OracleWHY AND HOW TO USE REAL-TIME DATA INTEGRATIONFOR SMARTER DECISION MAKING

SAPROAD MAP TO THE REAL-TIME BUSINESS

Objectivity PREDICTIVE ANALYTICS AND THE REAL-TIME GRAPH DATABASE

JackBe REAL-TIME ENTERPRISE: TURNING BIG DATA INTO BIG THINKING

BackOffice Associates LLC5 STEPS TO DATA READINESS FOR REAL-TIME ANALYTICS

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But beyond the promise of timelyaccess to information, no two real-timeenterprises will look the same, nor shouldthey. To that end, everyone interested inmoving to a real-time architecture needsto take the important first step of definingexactly what they mean by real-time data.“To some, real-time implies instantaneous,whereas others would define it in specificunits of measurement, such as hours ordays,” writes Stanford University’sBenjamin Tabrizi, Ph.D., in Becoming aReal-Time Enterprise: Harnessing the Powerof RTE to Maximize Competitive Advantage.“Real-time for one company or processwithin it may not be real-time for another,for the definitions of right data, rightprocesses, right people, right cost, andright time vary from company tocompany.” For his part, Tabrizi definesreal-time as “getting the right data aboutthe right processes to the right people atthe right time to create and sustaincompetitive advantage.”

Real-time access, in fact, is often whatexecutives want when they envision thebenefits of real-time data. Only some, butnot necessarily all, data conceivably needsto be part of the real-time enterprise,while the bulk of it remains in slower or

more low-latency environments, archivedor stored for historical analysis. Ultimately,it is a mix of real-time, right-time, andpreserved data that will deliver a well-rounded picture on which decision makerscan take their actions. As Tabrizi put it:“Information only needs to be as up-to-date as necessary for business processes to run optimally.”

The bottom line is that the goal of thereal-time enterprise is not simply to justdo everything faster—because it ispossible to do the wrong thing faster.For example, in a production line, if acustomer needs to make a change to anorder, the manager in charge needs to beable to look upstream and downstream inreal-time to weigh the impact of halting aproduction line. But he or she also needsto understand the customer’s lifetime

value to the organization. If an airlinecustomer misses a connecting flight, thecustomer service representative that he orshe contacts not only needs real-time dataon the customer’s agenda and upcomingflight availabilities but also information on the customer’s past relationship to the airline. He or she may be a premiumfrequent flier, for example, and thusrequire additional attention.

For this reason, “enterprise” is the keyword in real-time enterprise—the successof any effort hinges on the ability to deliverinformation from all available and relevantsources, across the entire business, to anydepartment that needs it.

Slowly, efforts to bring about real-timedata access are gaining ground. However,for many organizations, it’s still a batch-oriented world. Real-time data access isstill a distant pipe dream for at least halfof companies represented in a survey ofmore than 330 data managers andprofessionals who are subscribers toDatabase Trends and Applications. Thesurvey finds that relevant data still takes24 hours or longer to reach decisionmakers. The survey, “Moving Data:Charting the Journey From Batch toBlazing, 2012 Survey on Data

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What does it mean to be a “real-time enterprise”?

It means having a well-connected organization, free of the shackles of siloed

and slow-moving data environments. It means being able to compete in

today’s hyper-competitive economy by giving decision makers immediate

access to the right information, at the right time.

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Ultimately, it is a mix ofreal-time, right-time, and preserved data

that will deliver a well-rounded picture.

Integration Strategies,” was sponsored byAttunity and conducted by UnisphereResearch, a division of Information Today,Inc., in March 2012.

Typically, systems, applications andinterfaces have been developed andmaintained within their separatedepartments or functional areas, withlimited levels of integration. Today’s datasystems—many of which were built anddesigned for legacy systems of the pastdecade, supporting far less data—are notup to the task.

Complicating this vision is the fact thatin today’s information-rich world, there isno one single source for relevant data; thetypical enterprise today has a vast array ofdata types and formats moving through itssystems, from both internal and externalsources. Organizations are drawing datafrom a range of sources—not only fromcore enterprise applications such asfinancial, CRM, ERP, supply chain and HR—but also from unstructured sources suchas productivity applications, sensors, andsocial media postings.

The key to achieving the real-timeenterprise vision is to design a multi-faceted architecture that recognizes thatselected datasets and analysis are meant tobe speedily delivered to decision makers.Regardless of latency requirements, suchreal-time-capable architectures need to beunencumbered by today’s functional anddepartmental silos. The ability to buildsuch an architecture above and beyond the current state won’t happen overnight,of course, but requires an enterpriseretooling and intelligently tiered approachto information management, processingand storage.

Both batch and real-time processes canco-exist quite comfortably. But it’s goingto take a deep understanding of what thebusiness wants, needs, and can afford.“Deploying real-time solutions is similarto process re-engineering,” says Tabrizi.“Change management programs must bein place to ensure success. Entrenchedbusiness units and employees can beresistant to change, and appropriaterewards and training must beimplemented to encourage them tosupport the initiative. Real-time initiatives

exacerbate the already costly integrationproblem that organizations face.”

The Unisphere Research surveyconfirms that many efforts to move toreal-time enterprise are hampered byorganizational and budgetary issues. Inaddition, respondents are concerned abouttheir ability to ensure the quality of datathat is rapidly pulled from source systemsand fed into analytic systems. While “datademocracy” is seen by many as the mostsuccessful model for enhancing decisionmaking at all levels—from the front linesof customer call centers to executive suites—the Unisphere Research survey findsmost organizations are a long way offfrom this ideal. Only 12% say that mostanalytic data is available across theirenterprises. A majority, 63%, indicate thatless than 10% of their workforces havesuch direct access.

Technically, moving to real-timerequires enterprises to look at variousemerging techniques and technologies for organizing and moving their data.There are the tried-and-true traditionalapproaches, especially extract, transform,and load (ETL) processes, as well ascountless vendor-supplied and custom-written scripts and connectors. While data warehouses or analytics andreporting solutions will remain animportant component of data analysis,these platforms are but one aspect of thereal-time enterprise.

Real-time enterprises will increasinglylook to other modes of data managementand delivery, including data appliances,data virtualization and cloud to move data more efficiently to decision makersrequesting it. Traditional relationaldatabases and data warehouseenvironments also are being enhancedwith operational data stores that serve as

caches, and can capture streaminganalytics, for more timely decision-making. For organizations with manydifferent types of systems, datavirtualization provides for a data serviceslayer that can be accessed by any type of device or application. For sites withlimited scalability, cloud-based services arecapable of supporting resource-intensiveworkloads as well as large volumes ofdata. For companies that rely mainly on relational databases, a new breed ofopen source and NoSQL databases andframeworks enables the capture andconversion of big data files into digestiblenuggets of information. While the mostpopular open source framework, ApacheHadoop, is inherently a batch-orientedsystem, new tools on the market offer real-time Hadoop analysis.

For decisions requiring faster analyticsrunning against their data stores, in-memorydatabases offer blazing processing within a single system. For administrators stillconcerned about the slow pace ofprocessing, massively parallel processingoffers a way to distribute workloads acrossavailable nodes within an enterprisesystem. Change data capture techniquesdramatically lowers the amount of databeing sent back and forth between storageand applications.

Ultimately, real-time goals must be managed beyond the technologydepartment, as it is a business imperativemore than a technology initiative. In hisbook, Tabrizi points to key questions that need to be asked as the businesscontemplates real-time capabilities: “Whatcritical information does my organizationrequire to compete competitively? What isthe ROI of different technology solutions?”

Organizations are increasinglyrecognizing that their competitive futureslie in their ability to deliver timely analysisbased on a variety of data forms beyondwhat’s being held in their relationaldatabases, particularly unstructured data.A real-time enterprise architecture needsto be flexible and accommodating todelivery of this big data, made possiblethrough emerging approaches such ascloud, virtualization, open source, NoSQLdatabases, and data appliances. ■

Moving to real-timerequires enterprises

to look at various emerging techniqes and technologies.

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Every organization wants to be smarterabout how it does business. Particularlywhen large volumes of data are coming atus at a faster rate, we want to find ways toleverage this big data for smarter decisionmaking at all levels of the organization.While it is certainly smart to make data-driven strategic decisions, to see a majordifference you need to improve how yourun the business on a daily basis. Withmany “small” decisions, front-lineemployees impact how a company deliverscustomer experience and how it uses itsscarce resources. These decisions collectivelyaffect the bottom line, competitive strength,and the company’s growth prospects.

Decision-making support foroperational employees requires using lowlatency data and fast analysis. We see thatdata is generated at a much faster rate due to the increased number of devices,and the increased connectivity andcommunication between these devices,also called the “Internet of Things.” Mostof this data, though, loses its inherent valuevery fast by becoming less relevant andless effective in influencing operationaldecisions—unless it is analyzed andconsumed almost immediately. To extractvalue from such perishable data in adynamically changing environment,organizations need to capture, analyze and act upon them with a near real-timespeed. A new set of fast data technologiesnow address this exact problem. Real-timedata integration is at the center of fastdata solutions and provides one of the key technology components for enablingsmarter decision making in a real-timeenterprise: It enables capturing events asthey happen to enable accurate analysiswith up-to-date information.

A decade ago, real-time data integrationtechnologies were seen as an expensive

“luxury” endeavor, but today the picture is different. More industry leaders arerealizing the value of real-time dataintegration and fast analysis, anddemonstrate it in the way they dobusiness. These companies are usingadvanced technologies to change how they make decisions and run operationson a daily basis.

There are many different areas real-time business insight can improvebusiness results. Let me review the topthree categories with a few examples here.

1) Improved Customer Experience By using up-to-the-minute data,

businesses gain a complete, up-to-dateview of the customer and can find ways to address the customer’s needs. Forexample, when they have information on a customer’s most recent activity on thecompany’s website or the latest status of aservice ticket submitted by a customer, theservice team can handle the customer’srequest fast and effectively. In addition todelivering great service when customerscontact the company, innovative companiesfocus on preventing customer issues bypredicting and proactively coming up withsolutions before customers even notice the issue or contact the company. Forexample, some telecommunicationproviders detect network issues in realtime, and if an outage is inevitable,contact customers right away and informthem about when it will be fixed. Asconsumers, we know fast issue resolutioncan build brand loyalty.

Benefits of such customer service arenot limited to loyalty and associatedrevenue growth. You can save costs too.When companies share information withcustomers on a timely basis, they avoidhigh call volumes coming from

customers. By updating customer portalswith real-time data from back-endsystems, customers can access up-to-dateinformation on the web. This, in return,decreases the number of calls customersmake to access, confirm, or correctinformation about their account,resulting in operational cost savings for the company.

2) Higher Operational Quality and EfficienciesDay-to-day business operations can

be optimized with the support of timelybusiness analytics. Optimizations are moreaccurate and effective when there is real-time visibility into operational data. Forexample, for the marketing function, thesooner the marketing team sees the resultsof an ongoing campaign, the sooner it canadjust its promotion and increase thereturn on the campaign effort. Thiscapability enables to optimize marketingbudget and achieve campaign targetsfaster. In the retail industry, leadingcompanies use real-time data integrationto gather store data from regionallocations back to headquarters. They usethe data for timely comparisons of stores’promotion results and to improve the waythey execute promotions in locations withpoor results.

For service-based businesses,optimizing human resources can not onlyimprove their customers’ experience butalso minimize their own labor cost. Forexample, in field service operationsemployees’ schedules can easily changethroughout the day due to externalcircumstances, such as traffic, or customerdemand. Real-time data integration allowsthe resource planning applications tofactor in the dynamically changing eventsto provide the most efficient schedule

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Why and How to Use Real-Time Data

Integration for SmarterDecision Making

and workforce distribution to service the customers.

Another tangible example ofimproving operational quality is in frauddetection. Timely data enables betterresults in fraud detection. The earlier arisky transaction is identified, the earlier it can be stopped, thus preventing furtherloss. For this reason, many leadingorganizations in financial services andtelecommunication industries maximizereturn on investment for their frauddetection systems by bringing the datafrom transactional systems at the lowestlatency possible.

There are many other operationalimprovements beyond the examples I gave above. Especially in supply chainmanagement, real-time visibility intosupply and demand allows betterpredictions and brings significantoptimization opportunities. Betterassessment of demand drives inventorycosts down and helps avoid out-of-stockrelated revenue losses.

3) Product/Service DevelopmentThe ability to process highly-dynamic

data efficiently provides organizationswith broader insight into customers andallows companies to offer differentiatedservices or products that were notpossible before. As mentioned earlier,some of the dynamically changing datahas perishable value, typically due tolosing relevance to end users. A goodexample of this is location information.Mobile phone subscribers’ locationinformation has very little value the next day or even an hour later since the mobile user will probably move toanother location within a short period.But if the company processes locationinformation in real-time and enableslocation-based offers in near-real-time,the organization can gain value out ofthis location information. These types of new solutions and products not onlygenerate new revenue streams andimprove customers’ experiences, but also enable the company to constantlytransform its business with continuousinnovation as market dynamics andconsumer demands change.

Now that we discussed why real-timedata integration should be used forsmarter decision making, let me coverhow to choose the right real-time dataintegration technology. Many real-timedata integration solution offerings maysound similar on the surface. To make thebest choice, there are five importantaspects that should be closely evaluated:1. Performance: When talking about big

data, the ability to handle largevolumes is a must. The optimal dataintegration solution is capable ofkeeping up with the volume of changeddata at the determined latency—whether sub-seconds or minutes. Thiscould mean moving thousands oftransaction operations per second.Spikes during peak times should behandled with little to no additionallatency. As mentioned earlier, if thesolution introduces major latency, thepotential value of the information candiminish significantly.

2. Impact: Capturing changed data inreal-time and feeding it to the analyticalenvironment throughout the day shouldnot impact the performance of sourcesystems or the network. Invasive datacapture methods, such as addingtimestamps and scanning tables,or database triggers, can introduceoverhead and impact source systemperformance. Technologies that offerreading transaction logs typically resultin minimal impact on the sourcesystems. In addition, look fortechnologies that move only thenecessary data (for example, onlycommitted transactions) and offerfeatures such as compression to furtherminimize bandwidth requirements.

3. Flexibility: Beyond support for bigdata, and heterogeneous source andtarget systems, the solution should be easy to implement, manage, be ableto scale as needs change, and shouldsupport myriad topologies. Forexample, adding new data sourcesand/or targets should be straightforwardand not require major overhauls.Certification for major applications will also help with deployment timeand minimize risks.

4. Data Integrity and Recoverability:When analytical solutions supportoperational decision making, theiravailability, performance, and reliabilitybecomes as critical as we demand fortransactional systems. The real-timedata integration solution should beresilient and easily recover fromunexpected interruptions, such ashardware failures, network issues, andhuman errors, without losing orcorrupting transactional data. Inaddition, managing referential integritywhile moving high-volumes of data inreal-time can be challenging for somesolutions. During the evaluation periodtest referential integrity andrecoverability given several failurescenarios.

5. Integrated and Complete: For fast time to value, look for solutions thatcombine the core elements of dataintegration—real-time and bulk-datamovement, data synchronization, dataquality, and data services—to ensureyour investment serves the wholeenterprise with different initiatives,such as cloud integration, MDM, etc.End-to-end integrated solutions enablefast results as they are designed to worktogether and remove the guess-workfrom your project.

The Oracle Data Integration productfamily offers a complete suite of productsfor real-time and bulk data movement,transformation, data quality, data services,bidirectional data synchronization, andcloud integration. In this product family,Oracle GoldenGate delivers low-impactreal-time data integration solutions andenables organizations to leverage timelydata in decision-making across all levels.Oracle Data Integration products are corecomponent of Oracle’s fast data solutions,which help uncover the economic value of high-velocity and high-volume data byenabling faster insights and faster action.Oracle GoldenGate is integrated withOracle Data Integrator, which providesbulk data movement and transformation,data services, and Hadoop-basedtransformation and loading capabilities.Oracle Data Integrator also integrates

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with the other key offering, OracleEnterprise Data Quality. Oracle’s dataintegration products are designed towork with Oracle Exadata in additionto supporting heterogeneous databases

and non-database sources. OracleGoldenGate’s log-based real-time change

data capture feature results in negligibleimpact on source systems. The productoffers robust features to ensure reliability,automated recovery, and transactionalintegrity.

Many of Oracle Data Integrationcustomers, such as MegaFon featured

in this section, have transformed the way they run their business with smarterdecision-making by empowering theiremployees with accurate, actionableinsight. ■

ORACLE www.oracle.com

MegaFon Centralizes 200 Billion Real-Time, MobileCommunications Billing Transactions to Analyze Securityand Revenue, and to Protect Against Fraud

Founded in May 2002, MegaFon is the only Russiantelecommunications provider with a network that spans all of Russia, as well as the Republics of Abkhazia, South Ossetia,and Tajikistan. The company was the first in the country tolaunch 3G commercial operations. It is now Russia’s topprovider of mobile internet solutions, and is ranked secondhighest for the number of active communications subscribers—with 62.8 million at the end of 2011, representing a 9.6%increase from the previous year.

CHALLENGESBuilding on its growth, MegaFon recently acquired

Synterra, a Russian mobile carrier. Following the acquisition,the acquired company’s billing information was in eightseparate regional billing systems across Russia. To sustaingrowth in the multiple fixed and wireless segments, MegaFonneeded a data-centric IT architecture for multiple disparatedatabases, to ensure accurate, trusted, and timely data for allcorporate departments. For example, MegaFon wanted theability to create smart marketing campaigns built on mobilesubscriber profile data with real-time response analysis, so itcould facilitate growth by maximizing conversion rates andaverage revenue per user. In addition, they wanted to improvemobile fraud detection and increase mobile securitycapabilities by distributing real-time customer data tobusiness-critical subsystems for analysis.

SOLUTIONMegaFon deployed Oracle Golden Gate 11g to extract

billions of monthly transactions from eight regional billingsystems. The data was integrated and centralized onto OracleDatabase 11g, Enterprise Edition, and distributed to business-critical subsystems for revenue, fraud, and security analysis.MegaFon chose Oracle solutions because of performance, easeof implementation and use, scalability and agile management.MegaFon easily completed the implementation on budget,with minimal systems performance optimization required.

BUSINESS BENEFITSThe solution enabled MegaFon to create a 360-degree

view of the mobile subscriber base by consolidatinginformation from an existing Oracle’s Siebel CustomerRelationship Management application and disparate billingdatabases, which improved insight into customers.Deploying Oracle GoldenGate 11g allowed Megafon toimplement a data-centric IT architecture and provided moreaccurate, trusted, and timely data for sales, marketing,customer care, and other corporate departments. Now,MegaFon’s multiple offices and data centers across Russiacan respond quickly to events—especially potential mobilesecurity and fraud issues—thanks to more centralizedbusiness information and streamlined access to real-timereporting.

The solution also allowed Megafon to create sophisticated,targeted marketing campaigns based on enhanced mobilesubscriber information, enabling it to maximize marketpenetration and facilitate continued growth. With real-timeaccess to key performance indicators, field offices and datacenters can better meet strategic goals and increase growth.In addition, the solution enables more business-specificanalysis of billing data—such as revenue assurance, customeranalytics, and fraud detection—to accommodate thecompany’s revenue growth, customer insight, and securityrequirements.

Additionally, Oracle’s solution delivered IT benefits.Offloading tasks—such as operational reporting andanalytics—from billing and operations support systems to the new, centralized database optimized IT resource use. Thesolution suite also lowers total cost of ownership, as a singleMegaFon engineer maintains the entire Oracle GoldenGate11g infrastructure.

CONCLUSIONAs a result of the real-time data integration implementation,

MegaFon achieved more sophisticated, business-specificanalysis for billing and customer data, to improve businessdecisions, facilitate more targeted customer marketing, andprovide for ongoing growth.

Customer Success Example By Irem Radzik, Director of Product Marketing- Data Integration, Oracle

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What would your business look like if processes were frictionless? What couldyou do differently with the power ofin-the-moment insight? How could youbetter use the time and resources you nowspend on tracking down information andanalyzing data? SAP believes that there is a fundamentally better way for ITorganizations to manage the data thatdrives your business. To become a trulyreal-time business, you need a unifiedframework capable of managing yourapplications and business data seamlesslyand more efficiently.

SAP Real-Time Data Platform enablesorganizations to harness the value ofmassive data for actionable insight in themoment—so you can run your businesssmarter, faster, and more efficiently.

With SAP Real-Time Data Platform, allyour applications—and all the associateddata that drives them—are unified withina common framework for processingtransactions, analyzing data, anddelivering actionable information toknowledge workers around the enterprisewhen and where they need it. This meansthat you can now manage and move datathroughout the enterprise, regardless ofsource or processing technology, andbreak down application and data silos toenable a 720-degree view—360 degrees forthe enterprise and 360 degrees for yourenterprise’s customers.

RADICAL SIMPLIFICATIONSAP Real-Time Data Platform can

dissolve layers of complexity and addressthe challenges of data fragmentation inthe enterprise.

The promise of SAP Real-Time DataPlatform is to eliminate some of theconstraints to performance andfunctionality that have resulted in thisduplication of data and the proliferationof data management techniques. Bycombining powerful, in-memory databasetechnology with other purpose-specificdatabases in one unified environment,SAP Real-Time Data Platform can delivera highly optimized technology platformfor a wide range of business applicationsand analytics.

SAP Real-Time Data Platform providesyou with an adaptable, extensible, andopen enterprise software platform thatdelivers current and comprehensiveinformation at any time for any device—and with low TCO.

Through a unified orchestration ofoperational data and other valuable data(such as unstructured data, real-timeexternal data feeds, and so on), SAP Real-Time Data Platform frees you from trade-offs in choosing the best data managementplatform for a particular purpose.

SAP Real-Time Data Platformalso delivers a common modeling

environment, giving your IT

organization an enterprise-wide view ofall information, regardless of where thedata is physically stored. Pictorial viewsof the information architecture make iteasier for both business users and IT todefine the use of information acrossbusiness processes by using a commonmetadata language.

And common informationmanagement functions, such as dataintegration and data quality management,can be performed before data is movedinto data stores or embedded in memoryfor faster processing.

Finally, the unified architecture ofSAP Real-Time Data Platform includes acommon administration and monitoringenvironment, allowing companies tosimplify IT staffing, leveraging skill setsacross the whole IT landscape for easierday-to-day operations and rapid iterationof enterprise application development.

INNOVATION WITHOUT DISRUPTIONInnovation is the fuel that drives

growth and competitive differentiation formost businesses, and it can occur throughan evolution of continuous small changesor in dramatic leaps. Yet all forms ofinnovation share one thing in common:change.

Change can be disruptive. Traditional,large-scale upgrades to technologiesand business processes can impact the

Road Map to the Real-Time Business

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entire enterprise and hamper productivity.With SAP Real-Time Data Platform, SAPis delivering a solution that is bothevolutionary and revolutionary.

SAP Real-Time Data Platform is notsimply a collection of disparate databasesbut a foundation for orchestratingbusiness functions across the enterprisethrough reliable data movement, datasecurity, and a common environment formodeling and administration. It supportsa graceful evolutionary path that takesinto account and embraces existing ITlandscapes and practices. Deep integrationenables continuity across the various datamanagement styles spanning complexevent processing, online transactionprocessing, analytics, and elastic data-caching strategies.

Based on your business requirements,you may see the advantage of a disruptivenew technology, such as in-memorycomputing, that can deliver game-changingadvantages through instantaneous accessand analysis of vast volumes of businessdata. This level of innovation candramatically accelerate business decisionmaking and enable the strategic businessmoves that justify the investments in newtechnologies, practices, and skill sets.

Or you may want to make gradualchanges aimed at modernizing outdatedtechnology infrastructures, to takeadvantage of performance and functionalityimprovements, or to lower costs.

The focus of SAP Real-Time DataPlatform is to accommodate a variety of business scenarios with best-in-classoptions, and to reduce the complexity ofmanaging this heterogeneity through ahomogeneous experience—for developers,administrators, and users alike.

FLEXIBILITY TO ADAPT TO CURRENTAND FUTURE BUSINESS DEMANDS

More and more data is being produced,and more of it is relevant to yourorganization. Data is now distributed across

multiple systems—and sometimes acrossmultiple organizations—resulting in a need for new systems to help consolidate it. Leveraging new sources of data, from logs and machine-generated data tounstructured data such as e-mail andtweets, is placing additional challenges ontraditional IT infrastructures designed foran earlier era. Meanwhile, users bring aninsatiable appetite for speed and ease of useto the workplace, based on their experienceswith sophisticated new consumertechnologies in their personal lives.

These rapidly changing dynamics areforcing a renewal of the technologies thatsupport businesses today. Changes to thehardware stack deliver more power in asmaller footprint. Software advances giverise to new systems that can nativelyleverage the power of this new hardware.New data platforms and processingmodels are emerging to deal withchallenges ranging from the streaming of data in real time to demands for verylow latency and extreme scalability.

SAP Real-Time Data Platform is aimed at solving these types of challengesthrough an innovative combination ofdata management technologies andprocessing models, giving IT staff theflexibility to support day-to-dayoperations while building out newstrategic capabilities.

GROUND BREAKING NEW CAPABILITIESCombining processing models within

a unified and orchestrated framework can result in some groundbreaking newcapabilities, at a lower cost and withgreater efficiencies. Consider the ways that SAP customers are deploying this new approach in the following examples.

REAL-TIME OPERATIONAL REPORTINGOF MISSION CRITICAL APPLICATIONS

The SAP HANA® platform helps blurthe classic boundary between the“transactional world” of OLTP and the

“analytical world” of data mining andpredictive analytics. It can handle OLTPand analytic loads in a single in-memorydata management system. SAP HANAdramatically reduces the complexity ofprocessing large transaction loads while at the same time makes the transactionaldata immediately available for analysis.

An in-memory approach to datamanagement is a fundamental innovationthat can radically simplify IT landscapes.An in-memory database managementsystem has been designed from theground up to efficiently manage all data in physical memory for highperformance. Unlike other accelerationtechniques that exploit a tiered-memorystrategy to improve performance, in-memory data management that is at theheart of SAP Real-Time Data Platformgives users flexible, ad hoc data modelingfunctionality by providing non-materialized views directly on detailedinformation. This liberates users from the wait times associated with aggregatingdata from multiple data stores, datamodel changes, and databaseadministration tasks. With in-memorydata management systems, the tasksassociated with overcoming I/Oconstraints are no longer necessary—eliminating much of the tuning andoptimization efforts that can consumesignificant IT resources.

HANDLE EXTREME TRANSACTIONALBUSINESS VOLUMES MOREAFFORDABLY

Running SAP Business Suiteapplications on the high-performance SAP Sybase® Adaptive Server® Enterprise(SAP Sybase ASE) helps companies handledata growth and extreme transactionalvolumes more reliably and affordably.SAP Sybase ASE is proven in the toughestmission-critical environments, includingfinancial services, telecommunications,and healthcare.

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SAP Sybase ASE can help reduceoperating costs through more efficient useof storage, server, and staffing resources.In addition, companies can have completeconfidence that they will have highlyavailable access to mission-critical data in the event of a disaster. SAP SybaseReplication Server offers low latency ofreal-time data loading while maintainingtransactional integrity, enablingcompanies to ensure compliance withinternal and external regulations.

REAL-TIME ANALYTICS OF BIG DATASAP HANA and SAP Sybase IQ

software both offer powerful columnstores for analytics. When working inconcert with SAP HANA, SAP SybaseIQ—our popular column-based datamanagement system—provides a highlycost-effective, near-line storage solutionfor data that is not as time sensitive anddoes not need to be stored in cache.

SAP HANA, SAP Sybase IQ, and SAPData Services software can work togetherto help organizations gain valuable insightfrom unstructured data stored in Hadoop.SAP Data Services delivers a Hadoopconnector that provides high-performancereading from and loading into Hadoop.SAP Data Services identifies, extracts,structures and transforms the meaningfulinformation from Hadoop and Hive andprovisions the data to SAP HANA, SAPSybase IQ, or other data stores for deeperanalysis. This allows for reliable, efficient,and optimized real-time analysis across allenterprise information assets—structuredor unstructured.

REAL-TIME, EVENT-DRIVEN ANALYTICSA continuous flow of new information

streams into businesses each day aboutmarkets, customers, partners, and more.SAP Sybase Event Stream Processor (SAPSybase ESP) enables companies to quicklyanalyze and act on these events as theyhappen. The software enables you to

combine information from differentsources, filter out what is irrelevant, andexamine events in the context of otherevents to determine what is important—and do so at very high volumes and inreal time.

SAP Sybase ESP provides high-leveltools to define how events are processedand analyzed, enabling companies toreduce development time and effort by up to 75% compared to buildingapplications from scratch, based on our customers’ experience.

COMMON MODELING, METADATAMANAGEMENT, AND DATA QUALITY

SAP Real-Time Data Platform can helporganizations trust the quality of theirdata. With its rich offering of informationmanagement solutions, it helps you tounderstand, monitor, and improve thequality of data used across the enterprise.

SAP Sybase PowerDesigner® softwarecan help your organization eliminateinformation silos with a powerfulmodeling tool for data, information,and enterprise architectures. It provides a pictorial representation of complexenvironments, helping to simplifycommunication between business and IT as they manage the relationshipsbetween business processes, data,metadata, and data stores.

SAP Information Steward softwareempowers business users to usedashboards to measure and monitor data quality, so they can drill down tounderstand the lineage and impact of dataacross systems. SAP Data Services softwarehelps improve data quality by parsing,standardizing, cleansing, matching, anddeduplicating data anywhere in theenterprise. You can also use it to enrichdata with geospatial and referenceinformation.

When used together, theseinformation management solutionsenable organizations to model, monitor,

and improve data quality on today’ssystems and to plan appropriate changesfor tomorrow.

MOVING FORWARDTo meet your current and future

business needs, SAP has developed andwill continue to enhance an adaptable,extensible, and open enterprise platformand infrastructure that helps delivercurrent and comprehensive informationat any time for any device—and with alow TCO.

SAP has a proven track record oflistening to our customers, understandingtheir needs, and delivering innovativesolutions that offer outstandingfunctionality and unmatched businessvalue—solutions that can help you stayahead of the competition. In addition, wehelp you leverage your investment in SAPsolutions by providing the right strategiesand services, so you can transform yourbusiness into a real-time business. Look to SAP as your partner when it comes toleveraging groundbreaking innovations forthe benefit of your business. ■

FIND OUT MORELearn more about SAP Real-Time DataPlatform by visiting:www.sap.com/realtime_data

It’s all about immediate gratificationthese days … What do we want? Morerevenues and value from our data! Whendo we want it? Now! There are a few keychallenges to maximizing the efficiencyand accuracy of predictive analyticsapplications. The sheer volume andcomplexity of Big Data has significantlychanged in the last decade. In order tounderstand your data today you must first take into account the 5 V’s—Volume,Variety, Value, Velocity and Veracity.Secondly, you need to take intoconsideration the challenge of processingyour distributed data quickly to ensurereal-time decision support and discovery,an increasingly important requirement formany business focused applications. WithBig Data being distributed and even moreintricately interconnected, it’s difficult for existing solutions to support real-time relationship analytics withoutincorporating new tools to help speed up the process of ingesting, navigating and querying data.

The graph database is helping to extendthe capabilities of existing analyticssolutions. As data increasingly becomes aweb of inter-connections and relationshipsacross multiple sources, datasets lookmore like images of graphs with complex,multiple interactions linking across severalpoints/locations. Unlike existing databasetechnologies that may only be able tohandle a few degrees of separationbetween data, graph databases are built to easily navigate, query and discoverinformation in graph-like formations to“n” degrees of connections in real-time.

The power of the graph database is in its ability to analyze the ever-growingcomplexity of your data over time. Thegraph allows you to extract more valuefrom your existing data by analyzingmultiple points of connections/relationships/interactivity and also

incorporates new data as it is received.If you are looking for standarddemographics and analytics, many existingsolutions are able to solve those problemstoday. The problems that require deepanalytics involving multiple data pointsand connections are the sweet spot ofgraph databases. For example, a graphdatabase would work best to help identifyseemingly random patterns within adataset to locate key influencers, quickestpath-to-purchase, network securitymanagement risks or even enable real-time geo-location-based advertisingopportunities within a network.

There currently are some well-knownsolutions that are available to help manageBig Data, such as Hadoop and Cassandra.These solutions are great for maximizingyour existing bandwidth and performancewhen it comes to ingesting and managingBig Data, but they do not offer real-timequery performance. By combining yourexisting architecture with complementarytechnologies, such as Hadoop andCassandra, with a distributed graphdatabase, like InfiniteGraph, you cancreate a powerful, scalable, polyglotsolution that is able to uniquely tackleyour real-time, Big Data requirements.

Utilizing a real-time graph databasefor predictive analytics enablescompanies to exploit the 5 V’s—Volume,Variety, Value, Velocity and Veracity—for critical decision support. Today, thegraph database is helping in multiplefields such as law enforcement,healthcare, and telecommunications.Leveraging Big Data in the enterprisetoday requires the right tools for the right job and the graph database isquickly becoming a tool of choice. ■

OBJECTIVITYInterested in learning more aboutdistributed graph database technology?Visit www.objectivity.com/downloads for afree trial download of InfiniteGraph withaccess to support, samples and wikidocumentation to help you get started.

Predictive Analytics and the Real-time

Graph Database

THOUGHT LEADERSHIP SERIES | MAY 2013 10Sponsored Content

‘…problems that

require deep analytics

involving multiple data

points and connections

are the sweet spot of

graph databases.’

THOUGHT LEADERSHIP SERIES | MAY 2013 11Sponsored Content

I came across a book by visualizationguru Professor Ben Shneiderman,Readings in Information Visualization:Using Vision to Think, and it struck anerve. The book addresses how to usethings like space, interaction and focus tohelp people make sense of what they see.

It’s not the book’s visual concepts thatintrigue me; it’s the “visual thinking” part.

Every day, you process vast amounts of information for split-second decisions in ways that don’t even seem like activethinking. Consider the real-time visualthinking to drive a car or play sports. Youdon’t just look at information; you use visualcues to think, make decisions and react.

REAL-TIME ENTERPRISE: AUTOMATICRESPONSES, IMMEDIATE DECISIONS

As your driving or playing improves,you’re mostly on autopilot because patternsbecome ingrained. But when somethingunusual happens, like an accident or traffic,you activate your thinking about yoursurroundings to devise alternatives. Thesooner you have the new information, thebetter you can react.

Business decisions are no different:The faster you get actionable information,the faster you can “think” about it andtake action.

While most businesses may neverrequire the split-second decision-makingof an ER doctor or fighter pilot, access to real-time data to make time-sensitivedecisions is no longer nice to have, it’s a necessity. “Real time” means “as datachanges,” relevant to the decision window.Data may change every few seconds, minutesor even hours, but to the business personresponsible for operational-type decisions,this is real-time.

Real-Time Enterprise providesimmediate value from real-time data whencombined with the rest of the enterprisedata to determine patterns with positivebusiness impact. Identifying behaviorpatterns requires analyzing data-in-motion, and the best solution is datavisualizations which provide, especially

for Big Data, a rich, interactive dimensionto help you think, not just look.

ENTER BIG DATA AND IN-MEMORYBig Data offers new potential to tackle

problems previously unsolvable because ofcost and complexity. It’s no longernecessary to evaluate samples of Big Data.With technology advances, we can processthe entire data population to look for newsolutions—and new questions to ask.

One example: New technology allowedthe Large Hadron Collider to smashenough particles to create enoughcollisions to explore unanswered questionsof particle physics. (http://en.wikipedia.org/wiki/Large_Hadron_Collider) But it’sthe amount of data generated from thosecollisions that made it possible to confirmHiggs boson really exists. The Higgsparticle would still be a mystery todaywithout enough data for meaningfulresults and a way to process that data tomake the discovery.

Throwing massive computing power at Big Data doesn’t equal interacting withreal-time data. Real-time data interactionrequires in-memory analysis and providingbusiness users an immediate means toturn data to decisions. Big Data requiresreal-time visual analytics.

REAL-TIME VISUAL ANALYTICS:THINKING, NOT LOOKING

The new generation of data-dependentorganizations now face the design tenetthat the more data you have, the moreyou’ll need to solve your everyday problems,and the more analytics you’ll require todiscover important relationships. Real-Time Enterprises need to be Big Data- and visual analytics-driven.

Real-time visual analytics provideinteractive dashboards with charts, graphsand maps like the old-school BI tools youknow. But real-time visual analytics alsoprovide means for business users to createtheir own on-the-fly dashboards, withminimal IT help. These platforms, basedon powerful visualization frameworks, use

in-memory technology to analyze Big Datamoving through the system. In-memoryallows real-time data to be mashed withhistorical transactions and other enterprisesystems into one big data pool. In-memoryalso bypasses ETL processes that can’tprovide current real-time data.

One industry sector with all theingredients for Real-Time Enterprise—urgent, real-time decisions, Big Data andthe need for real-time visual analytics—is Machine-to-Machine (M2M).Machines with sensors—from jet enginesto your toaster—communicate vianetwork with other machines thatmonitor them. M2M’s connections,referred to as “The Internet of Things”or “Industrial Internet,” generate real-timeBig Data requiring constant monitoringfor time-sensitive decisions.

GE is one M2M company taking Real-Time Enterprise seriously, investing over$1 billion toward a 1% efficiency gain.Throw-away goal? A 1% efficiency gainfor its airplane engines could result in $30 billion in fuel savings alone forcommercial airlines.

Real-Time Enterprises understandcomplex business operations aren’t solelydependent on Big Data. They require real-time access to data as it changes,other contextually-important enterprisedata, and the means for business users tointerpret the information for immediatedecision-making. Real-Time Enterprise isdriving organizations to re-architect theirentire operations around real-time visualanalytics, providing the means to turn BigData into Big Thinking for sustainablecompetitive advantage. ■

JACKBE http://jackbe.com/

R

Real-Time Enterprise: TurningBig Data into Big Thinking

By John Crupi, CTO, JackBe

THOUGHT LEADERSHIP SERIES | MAY 2013 12Sponsored Content

When performing a large-scale datagovernance implementation or an ERPmigration to a real-time data platformsuch as SAP® HANA, there are manycritical preparations that need to becompleted first—most importantly, you’llneed to establish high-quality, Business-Ready Data™. Without clean, quality datain the system, the real-time capabilitiesavailable through SAP HANA will notdeliver the correct results. Rather, it willsimply end up delivering the wronginformation faster.

BackOffice Associates® has 17 years ofexperience in delivering highly successfulERP and information governance go-livesbased on these five major tenets—whichalso apply to preparing data for a real-time analytics platform.

1) Establish key metrics to test datarelevance and readinessThis is where organizations run into

trouble. Preparing data requires theinvolvement of both business users and IT.Business users typically are not trained inthe complexities of the IT tools that areessential in this process, while IT focuseson the technical aspects of the migrationwithout considering how businessprocesses will be affected by it. Both of these users need to be included indetermining which data is relevant.

2) Implement a data quality strategy.By using tools offered for data

management such as SAP EnterpriseInformation Management (EIM) tools,organizations can begin to create, cleanseand manage their data across businessfunctions. However, even with SAP’srobust solution set, it can be challengingto prepare the data for a large-scaleimplementation. Migrating low qualitydata can cause many costly and time-prohibitive problems during the SAPHANA implementation and after go-live.BackOffice Associates offers solutionsthat are uniquely focused to help businessusers and data stewards manipulate data—helping to enforce correct data entryand data policies. These solutions can

help eliminate gaps between businessusers and IT.

3) Manage system mappingOften business users default to a

Microsoft® Excel spreadsheet whengathering source and target mappingrequirements. These requirements are then used to build logic into the extract,transform and load (ETL) tools thatperform the data translations in businesssystems such as ERP, CRM, HCM andSCM prior to migration. Documentingand viewing mapping task status is critical—however, simple spreadsheets are not up to the task of providing this kind ofcomplex activity. BackOffice Associatesprovides a simple web application thatefficiently completes mapping tasks andprovides full visibility into completed andpending tasks. In addition, if a mapping isaltered, the solution automatically updatesthe documentation and execution logic.

4) Execute data constructionIn many instances, source data from

legacy systems is unable to run in a newersystem due to technical differencesbetween the systems. That’s when userswill turn again to Microsoft Excel for theconstruction of this missing data. Thisbecomes problematic when hundreds of business users are involved in datacreation, leading to an unlimited numberof spreadsheets and no visibility across the process. BackOffice Associatesprovides a simple web solution to collectmissing data and view which tasks arerequired, which have been completed and which still remain.

The ability to have a single version of all data being collected ensures qualitycontrol. Without high-quality data, you willencounter real-time business problems.

5) Transform to Business-Ready DataPossibly the most difficult task in any

large-scale data migration project isestablishing when the data is business-ready. This means that the data is not onlyready-to-load, but that it will also generatethe desired result in the new target in

real-time. BackOffice provides specializedtasks, reporting and the Business ReferenceModel™ (BRM™) to ready data to executein the target system. The BRM is a libraryof 10,000+ callable elements for reference.

These key tenets help ensure your real-time analytics implementations are backedby the highest level of data quality from accurate, Business-Ready Data. ■

BACKOFFICE ASSOCIATES®For more details, go to: 5 Steps to Data Readiness

5 Steps to Data Readinessfor Real-time Analytics

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BUSINESS BENEFITS OF SAP ANDBACKOFFICE SOLUTIONS FOR REAL-TIME DATA MANAGEMENT· Business applications that accelerate

productivity and time-to-value in customer, material, vendor and financialmaster data

· Embedded methodology, domain content and process to guide both SAP and non-SAP data quality and data migration efforts with best practices, KPIs and metrics

· Prebuilt templates for access to multiplelegacy sources—jumpstarts real-timedata access and integration

· Full platform of standardized applicationmanagement capabilities, utilities andcontrols for best practices in managingenterprise master data and flow in real-time