1. Nucleus Research Inc.NucleusResearch.comCorporate HeadquartersNucleus Research Inc.100 State StreetBoston, MA 02109Phone: +1 617.720.2000THE BOTTOM LINENucleus found organizations can earn an incremental ROI of 241 percentby using Big Data capabilities to examine large and complex data sets.One driver of high returns on Big Data was the ability to improve businessprocesses and decisions by increasing the types of data that can beanalyzed. Another driver of high returns was the ability to monitor thefactors that impact a company, such as customer sentiment, by scouringlarge external data sources such as social media sites.Nucleus has determined that enterprises increased their average ROI on analyticsby 241 percent when they used Big Data to become a more Analytic Enterprise. AnAnalytic Enterprise is an organization that improves its competitiveness andoperating results by continuously expanding its use of analytics (Nucleus Researchm17 The stages of an Analytic Enterprise, February 2012). There are four stagesin the evolution of an Analytic Enterprise, and Big Data plays an important role inthis evolution.0%200%400%600%800%1000%1200%1400%Automated Tactical Strategic ExtendedReturnonInvestmentExtendedStrategicTacticalAutomatedEnterprises with strategic deployments earn an average ROI of 968 percent bydeploying analytics across most of the organization and using this technology toalign daily operations with senior managements goals. Extended deploymentsachieve higher returns by tapping into what is commonly referred to as Big Data,data sources that are large, contain a broad variety of data sets, and changeRESEARCH NOTETHE BIG RETURNS FROM BIG DATAMarch 2012 M20Document

2. 2 2012 Nucleus Research, Inc. Reproduction in whole or part without written permission is prohibited.NucleusResearch.comMarch 2012 Document M20rapidly. Extended deployments also reach beyond the traditional limits of internalenterprise data to the Web, customers, vendors, and partners.BIG DATA, BIG RETURNSWhen Nucleus validated the benefits of Big Data, benefits achieved by end usersincluded: Increased productivity. A major metropolitan police department achieved an863 percent ROI when it combined its criminal records database with a nationalcrime database created by a major university. The combination of nationaltrends, local crime-related data, and predictive analytics enabled the policedepartment to allocate its law enforcement assets more effectively and reducecrime rates. Increased margins. An ROI of 942 percent was earned by a majormanufacturer which used Big Data capabilities to examine purchasing and cost-related data in all of its vendors databases, leading to vendor consolidationand reduced cost of goods sold. Increased revenues. Revenues can be increased when Big Data is used torapidly detect changes in consumers activities and preferences. For example,an organization making aggressive use of online campaigns can track clickstreams and data gathered from all customer touch points to continuouslymonitor and fine tune their programs, resulting in increased revenues. Reduced labor costs. A major resort earned an ROI of 1,822 percent when itintegrated shift scheduling processes with data from a national weatherservice, enabling managers to avoid unnecessary shift assignments andincrease staff utilization.BENEFITS OF BIG DATANucleus analysts identified four drivers of high returns on Big Data investments.First, Big Data enabled organizations to examine large volumes of structured andunstructured data, such as large data sets captured by customer loyalty programsand call centers. Second, Big Data improved decision making by rapidly deliveringdata and conclusions while the information was still valuable. Third, companiesimproved decision making by combining their own data with acquired large datasets, such as geospatial data. Finally, Big Data capabilities enable the scouring ofWeb-based data for tasks such as monitoring and detecting changes in customersentiment.Big Data enables analysis of vast data setsBy dramatically expanding the volume of data that can be examined on ananalytics deployment, Big Data capabilities enable employees to detect conditionsthat are otherwise unobservable, yet impact a large number of transactions. Forexample, an automobile manufacturer that examines parts purchases by itsservicing subsidiary can detect design flaws or quality problems before theybecome a public relations or branding crisis. Data gathered as a result of customerloyalty programs is another source of on-premise Big Data. By examining thethousands of members of a loyalty program and their purchasing habits, retailerscan improve their product offerings and price points, leading to higher revenuesand margins. With so many touch points with customers, partners, and vendors,many enterprises have Big Data sets even though they may not know it. Largetelecommunications providers can use Big Data to reduce customer churn by 3. 3 2012 Nucleus Research, Inc. Reproduction in whole or part without written permission is prohibited.NucleusResearch.comMarch 2012 Document M20continuously examining their customers interactions related to billing, purchases,and call center activity to anticipate which customers are considering switching.Big Data also improves decision making by aggregating and analyzing largevolumes of unstructured data. Although many organizations accumulate largequantities of data in the form of handwritten notes, e-mails, and voice recordings,this data is typically unavailable for analysis because most analytical tools aredesigned for highly structured data such as financial information. Big Dataanalysis tools enable organizations to aggregate this data and examine it to detectdesired operational trends or conditions. For example, an airline could examinerecordings of call-center interactions in order to identify the best practices that leadto higher customer retention rates when service levels are disrupted.Big Data accelerates decision makingBy rapidly sifting through such large volumes of information, Big Data enablesorganizations to identify problems or opportunities while something can still bedone. For example, many consumers are likely to tweet or blog about a productlong before they share their opinions with a call center representative. By usingsentiment tracking tools, organizations can get a read on customer sentiment farfaster than relying on call centers or focus groups. Customer churn prevention alsobenefits from speed. Many large telecommunications providers examine churnstatistics on a weekly or monthly basis. Although such reporting may successfullyidentify customers at risk for churn, they are likely to make that informationavailable after the customer has already switched. Big Data assets can reducechurn costs by continuously monitoring billing databases to identify at-riskcustomers and sending them offers designed to retain them.External Big Data sources make proprietary data more valuableNucleus found that organizations were able to improve decision making when BigData sets were created by combining proprietary data sets with externally availableBig Data sets, such as geospatial data or weather information. For example, a carmanufacturer could identify local customer preferences or climate-specific qualityproblems by adding geospatial data to the information gathered by onboardsensors. Lending institutions that purchase publicly available credit data andcombine it with their customer lists can improve the effectiveness of theirmarketing campaigns and improve the quality of their loan portfolios.Big Data enables Web-based sentiment monitoringThe Web was a common source of Big Data that was used to improve decisionmaking. The most common use was sentiment tracking. Nucleus found manyorganizations monitored customer sentiment by tracking the results of specific keyword searches, such as our brand, need to return phone. Tracking was alsoperformed by identifying individual types of users on social networking sites whowere capable of wielding influence on sites such as Twitter or Facebook. Bytracking such individuals, two benefits were achieved. First, those individuals wereclosely observed to detect shifts in customer sentiment. Second, by proactivelyproviding superior service to such individuals, companies could ensure that suchinfluencers had a good opinion of their company. 4. 4 2012 Nucleus Research, Inc. Reproduction in whole or part without written permission is prohibited.NucleusResearch.comMarch 2012 Document M20CONCLUSIONAlthough Big Data may seem overhyped, technology buyers should set aside theirskepticism and consider making investments that enable the analysis of large andcomplex data sets. When analytics capabilities are applied to large data sets, bethey on the enterprise, the Web, or the partner ecosystem, employees becomecapable of insights they cant make by examining traditional data sources. Thescouring of the Web for important shifts in customer sentiment, the use of acquiredcredit-ratings data for loan portfolio improvement, and the analysis of warrantydatabases for the detection of potential product failures are all examples of benefitsfrom Big Data that have significant bottom-line impact, yet are unavailable fromless mature analytics deployments.