Discover the value in IBM Business Analytics

IBM Business Analytics and Optimization Discovering the Value of Business AnalyticsLennart Frantzell alf@us.ibm.com Giuseppe Accardo gaccard@us.ibm.com, Chris Heckhart checkart@us.ibm.comIBM San Mateo Innovation Center, San Mateo, California 2011/05/6 © 2009 IBM Corporation

Event Agenda IEEE Accessing the Future Conference, Boston, July 2009.Discover the Value of IBM Business AnalyticsWednesday, May 25, 2011 TIME TOPIC 10:00 a.m. Registration 10:15 a.m. Introduction, an overview of Business Analytics Lennart Frantzell, San Mateo IBM Innovation Center 11:30 a.m. IBM Business Analytics product portfolio introduction Giuseppe Accardo, San Mateo IBM Innovation Center 12:00 Lunch, networking 1:00 p.m. IBM Business Analytics product portfolio introduction, cont Giuseppe Accardo, San Mateo IBM Innovation Center 1:30 p.m. Prescriptive analytics in the real world with ILOG Jeremy Bloom, IBM 2:00 p.m. Build Online Revenue Gene Hoffman, Vindicia 2:30 p.m. Where do we go from here? Lennart Frantzell, San Mateo IBM Innovation Center © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009.The sea change, from analog to digital data • Historical change, from analog to digital data • Today, mankind generates staggering amounts of digital data • How do we search vast amounts of digital data? • How do we make sense of all this data? • Mobile computing, social networks and Cloud Computing make business analytics accessible everywhere • Mankind entering era of informed decision making © 2009 IBM Corporation

1 billion transistorsIEEE Accessing the Future Conference, Boston, July 2009. for each person on earth. 1 trillion things connected to the net.THINKBy 2010,30 billion RFID tags,embedded intoour world.© © 2009 IBM Corporation 4ILO

IEEE Accessing the Future Conference, Boston, July 2009.Artificial Intelligence and Analytics• AI – Inference engines, Expert Systems, Rete Algorithm, Prolog, Neural Networks, Rules-based systems Analytics – Complex algorithms and very little data – Answers intertwined with algorithms – Snakebites in Australia Patient HIV treatment – Airline scheduling• Analytics – Staggering amounts of data – Separation of algorithms and data • Match incoming patient against patients – Successful HIV treatment in Ethiopia, who have been successfully treated for HIV, match patients against data. • Select that treatment © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009.Searching large amounts of data The canonical example application of MapReduce is a process to count the appearances of each different word in a set of documents: void map(String name, String document): // name: document name // document: document contents for each word w in document: EmitIntermediate(w, "1"); void reduce(String word, Iterator partialCounts): // word: a word // partialCounts: a list of aggregated partial counts int result = 0; for each pc in partialCounts: result += ParseInt(pc); Emit(AsString(result)); © 2009 IBM Corporation

IBM’s Grand Challenges: Deep BlueIEEE Accessing the Future Conference, Boston, July 2009. 1997 IBM’s chess-playing computer. Each chip was equipped with a million transistors, which evaluated 2 million positions Each second. In Deep Blue, some 256 chips were teamed together under the overall control of a general-purpose IBM SP2®, a parallel computer consisting of, in this case, 32 processor nodes. The parallelism derived from these 32 processors and 256 chess accelerator chips is what mades Deep Blue the most powerful chess computer in the world. It was capable of looking at an average of 100 million positions per second. © 2009 IBM Corporation

IBM’s Grand Challenges: Deep Blue, Blue GeneIEEE Accessing the Future Conference, Boston, July 2009. 1997 2005 Blue Gene Blue Gene is an IBM Research project dedicated to exploring the frontiers in supercomputing: in computer architecture, in the software required to program and control massively parallel systems, and in the use of computation to advance our understanding of important biological processes such as protein folding. © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009.Watson and structured versus unstructured data The canonical example application of MapReduce is a process to count the appearances of each different word in a set of documents: void map(String name, String document): // name: document name // document: document contents for each word w in document: EmitIntermediate(w, "1"); void reduce(String word, Iterator partialCounts): // word: a word // partialCounts: a list of aggregated partial counts int result = 0; for each pc in partialCounts: result += ParseInt(pc); Emit(AsString(result)); © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009.IBM Watson and IBM’s DeepQA Technology• Watson runs IBM’s DeepQA technology, developed using Apache UIMA, a framework implementation• of the Unstructured Information Management Architecture.• UIMA was designed to support interoperability and scale-out of text and multimodal analysis applications.• The Watson database includes Wikipedia and other sources• Powered by IBM POWER7 processor technology, Watson is an example of the complex analytics workloads that are becoming increasingly common in business• Watson also uses Apache Lucene, Indri, SPARQL and the Jena Toolkit• Watson’s DeepQA UIMA annotators were deployed as mappers in the Hadoop map-reduce framework, which distributed them across processors in the cluster. IBM DeepQA Apache UIMA The Regular Expression Annotator (RegexAnnotator) Database/Wikipedia is an Apache UIMA analysis engine that detects entities like email addresses, URLs, phone numbers, zip codes or IBM Power 7 Hardware any other entity based on regular expressions and concepts. July 20, 2009 © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009.Watson in Healthcare Natural Language Processing in Healthcare • As Electronic Healthcare Records systems are adopted by Government mandate, physician notes are digitized in a computer readable format…, the Mayo Clinic and IBM have already announced a partnership to open source much of the UIMA annotators Mayo developed to mine its own medical records. • Mining patient reported data is another interesting area. Patient communities such as PatientsLikeMe and Association of Cancer Online Resources. • In 1999 by BMJ (British Medical Journal) a team of researchers observed 103 physicians over one work day. Those physicians asked 1,101 clinical questions during the day. The majority of those questions (64 percent) were never answered. http://www.ibm.com/developerworks/industry/library/ind-watson/index.html July 20, 2009 IBM Confidential © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009.EuResist, HIV Treatment in EthiopiaDoctors in Ethiopia can instantlycompare this blood sample to over41,000 HIV treatment histories.EuResist is helping doctors predictpatient response with over 78%accuracy – outperforming 9 out of 10human experts.The tool is built on an IBM analytics solution that integrates a variety of disparatedatabases onto a flexible IBM DB2® platform to process complex metadata moreeffectively than anything else on the market. Link: http://www.euresist.org/ © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009. Optimization and Analytics, an Overview What’s the best that can happen Stochastic including the effects of variability? Optimization Prescriptive What’s the best that can happen ? OptimizationCompetitive Advantage What will happen next ? Predictive Modeling What if these trends continue? Forecasting Predictive Statistical Analysis What could happen…. ? Alerts What actions are needed? Query/Drill Down What exactly is the problem? Descriptive Ad Hoc Reports How many, how often, where? Std Reports What happened? Degree of Complexity Based on: Competing on Analytics, Davenport and Harris, 2007 © 2009 IBM Corporation

Architecture pattern: Service Orientation architecture and analytics IEEE Accessing the Future Conference, Boston, July 2009. Analysis Dashboarding Optimization Analysis Predictive Statistical Datamining Analysis Analysis Analytics ETL (Extract Transform Load) ETL DataData sources: WarehousePatient data, e-meters, Cycle initiationdata streams, Build reference data Extract (from sources)unstructured data Validate Transform (clean, apply business rules, check for data integrity… ) Stage (load into staging tables, if used) Audit reports (for example, on compliance with business rules. ) Publish (to target tables) Archive Clean up © 2009 IBM Corporation

Cross River, HIF Reference Implementation at GlobalIEEE Accessing the Future Conference, Boston, July 2009. Solutions Center • Premier test harness, product showcase, and center of excellence at the IBM Global Solutions Center (Dallas) • Proof point for integrating all essential components for an enterprise class health analytics platform (integration, analytics, presentation layer) Data Source Layer (Clinical, Financial, (Operational, Administrative)) … © 2009 IBM Corporation

Cross River, HIF Reference Implementation at GlobalIEEE Accessing the Future Conference, Boston, July 2009. Solutions Center • Premier test harness, product showcase, and center of excellence at the IBM Global Solutions Center (Dallas) • Proof point for integrating all essential components for an enterprise class health analytics platform (integration, analytics, presentation layer) Integration Layer InfoShpere WSTX Adapters Rational Data Information Analyzer, DataStage, QualityStage, Service Director (HL7) (e.g. Cache) Architect Data Source Layer (Clinical, Financial, (Operational, Administrative)) … © 2009 IBM Corporation

Cross River, HIF Reference Implementation at GlobalIEEE Accessing the Future Conference, Boston, July 2009. Solutions Center • Premier test harness, product showcase, and center of excellence at the IBM Global Solutions Center (Dallas) • Proof point for integrating all essential components for an enterprise class health analytics platform (integration, analytics, presentation layer) Data Layer Data InfoShpere InfoShpere WH InfoSphere Meta InfoSphere BCU Models Warehouse Cubing Services Data Management Business Glossary Integration Layer InfoShpere WSTX Adapters Rational Data Information Analyzer, DataStage, QualityStage, Service Director (HL7) (e.g. Cache) Architect Data Source Layer (Clinical, Financial, (Operational, Administrative)) … © 2009 IBM Corporation BCU: Balanced Configuration Unit

Cross River, HIF Reference Implementation at GlobalIEEE Accessing the Future Conference, Boston, July 2009. Solutions Center • Premier test harness, product showcase, and center of excellence at the IBM Global Solutions Center (Dallas) • Proof point for integrating all essential components for an enterprise class health analytics platform (integration, analytics, presentation layer) Analytic Layer Cognos Performance InfoShpere Structured and Cognos BI Management Unstructured Data Mining Data Layer Data InfoShpere InfoShpere WH InforSphere Meta InfoSphere BCU Models Warehouse Cubing Services Data Management Business Glossary Integration Layer InfoShpere WSTX Adapters Rational Data Information Analyzer, DataStage, QualityStage, Service Director (HL7) (e.g. Cache) Architect Data Source Layer (Clinical, Financial, (Operational, Administrative)) … © 2009 IBM Corporation

Cross River, HIF Reference Implementation at GlobalIEEE Accessing the Future Conference, Boston, July 2009. Solutions Center • Premier test harness, product showcase, and center of excellence at the IBM Global Solutions Center (Dallas) • Proof point for integrating all essential components for an enterprise class health analytics platform (integration, analytics, presentation layer) Presentation Layer Clinicians Researchers Patients Administrators Chronic Disease Track, analyze, Planning and Ad Hoc Cognos Cohort Analysis Management report events Forecasting Analysis WebSphere Portal Server Analytic Layer Cognos Performance InfoShpere Structured and Cognos BI Management Unstructured Data Mining Data Layer Data InfoShpere InfoShpere WH InforSphere Meta InfoSphere BCU Models Warehouse Cubing Services Data Management Business Glossary Integration Layer InfoShpere WSTX Adapters Rational Data Information Analyzer, DataStage, QualityStage, Service Director (HL7) (e.g. Cache) Architect Data Source Layer (Clinical, Financial, (Operational, Administrative)) … © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009.Technology Evolution of BI & Analytics (Blog: Wayne Eckerson - BeyeNetwork) Sub-market Segments: BI Tools Data Integration tools DB Management Systems Hardware Platform Reporting languages (Focus and Ramis) © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009. Actionable Optimization & Analytics What should we do, given the What-if Analysis alternatives and real-time changes? Prescriptive Mathematical Optimization How can we achieve the best outcome? Foresight Monte Carlo simulation What could happen …? Predictive Predictive modeling What will happen next if ? Forecasting What if these trends continue? Competitive Advantage Alerts What actions are needed? Query/drill down What exactly is the problem? Insight Descriptive Ad hoc reporting How many, how often, where? Standard Reporting What happened or is happening? Degree of Complexity Based on: Competing on Analytics, Davenport and Harris, 200734 © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009.Business Intelligence & Performance ManagementAnswer three important questions that drive better performance Finance Sales Operations How are we doing? Scorecards and DashboardsMarketing What should we be doing? Planning, Forecasting and Budgeting Why? Reporting & Analytics Customer Human Service Resources IT/Systems 37 © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009. 10 Capabilities Querying and Reporting Querying and Reporting Analysis & Planning Analysis & Planning Dashboarding Dashboarding Scorecarding Scorecarding © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009. 10 Capabilities Real time monitoring Real time monitoring Statistics Statistics Extending BI Extending BI Collaborative BI Collaborative BI © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009. 10 Querying and Reporting •Design and build – Create report templates to include standard report objects, queries, and layouts. •Analyze and share – View, interact with and analyze the result set, and share the results generate a unique perspective around information. •Assemble and format widgets from BI, TM1, Real-Time Monitoring, Metric Studio, PowerPlay, RSS and HTML elements etc and put them in a single report © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009. 10 Querying and Reporting •Relational databases from IBM, Oracle, Microsoft, Teradata, and Sybase, various sources •Satellite data sources, •Content management accessible via ODBC and including Microsoft Excel files, data, including IBM dimensionally aware Microsoft PowerPoint® files, FileNet®, EMC sources like SAP BW. Microsoft Access® files, flat Documentum, OpenSoft files and more. and others •Mainframe sources, •Modern data sources, including VSAM, IMS, Supported such as XML, LDAP and IDMS, COBOL® WSDL data copybooks and others sources •Enterprise data •Widely deployed ERP warehouses and marts, systems, including •All widely used OLAP mySAP (R/3), PeopleSoft with both 3NF and star sources, including IBM schemas. Enterprise, JD Edwards DB2 OLAP Server, IBM EnterpriseOne, Oracle Cognos PowerCube, eBusiness Suite and Microsoft Analysis Siebel CRM. Services, Oracle 10G and Oracle EssbaseOLAP. © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009. 10 Dashboarding Louis Barton, a Frost Bank IT executive, dashboards add value by “reducing the cycle time it takes to analyze information [key performance metrics], You can make a decision sooner. That means people are more productive.” © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009. TM1 Planning Software With Cognos Planning you can access current actual data to assess fiscal performance, and proceed from what- is to evaluate the what-if scenarios critical to forecasting future performance.• Rapid development.• Sophisticated modeling. PLANS & FORECASTS• Flexibility.• Finance friendliness.• Less time on processPower of “sandboxing”: DEMO DEMO Video Video Link: http://forms.cognos.com/?elqPURLPage=2293&offid=od_tm1 © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009. 10 Scorecarding - Communicate strategy - Understand key relationships - Build metrics and scorecards based on reliable information It allows executives and business managers to instantly visualize how the business is performing against key performance indicators. At the operational level, departments and employees Strategy Map with associated metrics can use scorecards to monitor their performance against targets set for specific projects and activities. Metrics grouped by owner Cause and effect diagram Advance initiative tracking © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009. 10 Extending Business Intelligence Provide actionable intelligence to users, no matter their location or their connectivity. Business users, from executives to mobile field workers, can know and understand the health of the business at all times, and have the tools to take action on what they see. Reduce the burden on IT to redevelop reports for various devices. Take full advantage of the mobile network infrastructure — an excellent opportunity for low-cost BI deployment. © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009. 10 Collaborative Business IntelligenceWith integrated Lotus Connections, users can:• Link directly from Lotus Connections to a Cognos Business Insight dashboard• Use single sign-on for both Business Insight dashboards and Lotus Connections• Add other individuals to an Activity at any point in the decision-making process• Search for Activities directly from the Business Insight window• Send email notifications directly from the Activity © 2009 IBM Corporation

IBM Cognos Express – Solution for the Mid MarketIEEE Accessing the Future Conference, Boston, July 2009.Features of IBM Cognos Express Features of IBM Cognos Express Features of IBM Cognos ExpressReporter Advisor Xcelerator•Complex reporting tool designed for •Create multidimensional view of your •Delivers the powerful and fast in-memorybusiness business based from your relational data multidimensional database•Reports against a single common data with a few clicks while employing the •Create scenarios, versions, variance andsource will harmonize your business powerful and fast in-memory what-if analysis against live data directly in•Self-service flexible reports to meet the multidimensional database Excelneeds of different users, including financial, •Get maximum information from your data •Build and edit your plans real-time withproduction, operational, transactional, using drill-down and drill-up capability in write-back capabilitymanaged or ad hoc reports combinations with lucid graphical outputs •Use worksheets - employ your strong•No matter if relational or multidimensional •Conformable self-service ad-hoc analysis knowledge of Excel and extend it withOLAP data are used for reports according to your needs without waiting for powerful Cognos Express functionality like•Ergonomic Web interface IT department implementation multidimensional data functions•Drag&Drop style of work •Step into the world of what-if analysis and •Web interface available for easy data•Publish reports to web portal, HTML, PDF planning with the write-back and data contribution and work with excelor Excel files spread features worksheets without having Excel installed•Interactive dashboard for quick orientation •Ergonomic Web interface on your machineand decision making across the whole •Employ the power of dashboards and •A single common base for metadata andcompany interactive reports data, business rules and calculations,•Integration with other modules, a single •Integration with other modules, a single which harmonizes the view of yourplatform for BI and planning platform for BI and planning business47 © 2009 IBM Corporation

Case Study #1 - BMR tones up its sales performance with advancedIEEE Accessing the Future Conference, Boston, July 2009. analytics Business need: BMR was in the process of replacing its core ERP solution, and saw this as an opportunity to enhance its business analytics capabilities to deliver improved sales performance management. As a mid-sized business, BMR wanted to find an affordable solution that would offer enterprise-class functionality. Solution: ProStrategy Colman, an IBM Business Partner, helped BMR become the first company in Europe to implement IBM Cognos Express – an all-in-one business intelligence and planning solution designed for mid-sized companies. The solution is integrated with the company’s new Microsoft Dynamics NAV ERP system, and also draws data from sales channels such as eBay, BMR’s Slendertone website and retail customer databases. Benefits: Provides real-time analysis of sales performance, helping sales teams and managers work more productively. Reduces time spent on collecting and checking data by more than 30 percent, allowing users to focus on actual analysis. Eliminates data silos and provides a ‘single version of the truth’ with accurate, up-to-date information. http://www-01.ibm.com/software/success/cssdb.nsf/CS/STRD-8CEE4P?OpenDocument&Site=default&cty=en_us © 2009 IBM Corporation

Case Study #2 - Mercury Medical a healthcare manufacturer improvesIEEE Accessing the Future Conference, Boston, July 2009. reporting and analysis with IBM Cognos Express Business need: IBM Software Valuenet Reselling Partner, BlueNET Technologies introduced Mercury Medical to Cognos Express through the 30-day product trial that allowed BlueNET to create a custom report and analysis demo tailed to Mercury’s specific data and user needs. Solution: Cognos Express met Mercury Medical’s recovery time objectives, giving the company confidence in its decision. Benefits: Mercury’s users can now create the most critical reports that they were previously relying on a Legacy Reporting Platform to produce. These include sales commissions, weekly sales, a rolling 12-month sales report, and an inventory summary report. http://www-01.ibm.com/software/success/cssdb.nsf/CS/SANS-8DBM29?OpenDocument&Site=default&cty=en_us © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009.Imagine you could gain new insights to…. …predict …apply social …adjust credit …determineregions where relationships of lines as discount levels for doctors customers to transactions are select people atprescribe high prevent churn? occurring to time of sale volume of account for risk instead of medication? fluctuations? offering to all? Pharma Telco Call Loan Officer Retail Sales Sales Center Rep Associate Manager52 © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009. SPSS Enables New Solution Value for IBM Cognos Customers How are Why are we What should we doing? on/off track? we be doing? Addition of KPPs Addition of KPPs Broad distribution of Broad distribution of Time series Time series (Key Performance (Key Performance statistical results statistical results forecasting forecasting Predictors) Predictors) New customer New customer Predictive analytics for Predictive analytics for insight through insight through deeper understanding of deeper understanding of Data Collection Data Collection the data the data53 © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009. DEMO DEMO Video Video Traditional decision processes evolved Traditional Approach Breakaway Sense and Respond Predict and act Back Office Point of impact Skilled analytics experts Everyone Instinct and Intuition Realtime fact driven Automated Optimized © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009. IBM SPSS Statistics is a comprehensive, easy-to-use set of data and predictive analytics tools for business users, analysts and statistical programmers.SPSS Statistics Family Linear models – make your analysis more accurate and reach more dependable conclusions Nonlinear models – have the ability to apply more sophisticated models to your data IBM SPSS Statistics Standard Customized tables – quickly slice and dice your data using pivot tables Data preparation – Prevent outliers from skewing analyses and results Decision trees – Better identify groups, discover relationships between groups and predict future events IBM SPSS Statistics Professional Forecasting – Deliver information in ways that your organization’s decision makers can understand and use Structural equation modeling - you can quickly create models to test hypotheses Bootstrapping - Estimate the standard errors and confidence intervals of parameters IBM SPSS Statistics Premium Direct marketing and product decision making procedures - Develop a marketing strategy High-end charts and graphs - Extend the capabilities of templates or create your own Provide more flexible pricing and licensing options Easily extend usage throughout the university Foster a permanent link between academic and corporate institutions IBM SPSS for Education Recognize IBM SPSS software users for their contributions to their respective industries Support more effective teaching with IBM SPSS software Ensure that students will be sought by employers © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009. Product Family Data Collection Modeller Deployment Survey and market IBM® SPSS® Modeler is Drive results-oriented researchers worldwide a powerful, versatile decisions by building use this rich suite of data mining workbench analytics into your products to achieve that helps you build operations. Integrate the deeper understanding accurate predictive analytics that predict of people’s attitudes, models quickly and outcomes. Automate preferences and intuitively, without processes to deliver behavior. programming insight at the point of impact. -Authoring -Interviewing -Reporting -Management © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009. Data Collection toolsAuthor Desktop Paper/ScanAuthor Professional Phone Interviews Remote AdministrationAuthor Server Survey Reporter DesktopBase Professional Survey Reporter Developer KitData Entry Station Survey Reporter ProfessionalData Model Survey Reporter ServerDialer Survey TabulationInterviewer Translation UtilityInterviewer Server Administrator Web Interviews © 2009 IBM Corporation

Modeller IEEE Accessing the Future Conference, Boston, July 2009.IBM SPSS Modeler includes advanced, interactive visualization for models that use single technique, or ensemblemodels that combine techniques making modeling results easy to understand and communicate. © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009. Case study: Predictive Analytics on Human Capital ManagementProblem:Optimize recruitment effort for a given position (Corporate job, Military school, etc ..).The volume of potential recruits or the intricacies of a specific job requirement can overwhelmthe efforts of even the best individual recruiter.Solution:Build a predictive performance model.Apply the experience and intuition of expert recruiters in creating a model that helps anorganization to prioritize and target the individuals most qualified for a specific position.Example:One of the branches of the U.S. military is responsible for getting more than 100,000 newrecruits every year under contract. Approximately 600,000 leads that must then be prioritizedand sent to individual recruiters.Baseline:Predicting the success of a potential employee or recruit in a given work environment isdifficult, there are numerous variables that affect a successful outcome for that person’scareer. (Examples: changes in management, co-workers, and mission goals …. )Reference Link:http://forms.cognos.com/?elqPURLPage=4206&offid=sb_spssrc_human_capital_mgmnt_imw14291&mc=-web_ibm_spss_stat_products © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009. Case study: Predictive Analytics on Human Capital Management (cont.) Performance Prediction with SPSS Modeling: - Collect Data (predictors) - Data cleansing - Eliminate Variables with low variance - Eliminate var. with too many missing values - Screen, rank and select predictor variables - Rank the importance of each variable Employee opinions and outlooks can be an IBM® SPSS® Modeler can consolidate data visually from multiple sources, important predictor of performance. such as demographics data and attitudinal data. Text Analytics and Text Mining with SPSS: Example: Analysis of open ended questions to model employee satisfaction Provides a technical foundation for extracting usable knowledge from unstructured text data through identification of core concepts and sentiments. Text analytics allows users to understand the relationships between concepts and the sentiment around concepts, and ultimately create a structure for unstructured text data that can be integrated with A view into text analytics within IBM® SPSS® Modeler Premium. On the left is analytics. a list of extracted categories and on the right is a visual representation of the linkages between concepts and sentiments (sentiment analysis). © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009. IBM Business Analytics and Optimization Discovering the Value of Business Analytics Where Do We Go From Here? Lennart Frantzell alf@us.ibm.com Giuseppe Accardo gaccard@us.ibm.com, Chris Heckart checkart@us.ibm.com IBM San Mateo Innovation Center, San Mateo, California 2011/04/27 © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009. Step 1) Read up on the IBM Productshttp://www.redbooks.ibm.com/redbooks/pdfs/sg247912.pdf http://www.redbooks.ibm.com/redpapers/pdfs/redp4710.pdf http://www.redbooks.ibm.com/redbooks/pdfs/sg247881.pdf © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009.Step 3) Join IBM PartnerWorld or IBM Academic Initiative http://www.ibm.com/partnerworld https://www.ibm.com/developerworks/university/academicinitiative / © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009.Step 4) Follow-on Business Analytics education at the SanMateo Innovation Center •Netezza Bootcamp (6/21-6/24) •Cognos seminar •SPSS seminar •ILOG seminar / © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009.Step 5) Join IBM Social Networks, read Business AnalyticsBlogs and the San Mateo IBM Innovation Center blog https://www.ibm.com/developerworks/mydeveloperworks/groups/service/forum/topicThread? topicUuid=45358eb2-315a-43e3-8e5f-5e94fd60009a#fullpageWidgetId=Membershttps://www.ibm.com/developerworks/mydeveloperworks/blogs/business-analytics/?lang=enhttps://www.ibm.com/developerworks/mydeveloperworks/blogs/iic-san-mateo/?lang=en http://www-935.ibm.com/services/us/gbs/bao/ / © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009. Reference Links: Cognos: http://www.reporters.cz/en/index.php?option=com_content&task=view&id=123&Itemid=168 SPSS: http://www-01.ibm.com/software/analytics/spss/downloads/ http://www-01.ibm.com/software/analytics/spss/products/modeler/ http://www-01.ibm.com/software/analytics/spss/products/modeler/professional.html http://support.spss.com/ProductsExt/Data%20Collection/ProductMatrix.html iLOG: http://www-01.ibm.com/software/websphere/ilog/ http://www-01.ibm.com/software/solutions/soa/newsletter/nov10/brms.html Blog http://www.b-eye-network.com/blogs/eckerson/archives/business_analyt/ March 24, 2011 © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009.Watson, the hardware• Each of Watson’s 90 clustered IBM Power 750 servers features 32 POWER7 cores running at 3.55 GHz.• Running the Linux®operating system, the servers are housed in 10 racks along with associated I/O nodes and communications hubs.• The system has a combined total of 16 Terabytes of memory and can operate at over 80 Teraflops (trillions of operations per second).• POWER7 also features 500 gigabytes of on-chip communications bandwidth, contributing to exceptional efficiency of both memory and processor utilization. And since each server packs 32 high performance POWER7 cores with up to 512 GB of memory, the Power 750 makes an ideal platform for Watson’s processor and memory-hungry Java processes.• Designing Watson on commercially available Power 750 servers was a deliberate choice to ensure more rapid adoption of optimized systems in industries such as healthcare and financial services.• That goal was a fundamental difference between Watson and Deep Blue, which was a highly customized supercomputer. Deep Blue was based on an earlier generation of Power processor technology, featuring a.But in addition to the regular POWER2 processors, Deep Blue’sperformance was enhanced with 480 special purpose chess processor chips. July 20, 2009 IBM Confidential © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009. Inside Watson• Watson uses UIMA-AS to scaleout across 2,880 POWER7 cores in a cluster of 90 IBM Power®750 servers.• UIMA_AS manages all of the inter-process communication using the open JMS standard.• The UIMA-AS deployment on POWER7 enabled Watson to deliver answers in one to six seconds.• Watson has roughly 200 million pages of natural language content (equivalent to reading 1 million books).• Watson uses the Apache Hadoop framework to facilitate preprocessing the large volume of data in order to create in-memory datasets used at runtime.• Watson’s DeepQA UIMA annotators were deployed as mappers in the Hadoop mapreduce framework, which distributed them across processors in the cluster. The Regular Expression Annotator (RegexAnnotator) is an Apache UIMA analysis engine that detects entities like email addresses, URLs, phone numbers, zip codes or any other entity based on regular expressions and concepts. July 20, 2009 IBM Confidential © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009.Madrid’s emergency first responders Youre invited to take a ride with Madrid’s emergency first responders as they rush to the scenes of three separate incidents. In the wake of the 2004 Madrid bombings, the city implemented a business process management solution from IBM to integrate the disparate applications, data and processes of its various emergency departments. IBM helped the city reduce emergency response times by 25%. the ride. http://www-03.ibm.com/innovation/us/leadership/response/index.html © 2009 IBM Corporation

IBM Watson and Healthcare. How natural language and semanticIEEE Accessing the Future Conference, Boston, July 2009. search could revolutionize clinical decision support According to an observational study published in 1999 by BMJ (British Medical Journal) a team of researchers observed 103 physicians over one work day. Those physicians asked 1,101 clinical questions during the day. The majority of those questions (64 percent) were never answered. And, among questions that did get answered, the physicians spent less than two minutes looking for answers. Only two questions out of the 1,101 triggered a literature search by the physicians attempting to answer them. Hence, providing quick answers to clinical questions could have major impact in improving the quality of healthcare. Enter Watson. To see the kinds of questions Watson can answer, check out the two example questions Dr. David Ferrucci showed to German Chancellor Merkel and Turkish PM Erdogan at the CeBIT 2011 Opening Ceremony.. Question: Streptococci cause this childhood "fever" characterized by a bright red rash and high temperature. Answer: 98% Scarlet fever, 15% Rheumatic fever, 8% Strep throat Question: This disease can cause uveitis in a patient with family history of arthritis presenting circular rash, fever, and headache. Answer: 76% Lyme Disease, 1% Behcets Disease, 1% Sarcoidosishttp://www.ibm.com/developerworks/industry/library/ind-watson/index.html © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009. IBM SPSS Statistics is a comprehensive, easy-to-use set of data and predictive analytics tools for business users, analysts and statistical programmers.SPSS Statistics Family Linear models – make your analysis more accurate and reach more dependable conclusions Nonlinear models – have the ability to apply more sophisticated models to your data IBM SPSS Statistics Standard Customized tables – quickly slice and dice your data using pivot tables Linear models Nonlinear models • General linear models (GLM) • Multinomial logistic regression (MLR) • Generalized linear mixed models (GLMM) • Binary logistic regression • Hierarchical linear models (HLM) • Nonlinear regression (NLR) and constrained • Generalized linear models (GENLIN) nonlinear regression (CNLR) • Generalized estimating equations (GEE) • Probit analysis Customized tables IBM SPSS Statistics Standard enables you to quickly “slice and dice” your data. Then you can create customized tables to help you better understand your data and easily report your results. © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009. IBM SPSS Statistics is a comprehensive, easy-to-use set of data and predictive analytics tools for business users, analysts and statistical programmers.SPSS Statistics Family Data preparation – Prevent outliers from skewing analyses and results Decision trees – Better identify groups, discover relationships between groups and predict future events IBM SPSS Statistics Professional Forecasting – Deliver information in ways that your organization’s decision makers can understand and use Data preparation Decision trees IBM SPSS Statistics Professional helps you streamline the Create classification and decision trees to help you better data preparation stage of the analytical process – saving identify groups, discover relationships between groups and time and ensuring greater accuracy. Perform data checks predict future events. Decision trees present categorical based on each variable’s measure level, quickly find results in an intuitive manner, allowing you to explore multivariate outliers by searching for unusual cases based results and visually determine how your model flows, and upon deviations from similar cases and preprocess data then clearly explain categorical results to non-technical prior to model building with an optimal binning procedure. audiences. You can also find specific subgroups and relationships that you might not uncover using more traditional statistics. Forecasting Predict trends and develop forecasts quickly and easily with advanced statistical techniques to work with time- series data. Regardless of your level of experience, you can analyze historical data, predict trends faster and deliver information in ways that your organization’s decision makers can understand and use. © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009. IBM SPSS Statistics is a comprehensive, easy-to-use set of data and predictive analytics tools for business users, analysts and statistical programmers.SPSS Statistics Family Structural equation modeling - you can quickly create models to test hypotheses Bootstrapping - Estimate the standard errors and confidence intervals of parameters IBM SPSS Statistics Premium Direct marketing and product decision making procedures - Develop a marketing strategy High-end charts and graphs - Extend the capabilities of templates or create your ownStructural equation modeling BootstrappingStructural equation modeling (SEM) can help you gain provides an efficient way to ensure that your models areadditional insight into causal models and explore the stable and reliable. It estimates the sampling distribution ofinteraction effects and pathways between variables. SEM an estimator by re-sampling with replacement from thelets you more rigorously test whether your data supports original sample. With bootstrapping, you can reliablyyour hypothesis. You create more precise models than if you estimate the standard errors and confidence intervals of aused standard multivariate statistics or multiple regression population parameter, including the mean, median,models alone. proportion, odds ratio, correlation coefficient, regression coefficient and numerous others. Direct marketing and product decision-making procedures Quickly perform various kinds of analyses, including recency, frequency and monetary value (RFM) analysis, cluster analysis and prospect profiling. Increase your understanding of consumer preferences to more effectively design, price and market successful products – maximizing campaign effectiveness and return on investment. © 2009 IBM Corporation

Business Analytics - acquisition landscapeIEEE Accessing the Future Conference, Boston, July 2009. Coremetrics, is a leader in Web analytics software. Coremetrics, based in San Mateo, CA, will expand IBMs business analytics capabilities by enabling organizations to use cloud computing services to develop faster, more targeted marketing campaigns. Unica is an enterprise and cloud-based marketing software solutions that help businesses streamline and automate marketing processes, and understand and predict customer preferences. Through Unica, IBM will enable its clients to develop more relevant and targeted communications while minimizing marketing expenditures. OpenPages, a leading provider of software that helps companies more easily identify and manage risk and compliance activities across the enterprise through a single management system.Clarity Systems delivers financial governance software that enables organizations to automate theprocess of collecting, preparing, certifying and controlling financial statements for electronic filing, insupport of mandates by the SEC and other financial regulatory agencies. Netezza data warehouse appliances bring analytics directly into the hands of business users within every department of an organization such as sales, marketing, product development and human resources. Netezza appliances makes the technology ideal for the needs of high-performance analytics, requiring minimal administration and IT skills, and enables clients to run complex data queries within days of deploying the solution. Initiates software helps healthcare clients work more intelligently and efficiently with timely access to patient and clinical data. By adding Initiates software to its software portfolio, IBM will be better equipped to help clients draw on data from hospitals, doctors offices and payers to create a single, trusted shareable view of millions individual patient records. Guardium, a market leader in real-time enterprise database monitoring and protection. Guardiums technology helps clients safeguard data, monitor database activity and reduce operational costs by automating regulatory compliance tasks. © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009. a recognized industry leader in Business Rule Management Systems (BRMS), visualization components, optimization and supply chain solutions enrich IBM software portfolio and fortify IBMs Smarter Planet initiative. WebSphere ILOG BRMS Optimization and Analytical Decision Support Solutions WebSphere ILOG BRMS Family WebSphere ILOG JRULES CPLEX Optimization Studio WebSphere ILOG LogicNet Plus XE Visualizationability for non-technical business create the best possible plans,users to be directly involved in Elixir Enterprise explore alternatives,business rules management, understand trade-offs, andenabling flexible decision JView Enterprise respond to changes in businessautomation. environment industry’s most comprehensive set of graphics products for creating highly graphical, interactive displays. © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009. What is a Business Rules Management System BRMS?A business rule management system(BRMS) enables organizational policiesto be defined, deployed, monitored andmaintained separately from coreapplication code. By externalizingbusiness rules and providing tools tomanage them, a BRMS allows businessexperts to define and maintain thedecisions that guide systems behavior,reducing the amount of time and effortrequired to update production systems,and increasing the organization’sability to respond to changes in thebusiness environment. © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009. Why Business Event Processing (BEP) matters? Business Event Processing describes a wide range of ways that enterprises approach events, simple or complex. But in all cases, information about the event needs to be quickly disseminated to others affected by the event for both awareness and to take appropriate action. DEMO DEMO Video Video © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009. What is ILOG Optimization ? A software based solution that enables enterprises to create the best possible plans, explore alternatives, understand tradeoffs and respond to changes in the business environment IBM ILOG optimization maximizes resource efficiency • By helping companies make Decisions • To reach a Goal • While observing Requirements • Determined by Analyzing Data Using powerful, robust, scalable and diversified optimization software and services Requirements Requirements Decisions Decisions Bus. Rules Bus. Rules Plans – alternatives - tradeoffs Plans – alternatives - tradeoffs Goals Goals Data Data © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009. What optimization can do? Optimization helps businesses make complex decisions and trade-offs about limited resources • Discover previously unknown options or approaches • Automatically evaluate millions of choices • Automate and streamline decisions • Compliance with business policies and regulations • Free up planners and operations managers so that they can leverage their expertise across a wider set of challenge • Explore more scenarios and alternatives • Understand trade-offs and sensitivities to various changes • Gain insights into input data • View results in new ways © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009.Success Story – Unit Commitment at REE Business Problem – Use exact mathematical methods to replace the approximate, heuristic methods Red Eléctrica de España, in charge of managing the Spanish national power grid, had been using for the last 20 years The methodology applied until now was an interactive methodology, which did not guarantee an optimum solution. There were many difficulties in the smaller systems and it was hard to find the most viable solution. Thanks to the new methodology, we have resolved this type of problem. - Mr. Mustafa Pezic, REE Project Director © 2009 IBM Corporation

IEEE Accessing the Future Conference, Boston, July 2009.Benefits• The implementation of the ILOG based solution has provided great operational advantages to company’s managers and engineers – “The new tool allows us to simplify all maintenance tasks and any changes made to the model, which in our particular case, are very frequent.” – “From a user viewpoint, it has brought greater trust in the solution and a significant reduction in planning time required by users. In parallel with this, from a development and maintenance viewpoint, there has been a significant reduction in associated costs, as well as in the duration of the processes.”• The bottom line: – REE reduced production costs by between €50,000 and €100,000 per day. – REE has reduced its carbon emissions by approximately 100,000 tons of CO2 annually. © 2009 IBM Corporation

Saving $140,000 Per Day:How Companies are AchievingBreakthrough Improvements in Bottom-Line Performance Using OptimizationDr. Jeremy BloomProduct Marketing Manager, ILOGOptimizationMay, 2010

The Story In Brief Better decisions faster • IBM ILOG Optimization Products are Helping Many Businesses Run More Efficiently • IBM ILOG Optimization Uses Sophisticated Technology to Solve Hard Business Problems • IBM ILOG Optimization Products and Services Can Help Your Business Run More Efficiently • IBM ILOG Optimization Can Generate Hard Benefits to Your Bottom Line 2

What Can Optimization Do? increased productivity at Europe’s most efficient car production Automobile Manufacturer facility by 30% • South American country’s two largest forest-products reduced their truck fleets by 30% and saved $20 million annually companies • Major Electronics cut wafer-processing cycle time in half, to just 30 days Manufacturer responded to unexpected delays with efficient crew rescheduling, International airline saving $40 million in one year cut package delivery costs by $87 million over 2 years and reduced Package delivery company its aircraft fleet by 10% Television network increased annual advertising revenue by $50 million Investment firm cut transaction costs by $100 million Consumer packaged goods dramatically increased the direct loading of trucks off its packaging manufacturer lines 3

What Can Optimization Do?• Whether the problem is large or small, straightforward or complex, optimization supports effective decision-making across a wide range of issues.• Firms in many industries use optimization software to solve business problems ranging from long-term planning to real-time scheduling and rescheduling. 4

Where is optimization used? 5

Benefits of Optimization• Calculable ROIs, with paybacks within months, sometimes even weeks – Capital expense avoidance or deferral – Operating expense reductions – Total revenue, revenue mix, and margin improvements• Improved customer satisfaction – Provide better and more customized customer service• Improved employee satisfaction – Satisfy schedule preferences while improving productivity – Better planning and scheduling processes 6

Sophisticated Optimization Technology Solves HardBusiness Problems• IBM ILOG Optimization helps businesses maximize resource efficiency – by helping companies make Choices – to reach Targets – while observing Limits – driven by analyzing Data• Using powerful, robust, scalable, and diversified optimization technology and services – Optimization has most value when there are many choices with complex relationships that force trade-offs 7

How Optimization Supports Decision Making What-If Analysis Collaboration 8

Case Study Cash Management: Restocking Automatic Teller Machines 9

Restocking Automatic Teller Machines The Customer• Provides financial electronic commerce services and products to financial institutions worldwide• Provides systems processing more than two-thirds of 14 billion annual automated clearing house transactions in the US• Provides reconciliation, financial messaging, workflow and compliance products and services to more than 600 banks and businesses• Its clients manage more than 2.6 million portfolios totaling about US $1.8 trillion in assets 10

Restocking Automatic Teller Machines The Business Problem Schedule restocking taking into account customer withdrawal habits and government cash management regulations• Too much cash some times – carrying costs• Too little cash at other times – angry customers• Forecast errors – volatility• Data errors – static, dirty, missing, wrong! 11

Restocking Automatic Teller Machines Vaults as Distribution Centers • Services: counting, verifying, sorting, packaging, shipping • Federal Reserve Regulations – Cross-shipping penalties – Custodial Inventory: De Minimis Exemptions, Fitness Issues, etc. • Banks Organize Vaults Geographically by FRB zone – 33 Zones in US – From 2 to 12 Vaults per Zone • High Service Levels – Due to nature of product (cash) and customer (ATM’s and bank branches) – Substantial business case for optimization solution 12

possible Day 1 Day 2 Day 3 Day 4solution +10 -10 +40 10 0 v1 v1 v1 v1 40 20 +10 -50 +20 FED 10 0 10 DEPOSITS FEDORDERS 10 v2 v2 v2 v2 20 10 +10 +10 0 10 -10 v3 v3 v3 v3 Note: Uses 4 trucks 13 13

Restocking Automatic Teller Machines Business Case Synopsis: Top-10 Bank Client• Daily Retail Cash Dispensed – $ 200 million (+20,000 retail outlets - Branches & ATM’s)• Total Cash in System (before optimization) – $ 7 billion• Optimization Development Goals – No change of current replenishment schedules – Reduce cash inventory levels (i.e. carrying costs) – Reduce replenishment costs (i.e. deliveries) – Reduce cross-shipping costs (penalties at Fed) – Improve reporting capability (information) – “Piggybacking” fixed-charge denomination shipments – Must solve overnight for implementation next day 14

Restocking Automatic Teller Machines The Bottom Line: Results After 6 Months • 58 Vault Pilot • Reduced cash inventories by 35%* • Reduced replenishment costs by 55% • Cross-shipping fees decreased about 63% • CPLEX runtimes within overnight window • Project rated “Highly Successful” by client’s internal Six Sigma Unit • Rolled-out to entire enterprise in 2008 * Attributable to the optimization model and other factors including better forecasting, better operations, better people, and better measurement. 15

Restocking Automatic Teller Machines What the Customer Says:• “Our OPL model solved by CPLEX has proven to be a powerful platform from which advanced uses of MILP can be studied, showcased, and advanced. Several successful efforts have been accomplished thus far with respect to speed improvements, always the challenge for us.”• “We like IBM ILOG’s people, and the reason we like them is we could call people up and talk to intelligent, well-versed, experienced people who either could answer our questions directly or could point us to a resource that could answer our questions.” 16

Case Study Transportation Scheduling: Train Timetabling 17

Train Timetabling The Customer • Netherlands Railways • Operates the busiest national railway network in Europe • Manages more than 4,800 trains per day • Has 2,100 km of track and 279 stations • Between 1970 and 2006, traffic has nearly doubled from 8 billion passenger km in to 15.8 billion • During the same period, freight transport increased by 285 percent In 2006, • 9 million different passengers traveled 15.8 billion passenger km • Operating revenues of €1.5 billion and operating income of €200 million 18

Train Timetabling The Business Problem To keep costs down and service on time, more accurately match trains to expected user traffic by developing a new, automated timetabling system• Stimulate growth of passenger railway transport, without investing additional billions of euros in new infrastructure• Improve on-time performance – percentage of trains that arrive at the 35 main stations with delay of less than three minutes• Reduce greenhouse gas emissions by reducing automobile travel 19

Train Timetabling Features of the Timetabling Problem • Trade-off between number of trains and on-time performance • Frequency of arrivals • Connections with Germany and Belgium • Cyclic scheduling – departures at same minute in every hour • Line system – Origin, final destination, frequency, stopping pattern • Timetable – Planned departure and arrival event for each train at each station served • Station routing – Inbound track, platform, outbound track • Crew rostering – Assign duties (consecutive trips from base to return) to crew members 20

Train Timetabling The Bottom Line • Operating efficiency has improved 5 - 10% percent • Additional profit of €10 million annually • In 2007, on-time performance reached a record high 87% up from 84.8% in 2005 and 2006 21

Train Timetabling What the Customer Says • “Netherlands Railways used IBM ILOG CPLEX to enhance its rolling stock allocation planning for better resource utilisation, resulting in a cost savings of over 20 million Euros annually. CPLEX is also used for time table scheduling and has helped improve on-time performance for more than a million daily rail passengers” • “IBM ILOGs optimisation technology helped us to achieve our primary goal of improving service but has also had a significant impact on our bottom line in terms of operational cost savings. This resulted in winning the INFORMS 2008 Franz Edelman Award for outstanding practice in Operations Research and Management Science” – Mr. Wim Fabries, Head of Logistics 22

Case Study Operations Planning: Dispatching Power Generators 23

Dispatching Power Generators The Customer • Red Eléctrica de España • Operates the Spanish national power grid In 2008 • Managed production and delivery of 280 billion kWh • Served a peak demand of 43 thousand MW • Handled 95 thousand MW of generating capacity, including 17 GW of hydroelectric and 16 of wind • Operated 35 thousand km of high-voltage transmission lines • €1.1 billion in revenue (net turnover), €286 million in after-tax profit 24

Dispatching Power Generators The Business Problem Schedule the dispatch of power generators (when to start and how much to produce each hour) in order to minimize the cost of electricity to consumers• Power supply and demand must match at all times• Wind production is highly variable• Significant cost to starting up a combustion generator• Seasonal limits on hydroelectric generation• Changing market structure made old methods of dispatch obsolete 25

Dispatching Power Generators 26

Dispatching Power Generators The Bottom Line • REE reduced production costs by between €50,000 and €100,000 per day ($70-$140 thousand) • REE has reduced its carbon emissions by approximately 100,000 tons of CO2 annually • The ILOG project required an investment of €300,000 but no new acquisition of hardware or any changes to the existing platform 27

Dispatching Power Generators What the Customer Says • “The methodology applied until now … was an interactive methodology, which did not guarantee an optimum solution. There were many difficulties in the smaller systems and it was hard to find the most viable solution. Thanks to the new methodology, we have resolved this type of problem.” • “From a user viewpoint, it has brought greater trust in the solution and a significant reduction in planning time required by users. In parallel with this, from a development and maintenance viewpoint, there has been a significant reduction in associated costs, as well as in the duration of the processes.” – Mr. Mustafa Pezic, REE Project Director 28

Case Study Donor Pairing: Organ Transplants 29

Organ Transplants The Customer • Non-profit organization • Focuses on live donations of a particular organ • Arranges organ swaps among incompatible donors and recipients • Has 70 transplant program members in 25 states of the US • Began enrolling transplant centers in October 2006 30

Organ Transplants The Business Problem More than 83,000 people in America are waiting for a transplant of this organ; about 12 die every day due to lack of donors. Many patients have someone who is willing to donate, but because of immune system or blood type incompatibilities, they are not able to give the organ to their significant other. Paired donation matches one incompatible donor/recipient pair to another in the same situation. The donor of the first pair gives to the recipient of the second, and vice versa. Longer chains of donations are also possible. The transplants do not have to be performed simultaneously. 31

Organ Transplants Paired Donation Donor A X Recipient A Donor B Recipient B X 32

Organ Transplants Solution Features• Organ compatibility measured on a point scale – HLA Match, Blood Group, Pediatric Case, Distance, Recipient vs. Donor Age Disparity, Wait time, …• Optimization considers all potential matches and chains of matches• Find the chains with the largest total quality points• Can consider altruistic donation (donor has no significant other as recipient) 33

Organ Transplants The Bottom Line• In first 18 months, performed 28 paired donations• In a recent month, found potential matches for 21 patients in 10 transplant centers• July 2007 – March 2008 10 donation chain with altruistic initial donor• This application featured on NBC’s TechNow program• In 2009, IBM added this organization to its list for the Employee Charitable Contributions Campaign 34

Organ Transplants What the Customer Says • “The number of possible solutions are like the grains of sand on the seashore, and what IBM ILOG’s CPLEX does is that helps us to identify the best possible solution amongst all those solutions.” – Founder • “It once took 3 weeks to match a kidney donor with a patient, with this software, it takes seconds to save lives and money by bringing people together.” – NBC Technology Reporter 35

IBM ILOG Optimization Can Help Your Business Run More Efficiently 3 Ways to Use IBM ILOG Products and Services • Build an optimization application yourself – If you have the OR experts, IBM ILOG technology will make their development faster and more reliable • Use a packaged solution for your business – IBM and its ISV partners have configurable applications for many kinds of business issues – Supply chain, logistics, transportation, energy, financial services,… • Let IBM help you build it – IBM Professional Services can provide the expertise to build custom applications that meet your business requirements 36

The Value IBM ILOG Optimization Technology • Better decisions Get better performance for lower cost Optimization produces quantifiable benefits on your bottom line • Faster decisions Increases your speed of response Allow your managers and planners to focus on critical complexities • Faster, lower cost development and maintenance Code and validate your application with less time and effort Maintain and upgrade your system easier and more reliably • Turn information into action Leverage your investment in enterprise information technology 37

For More InformationDr. Jeremy BloomProduct Marketing Manager, ILOG Optimization408-956-3104bloomj@us.ibm.comhttp://www- 01.ibm.com/software/websphere/products/optimization/http://www-01.ibm.com/software/websphere/products/sca/ 38

Build Online Revenue IBM Innovation Center May 25, 2011 Gene Hoffman, CEO - FounderConfidential - Prepared by Vindicia January 2011

Vindicia ClientsConfidential - Prepared by Vindicia January 2011

The Vindicia Advantage Customer Acquisition Customer Retention Operational Excellence Complete Solution Fraud Screen & InternationalChargeback Management Customer Data Ownership Payments, Language, Tax Calculation Business Analytics & Process Confidential - Prepared by Vindicia January 2011

Customers Architecture Overview Provider Customer Website Entitlement Service Reporting / Balance Product / Mgmt. Email & Analytics Service Server SOAP API with multiple client librariesProducts & Product Fraud Customer Reporting & Global Access / AccountPromotions Balances Mgmt. Mgmt. Analytics Taxation Customer Secure Card Communication Transaction Tokenization Processing Gateway Merchant processors, PayPal, alternative payment providers, etc.Confidential - Prepared by Vindicia January 2011

B2C Merchants with recurring customer relationships What monetary metrics matter? Why payments fail? How to ensure the highest level of renewals / churn? How to optimize product / pricing mix? • ACLV – Average Customer Life Time Value • Rating (usage based metering) • Chargeback percentage • Geo location billing (AVS) • Auth to deposit ratio • Churn by any attribute • Method of Payment • CMRR - Committed Client Monthly Minimum (SaaS) • Presentment & Settlement Currency • Duration of Subscription (Term, Block or Indefinite) • Credit / Debit Mix • Number of / types of Subscriptions (Products) • Decline code analysis (Hard / Soft)Confidential - Prepared by Vindicia January 2011

Sample Clients Data from April 2011Confidential - Prepared by Vindicia January 2011

Vindicia Client Revenue Lift (April 2011) In 2010 CashBox increased client subscription revenue $50M+ * understates the compound annual effect ** Blended average of multiple price pointsConfidential - Prepared by Vindicia January 2011

Reporting & DashboardsOver 24 out of the box reportsReporting is customizableNew Report development All reporting data available via CSVdownload or direct API integrationConfidential - Prepared by Vindicia January 2011

Cohort Analysis For ACLV Average ~ 8mo Vindicia Client With Monthly SubscriptionsConfidential - Prepared by Vindicia January 2011

IBM Solutions that complement Clients who want to build online revenue Cognos 10 Business Intelligence and Financial Performance Management IBM SPSS Predictive Analysis Unica & Coremetrics: Marketing software solutions, offers web analytics, campaign management, website personalization, email, marketing resource IBM WebSphere Commerce 7: customer interaction platform for cross-channel and online commerceConfidential - Prepared by Vindicia January 2011

Questions & Answer – visit www.vindicia.comConfidential - Prepared by Vindicia January 2011

Discover the value in IBM Business Analytics

by Daryl Pereira

Accessibility

Categories

Upload Details

Usage Rights

2 Embeds 182

Statistics

Discover the value in IBM Business Analytics Presentation Transcript

https://www.ibm.com	181
http://webcache.googleusercontent.com	1