovernments continue to issuenew regulations and standards,and compliance reportingrequirements are becomingall-consuming. When a compli-ance officer at a US credit unionsaw her limited IT resourcesused week after week tomanually scan hundreds ofthousands of rows of data forfraudulent activity, she knew itwas time to invest in an alternativesolution. Analytically robustsolutions can help ensure dataintegrity and maintain completeaudit trails so you can sleepat night.If you think your companyis too small to invest in realanalytics, you may be lullingyourself into complacency.Successful companies suchas TrueCar and Build-A-BearWorkshop aren’t letting theirsize impede them. They’refocusing on analytics to ensurethat their information needsare met.SMBs: Make the most of your dataBig or small, data is your most important corporate asset.Don’t let inadequate tools like spreadsheets limit you.With SAS Visual Analytics, you can:• Visually explore all of your data in minutes,even seconds, for better, more precise insights.• Empower all of your users with sophisticated,yet easy-to-use, analytics.• Liberate IT by letting business users accessthe information they need, all while maintaining dataintegrity and security.• Access critical information anytime, anywhere withmobile BI – even when there is no Internet connectivity. Online interactive demos: nlineFollow the SAS SMB blog
  • 16. 14sas com2.Q2013Right shoes,wrong size?Not at DSWShoe retailer DSW knows exactly how you feel whenyou find the perfect pair of shoes – just not in yoursize. That‘s why the second-largest shoe retailer in theUS chose SAS Size Optimization, including SAS Size Profilingand SAS Pack Optimization, to stock the right shoes in theright location at the right time.DSW is seeing early indications that customized ship-ments can reduce lost sales from stock-outs, increasecustomer satisfaction, and reduce the percent of stockmarked down.DSW has 363 stores in 41 states and runs an additional344 shoe departments for other retailers. The company isgrowing fast – with 37 new locations in a single year inaddition to online and mobile shopping platforms.While shoes are an age-old product, the way they aremarketed has changed a lot, notes Harris Mustafa, DSW‘sExecutive Vice President of Supply Chain MerchandisePlanning and Allocation. “Customers want to transact attheir convenience, not yours.”Seasonal sales and overseas suppliers create additionalchallenges.Finding a better way to stock storesFor years, DSW stocked its stores based on a nationalaverage size profile, even if the tastes and shoe sizes ofcustomers varied from store to store. That meant lost saleswhen boots sold out too quickly at one store – and lostprofits when those same boots had to be marked downat another store.Size profiles and demandforecasts ensure that nocustomer walks awayempty-handed
  • 17. 2.Q2013sas com15“ Our out-of-stocks are fewer, our markdowns arefewer and our margins are higher – which, at theend of the day, is what every retailer wants.” Harris Mustafa, Executive Vice PresidentSupply Chain Merchandise Planning and Allocation, DSWWhat DSW discoveredwith analytics• The 20 million members of itsloyalty program contribute adisproportionately largeamount of total revenue.• Customers who shop usingmore than one channel spendsignificantly more than single-channel customers.• Creating “clustomers” of low-and high-margin customersbased on their purchasingbehaviors helps DSW optimizehow those customers shop withthe retailer.
  • 18. 16sas com2.Q2013“It wasn’t the most efficient way to run your inventorymanagement,” says Mustafa.Meanwhile, DSW was learning a lot about the 20 millioncustomers who are part of its loyalty program and accountfor 85 percent of its revenues. It was already doing targetedmarketing – why not targeted inventory management?In looking for a solution, DSW considered developingits own system and looked at a solution designed by afirm whose software it was using for other projects.DSW ultimately chose SAS because of its “long, greathistory of statistics-driven solutions,” Mustafa says. “Easeof use was also important.”A key factor for DSW was the SAS solution’s “truedemand” calculations. Relying solely on historical salesdata can be misleading because it misses the demand foritems that weren‘t in stock.True demand estimates size, store and weekly sales datafor situations when there is not enough useful data becauseof those significant early season stock-outs.DSW hosted a trial of SAS and one other finalist, givingboth solutions sales data to create size profiles and recom-mend pack configurations for the next product/inventorybuying cycle. The recommendations were comparedagainst what actually sold in the next sales cycle. The SASresults were much more accurate.Moving the solution into productionMustafa has been involved in deploying eight major solutionsin the last six years. “I would say the SAS implementationis in the top one or two for the least number of problems.The SAS team was very responsive. It has, frankly, exceededour expectations.”In addition, DSW has had great success convincingsuppliers to work with the new solution. In somecases, suppliers managed the size packs, soMustafa wasn‘t certain how they would respondto requests for a more customized approach.“These are big companies that are fairly set inhow they distribute their product,” Mustafa says.But they’re on board – 90 percent of DSW‘spartner shoe companies are using the customizedpack recommendations.The result? “Our out-of-stocks are fewer, ourmarkdowns are fewer and our margins are higher –which, at the end of the day, is what every retailerwants,” Mustafa says. “We were looking for a robustsolution that was simple to use, and that’s what weare seeing.”Who else is using SAS®Size Optimization?Fast-fashion retailer Wet Seal is using optimized size profil-ing to:• Increase same-customer and same-store sales.• Calculate and reduce sales losses.• Improve margins through reduced markdowns.• Keep store inventory at a manageable level.• Increase customer loyalty and satisfaction. is SAS®Size Optimization designed for?The solution is designed for any retailer that sells sizedmerchandise or stocks assorted products that aredistributed together. Similarly, any organization thatships assorted merchandise to multiple locations couldfind the solution useful in optimizing shipment profiles.What does SAS®Size Optimization do?SAS Size Optimization transforms sales data intopredictions for future sales and inventory needsby size and determines the optimal case-packsupply to meet demand. nlineLearn more about SAS Size Optimization: High-Performance Retail e-book:
  • 19. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies.© 2013 SAS Institute Inc.All rights reserved. S99364US.04132013 LocationsAmsterdam · June 11–12Orlando · October 22–24Innovate. Optimize. Transform. The Premier BusinessLeadership Series is an exclusive, invitation-only event forsenior executives from public and private organizations.This unique learning and networking experience hascarefully curated content with an eye on competing in aglobal business environment. Attendees experience:• High-caliber presentations on the most current trends.• Discussions addressing the challenges of leadership.• Sessions for sharing specific business issues.Premier Speakers. Premier Audience.Premier Leadership.Orlando · October 22–
  • 20. Philip Russom is the Research Directorfor Data Management at The DataWarehousing Institute (TDWI). He hasbeen an industry analyst at ForresterResearch, Giga Information Group andHurwitz Group, specializing in BI issues.COLUMNtechhighlightTOP 10 PRIORITIES FOR HIGH-PERFORMANCEDATA WAREHOUSINGThis quick guide will help you determine yourbusiness needs and technology requirementsBig data volumes, increas-ingly complex analyticworkloads, growing usercommunities, business require-ments for real-time operation… these are just a few of thechallenges IT faces. The goodnews is that high-performancedata warehousing is helpingorganizations meet thesechallenges, achieve speed –and scale as needed.High-performance datawarehousing solutions abound,but there are a few things youneed to know to ensure success.Here are the top 10 prioritiesto consider when implementinga high-performance datawarehousing platform:1Enable new business practicesbased on high-performancebusiness intelligence (BI) datawarehousing (DW), dataintegration and analytics. Thisis what high-performance datawarehousing is really about.Expect to apply high-perfor-mance data warehousingoptions to more businesspractices as your organizationmoves deeper into businessanalytics (demanding work-loads for queries, mining,statistics) and big data (scalingto massive, diverse data setsto discover new facts aboutthe business).2Make real-time operation yourfirst priority for high-perfor-mance data warehousing.Collecting, processing anddelivering time-sensitive datais the key enabler of most newapplications that businessesare currently clamoring for,including operational BI,operational analytics, just-in-time inventory, facilitymonitoring, price optimization,workforce management, frauddetection and mobile assetmanagement.3Make scalability your secondpriority. On the one hand, youhave no choice but to keeppace with growth in datavolumes, BI user communitiesand burgeoning bodies of{ Select databaseplatforms andanalytic tools thatare designed forhigh performance.18sas com2.Q2013
  • 21. 2.Q2013sas com19reports. On the other hand,tapping new data sources –Web data, social media,traditional enterprise applica-tions – can provide richerinformation for businessprograms, such as 360-degreeviews, sentiment analysis,operational efficiency, websitevisitor analysis and customerrelationships beyond theusual channels.4Hardware: Use it, but don’tabuse it. There’s no doubt thatservers, networks and storageare key components of anyperformance strategy, but hurl-ing hardware at performanceproblems raises the cost ofhigh performance. It also dullsa team’s optimization expertisefor software and data. Balanceyour reliance on hardware withsoftware optimization skills andwell-performing designs forqueries, reports, data modelsand ETL jobs.5Select database platformsand analytics tools that aredesigned for high performance.There are many types to consider,including analytics databasemanagement systems, columnardatabases, appliances and otherengineered systems, as well asHadoop with MapReduce andother non-SQL databases.While the data structures andworkloads of these tools andplatforms will deliver perfor-mance gains out of the box, youshould still expect to performsome development work toremodel and tweak dataprocessing for additional gains.6Rely on specialized platformand tool functionality forcertain performance gains. Forexample, in-database analyticsassists the overall performanceof non-query-based analyses byalleviating the need to moveanalytics data before analysis.In-memory processing decreasesquery response and analyticsrescore time by decreasingdisk I/O. Columnar data storesaccelerate column-orientedqueries by collating the data oftable columns in physical storage.7Consider the many newarchitectures that boostperformance. If your enterprisedata warehouse is still on asymmetric multiprocessing(SMP) platform, make migra-tion to massively parallelprocessing (MPP) a priority.Consider distributing your datawarehouse architecture,especially to offload a work-load to a standalone platformthat performs well with thatworkload. When possible, takeanalytics algorithms to thedata, instead of moving data tothe algorithm (as is the DWtradition); this new paradigm isseen with in-database analytics,Hadoop with MapReducelayered over it, and gate-arrayprocessing in some storageplatforms and appliances.Hadoop and MapReduce arean alternate MPP architecturethat fits some analytics work-loads quite well.8Keep your performanceoptimization skills sharp andcurrent. Ample hardware andfast, scalable software canautomatically provide goodperformance for many situa-tions. However, it’s inevitablethat some queries, reports,data models, ETL logic andanalytics algorithms will needtweaking and tuning beforethey achieve the desired perfor-mance level. So maintain youroptimization skills, especiallyfor SQL tuning and data modeltweaking. Get optimizationtraining, if needed.{ Hardware: Use it, but don’t abuse it. Hurling hardwareat performance problems raises the cost of high performance.
  • 22. 20sas com2.Q20139Design and develop withhigh performance in mind.Most teams have standardsfor the look and feel of reports,approaches to modelingwarehouse data, preferredinterfaces for specific datasources or targets, and thestyle of handwritten code.Whoever determines and en-forces these standards shouldensure that they also fosterhigh performance. Of course,the performance of any newdevelopment work should betested during peer review andquality assurance processes.10Develop and apply a technologystrategy for high-performancedata warehousing. No singleapproach to attaining high-performance data warehousingis adequate for all situations.Excerpted from the TDWI Best PracticesReport High-Performance Data Warehousing,Q4 2012. ©2012 by TDWI (The DataWarehousing InstituteTM), a division of1105 Media, Inc. Reprinted with permission.Visit for more information. nlineDownload the full TDWI requirements for high-performance successUp-to-date hardwareplatform components,especially CPUs,memory and storage.Enterprise softwareplatforms and toolsdesigned specificallyfor demanding datawarehousing andanalytics applications.Technical users’ globalarchitectures for dataand team standardsfor BI development,especially whengoverning datamodels, SQL coding,ETL logic and analyticalgorithms.Tactical tweaking andtuning on the locallevel, as requiredby reports, datastructures, analyticalgorithms, ordeficient tools andplatforms.
  • 23. 2.Q2013sas com21Become a newbreed of CMOEmbracing big data can add years toa marketing executive’s tenureThe average tenure of a FortuneGlobal 500®Chief Marketing Officer(CMO) has been compared to the1CUSTOMER EXPERIENCEIn the past, marketers analyzedcustomer feedback with minimalconsideration of operational andfinancial data. Big data offers richinsight unachievable by examiningcustomer feedback data alone.For instance, CMOs can useoperational data in call centers(e.g., wait times or time to resolution)to improve the customer experienceacross channels. Operational datacan also reveal training opportunitiesto enable frontline staff to deliverbetter service.lifespan of a fruit fly. Why is the position so precarious?In a recent article for Chief Marketer, Wilson Raj, SASGlobal Customer Intelligence Director, tackles this ques-tion and offers a solution: Evolve. Become a new breed ofCMO – one who recognizes big data as the fundamentalconsequence of our new market landscape and takesadvantage of it with high-performance analytics.Raj makes a strong case that CMOs who adopt anintegrated marketing management strategy with big datacan make a substantial impact in these four key areas:
  • 24. 2CUSTOMER ENGAGEMENTTo engage your customers successfully, you mustknow who they are, where they are, what they wantand when they want it – across all channels.This is a huge challenge for marketers, but with big dataanalytics CMOs can exert tremendous influence oncustomer engagement. They can find out what needsto change to achieve positive customer engagements,and, better still, what customers want.3CUSTOMER RETENTION AND LOYALTYBig data lets marketers augment existingcustomer touch points and anticipatenew ones to keep valuable customers loyal in abrand-fickle world. Further, big data analyticscan help CMOs allocate resources to driverevenue through successful loyalty initiatives.4MARKETING OPTIMIZATION/PERFORMANCEAs marketers shift budgets fromtraditional to digital marketing channels(email, social media, search engineoptimization, display advertising andmobile), CMOs need to know theoptimal marketing spending acrossmultiple channels. With big data, CMOscan continuously optimize marketingprograms through testing, measure-ment and analysis. With a test-and-learnapproach, CMOs can deliver on the keydeterminant of longevity: return oninvestment.{ With big data, CMOscan continuouslyoptimize marketingprograms throughtesting, measurementand analysis.THE BOTTOM LINE:CMOs who capitalize onbig data will reap bigrewards, both personallyand professionally.Become a new breed of CMOManage customer relationships across all channelsWith the new release of SAS Customer Intelligence, CMOs get a consolidated view of customer preferences andexpectations across all channels, including Web, point-of-sale, social media, mobile and more. Marketers can use thisinformation to create consistent, coordinated customer experiences that will move customers along in the buying cycle.Enable dynamic, relevant customer interactionsThe analytics in SAS Customer Intelligence deliver perceptive and timely insights for automated, highly targetedmarketing campaigns. Marketers can react to results quickly, testing and learning on the fly to constantly improvecampaign ROI and customer experience.Extract value from big data to make the best decisionsSAS Customer Intelligence brings all customer data (preferences, demographics, lifetime value, etc.) and marketingdata (response rates, campaign performance, resource allocation, etc.) together in one place. CMOs have theinformation they need to make the right strategic and tactical decisions. Learn more about SAS Customer Intelligence: nlineRead more from Wilson Raj on the Customer Intelligence Knowledge more about big data: com2.Q2013
  • 25. Hitting themark withBIG DATAXL Insurance Group talks aboutits visual analytics initiative2.Q2013sas com23
  • 26. 24sas com2.Q2013Insurers have long seen data as a source of competitiveadvantage. But data alone is worthless – it’s the insightsderived from the data that matter, says Kimberly Holmes,Senior Vice President of Strategic Analytics with XL InsuranceGroup’s Bermuda office. And with the emergence of bigdata, she notes, the possibility for deriving insights isincreasing dramatically.Yet, for those insights to have an impact on the business,they have to have the attention of senior underwriters. “Thedata analytics means nothing without the decision makersembracing it,” Holmes insists. “We see a lot of our com-petitors create models that don’t have an impact becausethe underwriters don’t use them.”To foster a more collaborative approach to the analysisof large volumes of data, XL ($45.1 billion in total assets)currently is implementing SAS Visual Analytics technology.Holmes describes the solution as a powerful communica-tion tool for creating a partnership of exploration betweenthe analytics team and underwriters. She says the tool willbring the expertise of the decision makers and otherstakeholders more deeply into the analytics process bydemonstrating the meaning of data more readily andinspiring further exploration and insight.“It’s really a demonstration of the expression that ‘everypicture is worth a thousand words,’” Holmes adds. “Thekey to getting people to embrace new insights and changehow they make decisions is that they believe in their gutthat this insight is true.”Rewriting the rules of the gameHolmes asserts that the world of insurance is changingexponentially as volumes of available data rapidly expandand sources of data proliferate. As a result, roles within theinsurance enterprise will change, along with the terms ofcompetition. “Commercial insurance will become moreefficient by creating more automation in decision makingand how we access our customers,” she predicts. “Thosechanges will happen more rapidly in smaller-accountbusiness, but we need the right technology and data totake advantage of that.”Holmes characterizes XL as one of the few carriers in thecommercial insurance domain to act on this vision. “Weexpect investments such as SAS Visual Analytics to createenormous competitive advantage and shareholder value forXL,” she says.According to Celent Senior Analyst Benjamin Moreland,though, few insurers are ready to talk openly about theirbig data-related initiatives. The world of big data consti-tutes a paradigm shift for carriers, many of which continueto struggle with issues in their traditional transactional data.“Carriers continue to have trust issues with internal data,”What can you do withSAS®Visual Analytics?• Apply the powerof SAS Analyticsto massive amountsof data.• Visually explore dataat the speed of sight.• Share fresh insightswith everyone,everywhere, via theWeb or iPad®.
  • 27. 2.Q2013sas com25Moreland says. “Many insurers are not used to using datafor operational status and decision support because oftheir skepticism. Also, business-line-specific data orienta-tion has resulted in inconsistencies in reports, leavingC-level officers to ask, ‘Which report should I believe?’”Insurers that can take advantage of large amounts andtypes of data early on will be able to do better on pricingand customer segmentation, Moreland says. Their chal-lenge will be driving data into the decision-making process.“Senior leadership often makes decisions on anecdotalevidence,” he notes. “Their instincts may be strong, butthey have to determine the worth of those instincts basedon whether the datasupports it.”Big data isn’t just amatter of the volumeand source of data,but also the speed atwhich it is processed,Moreland says. In thepast, carriers couldcrunch numbers overtime, distributereports and thenmake decisions.Today many moredecisions need to bemade in or near real time. Whereas traditionally underwrit-ers may have reviewed overexposure in a given area inhindsight, Moreland says, “The task for IT today is to bringopportunities to underwriters and other decision makers tosupport decisioning as events are happening.”Effective handling of big data, he suggests, will alsoenable an increasing range of automated underwritingdecisions in near-real time, such as preventing the writingof new business in an area with the potential to be struckby a developing weather event.Martina Conlon, a principal at Novarica, suggests thatcompany size will be a factor in how – and how quickly – agiven insurer will adopt big-data-related capabilities forunderwriting and other purposes. Larger insurers, she says,have made far more progress in the use of large volumes ofdata – for example, telematics, geo-spatial data, mobileinformation, social media data, automated informationfrom weather services and even clickstreams of visitorsto their websites.“Very large carriers are leveraging big data and opera-tionalizing automated analytics in their business processes,”Conlon says. “Below the top tier, most are dealing withmore basic issues, such as implementing solid core systemsand trying to establish a baseline business intelligence“ We expect investments such asSAS Visual Analytics to createenormous competitive advantageand shareholder value for XL.”Kimberly Holmes, Senior VPof Strategic Analytics, XL Insurance
  • 28. 26sas com2.Q2013infrastructure for an integrated view of their data, as wellas trying to marry-in structured external data.”Conlon says the greatest big data barrier for small carriersis cost of entry, as both initial costs and maintenance arehigh. Second-tier and smaller carriers also struggle to findthe right talent to adopt big data capabilities. “Lower-tiercarriers don’t have the resources to determine whethersuch initiatives are worth it. Bigger firms can afford toinvest in the analysis to make the business case.”Vendors will help smaller carriers punch above their RDbudget weight, suggests Conlon’s Novarica colleague GregWittenbrook. Vendor products will begin including big-data-related functionality or enabling capabilities, Witten-brook says, and more data providers will emerge.Life-changingtechnologyWhereas personallines PC insur-ance has led thebig data trend,commercial linesare approaching abreakout stage.Capgemini VicePresident TonyPavia predicts thatcommercialinsurers thatembrace big datawill gain marketshare at the expense of laggards. But Pavia says big dataalso is driving changes in the life insurance industry. “Termlife underwriting is dramatically different than it was five orsix years ago, because of the use of aggregators of data,”he says. “Across the industry, carriers are going to fallbehind because they lack the culture to embrace change.Over the next five years you’ll see a real separation be-tween those that are rapidly migrating to new environmentsand those that are not.”Wittenbrook notes that life insurers are looking fornew opportunities to use data to decrease the costand intrusiveness of life insurance underwriting, whichinvolves the administration of various medical tests.Insurers may be able to take advantage of the generaltrend of consumers to share personal data in exchange fordiscounts, as well as the move to broader medical recordsand the availability of social media data. “However, there isa creepiness factor related to an insurer knowing too muchabout a client,” he cautions, “and insurers also need to deal“ Insurers that can take advantageof large amounts and types ofdata early on will be able to dobetter on pricing and customersegmentation.”Benjamin Moreland, Senior Analyst, Celent
  • 29. 2.Q2013sas com27with constantly changing laws and regulations governingwhat they can and cannot access.”Prudential Financial ($961 billion in assets under manage-ment) is sensitive to the “creepiness factor” and is steeringclear of it, according to Mike McFarland, VP of Underwritingin Prudential’s individual life insurance business. “There arepotential components of predictive modeling that somepeople find disturbing, but we are not doing that; we’reusing traditional risk points, but in a different way,”McFarland says. “We’re looking for more economical waysto issue life insurance, which is expensive because it utilizesthis very expensive resource we call an underwriter.”McFarland refers to the emergence of vendors that collecttraditional types of lab data and perform risk analysisthrough a scoring system. But other sources of data alsoare emerging, such as those that can predict the likelihoodof diseases for individuals of a certain age. “Data that usedto be hard to get is now aggregated and available in a veryusable form,” he says.Still, McFarland says Prudential is moving cautiously withits use of big data to support underwriting. He describesthe company’s interest in big data as being like “a kid in atoy store” and says the carrier has gone beyond experimen-tation, devoting a great deal of resources in its developmentand testing of predictive modeling for underwriting.“Predictive modeling will evolve,” he says. ”We could plugit in and make it work tomorrow. The question is, can wemake it work at the right price point? It has the potential tobe the better mousetrap, but will it really catch mice? Wedon’t know.”According to McFarland, Prudential will probably performseveral more months of analysis before making a decision todeploy predictive modeling for underwriting. “Whether we’llroll it out remains to be seen,” he says. “There are severalfactors: impact on product pricing, whether or not itreduces expenses required to reach an efficiency threshold,the ability to produce the margins we decided we wantedfor that business, and whether we could gather supportfrom our reinsurance partners.“As soon as one or two companies take the leap of faithfor a given product or age group, then others will follow,because it will be necessary in order to compete.”Reprinted with permission from Insurance Technology. October 2012. nlineVisit the Analytic Insurer blog: white paper: What does big datareally mean for insurers? CEOJim GoodnightexplainsSAS®Visual Analytics“SAS Visual Analyticshelps business usersto visually explore dataon their own,” says SASCEO Jim Goodnight.“But it goes well beyondtraditional query andreporting. Runningon low-cost, industry-standard blade servers,its high-performancein-memory architecturedelivers answers inseconds or minutesinstead of hours or days.”
  • 30. James Lam is President of James Lam Associates and author of EnterpriseRisk Management: From Incentivesto Controls.COLUMNriskinsightFIVE HOUSE RULES FORMANAGING RISKY BEHAVIORHow can organizations protect themselvesfrom their own employees?If risky behavior can happen at the house of Morgan under the watchful eyes of Jamie Dimon,it can happen anywhere. It comes with the territory of employing people.A 1980s study by the security firm Pinkerton concluded that 30 percent of the population will notonly steal if an opportunity exists, but will actively create an opportunity to do so. Another 40 percentwill take the opportunity if they’re convinced they won’t get caught. Only 30 percent will not steal at all.The best defenses for preventing reckless or unethical behavior come down to five house rules:1SAFEGUARD THEFRONT DOOR.It isn’t enough to studyresumes closely; studies haveshown that more than 50 percentcontain inaccuracies. Basiccontrols include employmentand background checks, while agrowing number of companiesalso conduct behavioral andhonesty testing. As a recentexample, a simple backgroundcheck would have saved theYahoo! board the trouble ofousting Scott Thompson,the company’s fourth CEOin five years, because hefalsely claimed a computerscience degree.2SET CLEAR POLICIES.For enterprise risk man-agement, key policiesinclude a statement of riskappetite and explicit risk toler-ance levels for critical risks.Appropriate risk, compensationand financial policies will setthe incentives and boundariesfor employee behavior. Ofcourse, the right people haveto be dictating policy. JeffSkilling, as a condition of hisemployment at Enron, insistedthe company adopt mark-to-market accounting. That meantEnron was able to report $3.3billion in net income during thefive years prior to its bankruptcyin 2001, while only $114 millionin net cash was generated.Skilling created an opportunityto steal.3CREATE A RISK CULTURE.Intelligent risk taking,even if it results in failure,should be encouraged, whilethere should be zero tolerancefor unauthorized and unethicalbehavior. The “tone from thetop” is important for howemployees value honesty andintegrity. Ongoing training andcommunication, as well asinstallation of leaders with highintegrity, further reinforce a riskculture. In his congressionaltestimony regarding theColombia prostitution scandal,Secret Service Director MarkSullivan denied that the agencyhad long condoned a cultureof misconduct. Sen. SusanCollins from Maine counteredby pointing out that (1) theagents made no attempt toconceal their identity despitebringing the women to theirhotel rooms; (2) misconduct{ Even when theydon’t set out tocheat, steal or lie,people can dostupid things atthe wrong times.28sas com2.Q2013
  • 31. 2.Q2013sas com29was not limited to one group ofindividuals but rather severalsmaller groups; (3) two of theagents were leaders with morethan 20 years of service; and(4) a survey indicated that fewerthan 60 percent of the SecretService personnel said theywould report ethical misconduct.4FIX THE BROKENWINDOWS.According to RudyGiuliani’s “broken windows”theory, credited with reducingcrime in New York City, whenurban environments are wellmonitored and maintained,vandalism doesn’t escalate intomore serious crime. Keepingthis in mind, organizations mustidentify and discourage riskybehavior at every turn. Riskescalation and whistle-blowerprocesses can enhancemonitoring and transparency.One of my clients, a CEO ofan asset management firm,said to me, “I would not blinkif one of my fund managerslost $10 million due to a wrongbet, but I would fire himimmediately if he cheated$10 on his expense report.”5HAVE STRONGGUARDIANS.The board and manage-ment are in place to provideleadership and oversight.Organizations must ensure thatkey risk, compliance and auditpositions are filled with highlyqualified professionals. Thisextends to the boardroom.Critics have pointed outthat the risk committee ofJPMorgan’s board consistsof three directors with nosignificant banking or riskexperience. In contrast, theboards of the five next-largestbanks have all placed directorswith deep banking and riskexperience on their risk com-mittees. Senior risk staff mustalso have sufficient staturerelative to the line executivesthey are responsible for over-seeing. JPMorgan’s chief riskofficer, Barry Zubrow, earnedless than his peers at globalbanks and was not among thetop tier in compensation atJPMorgan.Even when they don’t set outto cheat, steal or lie, people cando stupid things at the wrongtimes. Organizations shouldminimize all these behaviorsand their effects by establishingappropriate culture and controls.Doing so ensures that riskybehavior will not bring downthe house.Originally published by Harvard BusinessReview in 2012. Copyright 2012 HarvardBusiness Review. All rights reserved.Reprinted by permission. nlineFor more risk managementtips, download white paper:The Art of Balancing Riskand you dont know can hurt youGovernance, risk and compliance (GRC) is aboutensuring that your business is in control ratherthan out of control. Its about being proactive,rather than waiting to see what happens next.What can you do?• Integrate GRC with business strategyand decision-making processes.• Automate common GRC processes.• Manage policies throughout their life cycles. For more tips, download white paper:How to Build a Successful EnterpriseRisk Management{ A 1980s study by the security firmPinkerton concluded that 30 percentof the population will not only steal ifan opportunity exists, but will activelycreate an opportunity to do so.
  • 32. 30sas com2.Q2013A taxingsituationToday, governments bear a heavyresponsibility. With agingpopulations, rising unemploy-ment (particularly among the young)and spiraling deficits, we have a toxicmix of challenges. As the size of ourproblems increases and the amount ofdata grows, delivering useful analyticson time and on budget is crucial.In the following stories, you’ll seehow three government agenciesapplied analytics to increase efficien-cies, detect and prevent fraud andimprove revenue collection.
  • 33. 2.Q2013sas com31SPEEDING TAX COLLECTION IN WISCONSINDramatic gains in efficiencies and revenue collection– all from a data warehouseLike many US states, Wisconsin is dealing with a dramatic reduction in revenueas a result of the lagging economy. To complicate matters, tax collection isslowed because the data driving tax collections is spread across multiple,isolated systems. So Wisconsin’s Department of Revenue (DOR) implementeda data warehouse solution based on SAS Business Analytics.The warehouse is populated with about 2 terabytes of data from a dozendata sources – expected to grow to 30. The state can now:• Collect more tax dollars more efficiently – an estimated 1,500 personnelhours saved and $5 million in recovered revenue in the first six months.• Select more appropriate returns to audit and prioritize those audits: $32million was quickly collected; before, it would have taken more than a year.• Process federal audit reports more efficiently and more quickly. Theseaudit reports help the DOR adjust state taxes and find potential targetsfor audits.• Respond more quickly to taxpayer inquiries, such as when a refundwill be processed.• Provide easy access to the data, so the auditor needs less frequentcontact with a taxpayer undergoing an audit.“With this data warehouse, we‘ve been able to keep our costs low and ourproductivity high. It‘s what people expect from us and what we expect fromourselves,” says Roger Ervin, Secretary of Revenue for Wisconsin. Read the full story online: other tax authorities around the world, the UK’s HM Revenue Customs(HMRC) must deal with significant tax evasion and fraud, increased criminalactivity, and the onslaught of big data.With the UK government allocating £917 million (US$1.18 billion) to supportHMRC’s efforts – anticipating a major return on investment in the form of £7billion (US$9 billion) additional tax revenues – high-performance analyticsplays a vital role.HMRC’s Connect system, of which SAS has been a part for several years,brings together numerous internal and external data sources to reveal hiddenrelationships. Able to search a billion records at the touch of a button, Connecthas revolutionized how HMRC deals with fraud detection and prevention.Bill Cockerill, Data Analyst at HMRC, says access to an extensive repertoireof analytics is required to tackle the huge variety of fraud and evasion, includingsegmentation and profiling, clustering, predictive models, anomaly detection,and more.HMRC’s analytics and the Connect system – working faster and smarter,improving detection rates and finding new opportunities for prevention anddeterrence – will do more than help the government simply avoid significantfinancial losses; it will ensure that the government receives greater tax revenues.£7 BILLION IN ADDITIONAL TAX REVENUES IN THE UKScientific fraud detection and prevention lead to big results
  • 34. 32sas com2.Q20131.Q2013sas com32Malaysia‘s Inland Revenue Board (IRB) needed to perform faster and betteranalysis on tax collections and simulate the impact on revenue of proposedtax changes. Before using SAS, it took two weeks to prepare complex reports,and there were no drill-down capabilities. The IRB also had no solution forhandling big data.With SAS, the board can handle complex reporting in as few as three days.Formerly, that work took two weeks. Simpler reports that used to take 11hours to produce are now completed in three hours. These reports help theboard get a picture of taxpayers who might be under-reporting or learn whythose who overpaid didn’t claim a refund.Accurate, timely information is critical. The board has now cleared itsbacklog, and departments such as the Ministry of Finance, the EconomicPlanning Unit and the Central Bank appreciate the accuracy of the informationIRB produces.“If it takes too long to provide information, we can‘t respond to our stake-holders’ requests. We are using SAS to calculate tax-rate impacts on rebates,reliefs and income to the government as well as the taxpayer,” says PuanMariam Bt Mohd, Director of IRB’s IT Department. Read the full story online: FASTER WITH GREATER RELIABILITY IN MALAYSIABig data analysis in hours, not weeksIn building their fraud detection methodsand models, many firms use all of theapproaches listed below. Deciding whichmethods to use often depends on the de-tails of the application and the institution.In general, many firms are moving awayfrom using business rules as the solemethod for defining alerts, preferring touse all methods as needed:• BUSINESS RULES. Individual rulesscore or define alerts based onintuition and general experience.• ANOMALY DETECTION. Alertsare defined based on events thatrepresent statistical deviations fromnormal or expected behavior.• PREDICTIVE MODELS. Full-scalestatistical models establish alertsbased on a risk score derived fromevent characteristics that are indica-tors of prior fraud incidents.• SOCIAL NETWORK ANALYSIS.Alerts are based on the level ofassociation between the eventand individuals or accounts thathave exhibited or are suspected offraudulent behavior. TOapproach fraud
  • 35. 2.Q2013sas com33Why Brazil’slargest bank is No.1in Latin AmericaBanco do Brasil responds tocustomer needs with smart,targeted products and servicesBrazil is home to the world’s sixth-largest economy – one that’sgrowing 5 percent annually and that’s second only to the US inthe Western Hemisphere.Helping to drive these economic achievements is a reinvigoratedfinancial services industry, led by Banco do Brasil – the No. 1 LatinAmerican bank in assets, and an enthusiastic proponent of SASAnalytics solutions.Banco do Brasil is tapping SAS technology to move beyond firstimpressions and more toward true customer intelligence. That meanssifting through terabytes of data to segment, profile and understandeach customer’s attributes and behaviors. Armed with thoseinsights, Banco do Brasil is forging stronger relationships withhappier customers, reducing costs and increasing profitability.
  • 36. 34sas com2.Q2013“ We wanted fact-basedcampaigns based on dataand analytic insights intoour customers. And weneeded to identify thesecorrelations and propensitiesvery quickly. That’s whywe chose SAS ...”Luis Lessa, Executive ManagerBanco do BrasilRelevant cross-sellingIn 2009, Banco do Brasil adopted a new strategic focus to transformits retail banking operations with new programs, initiatives, pro-cesses, channels and models. The ultimate goal: clarify andstrengthen relationships with each of its 55 million customers.According to Luis Lessa, Executive Manager, previously manualprocesses for engaging with retail customers meant that branchmanagers were responsible for creating bundled products andservices and devising up-selling and cross-selling strategies. Thesetime-intensive efforts created wide variability in customer satisfac-tion and financial results for the bank.“We needed to move beyond basic hunches and intuition,”he said. “We wanted fact-based campaigns based on data andanalytic insights into our customers. And we needed to identifythese correlations and propensities very quickly. That’s why wechose SAS for our customer service platform.”Which product should a Banco do Brasil employee offer to aspecific customer? Which information is essential to identifyingthe customer and his profile? SAS helped uncover the answers tothese and other strategic questions and – through integrationwith customer relationship management (CRM) tools – presentthe best offer to the customer.SAS groups customers into segments and behavior patternsand identifies the common characteristics that point to future likelypurchases. The solution factors in preferences, speed of purchase,customer satisfaction, profitability and more. The result? As muchas 20 percent of all cross-sell/up-sell offers proactively presentedto customers through this system result in a sale.“SAS analyzes each customer and automatically presents theoptimal assortment of products and services that are most likely tomeet the customer’s needs,” said JorgeLuiz Henrique, CRM Architecture Managerfor Banco do Brasil. “That informationappears right on the branch manager’sscreen so he can immediately serve thecustomer. Our new platform is much faster,much easier and much more accurate.”Overwhelming data volumesFrom there, Banco do Brasil broadenedits use of analytics at its 5,000 branchesthroughout Brazil, tying in analytics toits sales automation and service tool
  • 37. 2.Q2013sas com35Who else usesanalytics to knowcustomers?EXPEDIA understandscustomer lifetime valuethrough relevant offers,resulting in increasedconversions.GLOBE TELECOM’Sstrong customer focushas led to impressiveresults: more than $42million in revenues bymicro-segmentingmarketing campaigns.LOTTE.COM uses SASCustomer ExperienceAnalytics to identifythe causes of onlineshopping-cart aban-donment, resulting in afirst-year sales increaseof $10 million.DBS BANK reviewedthe credit applicationprocess and identifiedwhere the processcould be optimized.“ SAS analyzes each customer and automatically presentsthe optimal assortment of products and services thatare most likely to meet the customer’s needs.”Jorge Luiz Henrique, CRM Architecture Manager, Banco do Brasilto transform every customer-service encounter into a businessopportunity. But with so many customer accounts, the datavolumes were a tremendous challenge.“We have structured and unstructured data on tens of millionsof accounts,” said Lessa, “and we needed a way to transform thisdata into actionable information and business opportunity. SASwas clearly the solution for us to make that happen.”After five months of model development and testing, Banco doBrasil piloted the integrated SAS Customer Intelligence solution atseveral branch locations in Rio de Janeiro, which validated theworkflows and gauged customer satisfaction.“We wanted to understand if the product offerings we presentedto different customer segments were meeting customer needs,”explained Henrique. “We even called a few customers afterward tosee if their reactions were positive – and all of them were happywith our performance.”As the analytic platform spreads throughout Banco do Brasil,the company now believes it is in a far better position to take fulleradvantage of opportunities that arise when customers visit bankbranches and that service will be faster and more relevant tothe customer.“Our branches can now deliver a higher level of service to ourcustomers,” said Lessa. “Our relationship with SAS is very importantto our bank. As Brazil continues to evolve and grow, we are nowready to take full advantage of our economic opportunity.” nlineLearn more about SAS Customer Analytics for white paper: Banking on
  • 38. WHO‘S AFRAID OFTHE BIG DATA WOLF?There‘s a lot more to big data than just sheervolume. Author Evan Stubbs explains.There‘s probably more people who don’t have a ”big“ dataproblem that those who do. The trap is thinking that becauseof that, everything to do with “big data” is irrelevant. Englishis a funny language – sometimes, the whole is greater than the sumof its parts.Big data is very real (anyone who argues otherwise is misinformed,delusional, or trolling for pageviews), but that doesn‘t mean the“big” part (in a literal sense) applies to everyone. Equally though,that doesn‘t mean that the ”big data“ part of big data doesn‘t.While not everyone is working with petabytes of data, everyonehas a big data problem. It‘s a relative measure; it‘s when an orga-nization‘s ability to handle, store, and analyze data falls behind itsavailable data.Evan Stubbs is the Chief Analytics Officer for SAS Australia. He isthe author of The Value of Business Analytics and Delivering BusinessAnalytics, sits on the board of the Institute of Analytical Professionalsof Australia, and is a guest lecturer at Macquarie University and theUniversity of Sydney. com2.Q2013
  • 39. 2.Q2013sas com37If you listen to the online commentary, big data‘s gettingclose to hitting saturation. It‘s not just online, either. Earlierthis week, I asked an audience of over 300 people howmany felt like they had a “big” data problem.Two people put their hands up. Two.I‘m a simple man. I know it‘s wrong to generalize, but ifthose numbers are even close to being correct, that‘dsuggest that maybe 99 percent of people feel like theydon‘t have problems with managing the ”volume“ sideof big data.Australia and New Zealand are small by global standardsbut for us and for where most organizations are, thatsounds about right.Here‘s the trap though: just because you don‘t havea ”big” data problem doesn‘t mean you don‘t have a“big data” problem or even just a “major” data problem.There’s a lot more to big data than just sheer volume.Few of us are actually using all the data that‘s availableto us. Even fewer are using all the data that they could beusing; we‘re far better at capturing information than we areat using it.Out of that same 300 people, guess how many held uptheir hand when asked whether they weren’t using all thedata they could?Every single one. To a person.The difference between what we use and what we coulduse is usually enormous. The information‘s there, it‘sjust dark, yet to be exposed. In all the noise, it‘s a pointthat’s easily missed. Just because someone doesn‘t havemassive volumes of information doesn‘t mean they aren‘tstruggling with “big data” problems. One of the mostmisleading things about “big data” is sometimes just the“big” part.Everyone, from small organizations to the largestmultinationals, is struggling with aspects of the bigdata problem even if they don‘t think of it in those terms.Even if it‘s as simple as using everything that‘s potentiallyavailable to them, most haven‘t been able to even takethat step yet. When it comes to the variety side of data,it‘s often easier to try and ignore it rather than use it.Bringing this dark data to light is sometimes the easiestway to find new patterns, new insights, and suggest newactions. Visualizing patterns and using high-performanceanalytics is often the fastest path to value: the mostinteresting discoveries and changes come not from theobvious but the different. Becoming comfortable withwhat‘s easy rather than what‘s right is tempting, comfort-ing, and in the long run, destructive.If you don’t know what‘s possible or where your priori-ties should be, maybe it‘s worth starting by having a lookthrough your dark data. nlineSee more Evan Stubbs blog more about business analytics in thiswhite paper Big Data Meets Big Data
  • 40. Turnwhattheysayintowhytheystay.Marketinganalyticsgathersdatafrommultiplecustomertouchpointsovertime–soyoucantrackwheretheycamefrom,predictwherethey’regoingandkeepthemengagedeverystepoftheway.Theresultsarehigherqualityleads,®indicatesUSAregistration.Otherbrandandproductnamesaretrademarksoftheirrespectivecompanies.©2013SASInstituteInc.Allrightsreserved.S99364US.0413