April 25, 2012 The Three R’s Are Old School – Now It Is All About Volume, Velocity & Variety Peter Guest Alberta Public Sector Client Technical Advisor pguest@ca.ibm.com

Why analytics now? Because we can…

The world is changing and becoming more…

Big Data = huge opportunity to generate new outcomes… Steve Mills 03/20/12 Big Data = huge opportunity to generate new outcomes… Volume Velocity Variety Grab the <5% of tweets of interest in 12 terabytes of tweets created everyday Identify potential fraud in five million trade events per second Identify events of interest from hundreds of surveillance cameras Predict power consumption using 350 billion meter readings from 20 million households annually. Analyze up to 500 million Call Detail Records per day for revenue assurance Harvest insight from the 80% of new data growth coming from email, documents, images, video and audio However, organizations are challenged to achieve the potential insight from big data with traditional approaches. For US equity electronic trading brokerage, handling the speed of the market is of critical importance because latency impedes a broker’s ability to provide best execution. In 2008, 16% of all US institutional equity commissions are exposed to latency risk, totaling $2B in revenue. As in the Indy 500, the value of time for a trading desk is decidedly non-linear. TABB Group estimates that if a broker’s electronic trading platform is 5 milliseconds behind the competition, it could lose at least 1% of its flow; that’s $4 million in revenues per millisecond. Up to 10 milliseconds of latency could result in a 10% drop in revenues. From there it gets worse. If a broker is 100 milliseconds slower than the fastest broker, it may as well shut down its FIX engine and become a floor broker. http://www.tabbgroup.com/PublicationDetail.aspx?PublicationID=346 We all know that Big Data is “big”, but I think it’s always helpful to ground us in numbers. As much as I read about this, I’m still amazed when I come across a new piece of data. Look at that cell phone number – 5B cell phones in 2010, only 12% were smart phone. Obviously all phones generate data that can be analyzed, but smart phones even more so. The CAGR on smart phones is usage is forecasted to be 20%. Sensors are another amazing one. 30% grown on 30M networked sensors. We’ve talked to clients with vast networks of sensors, but only use the data off 10% of them. The installed the sensors because they know the data is valuable, they just don’t know how to handle that data. I’m sure you can think of similar examples in your business or industry. 120320 Mills Smarter Analytics Event NYC 4 4

Why Business Analytics Matter The Need for Analytics is Pervasive Across Business and Industry The healthcare industry spends $250 - $300 billion on healthcare fraud, per year. In the US alone this is a $650 million per day problem.1 One rogue trader at a leading global financial services firm created $2 billion worth of losses, almost bankrupting the company. $93 billion in total sales is missed each year because retailers don’t have the right products in stock to meet customer demand. 5 billion global subscribers in the telco industry are demanding unique and personalized offerings that match their individual lifestyles.2 Source: 1.Harvard, Harvard Business Review, April 2010. 2,IBM Institute for Business Value, The Global CFO Study, 2010.

Why Business Analytics Now Market Forces are Driving New Client Needs The emergence of Big Data – enabled by cost effective storage and processing of data The shift of power to the consumer Pressure to do more with less

BAO Competencies and Offerings Steve Mills 03/20/12 BAO Competencies and Offerings Business Intelligence & Performance Management Enterprise Information Management Enterprise Content Management Advanced Analytics and Optimization BAO Strategy BAO Strategy and Roadmap BAO Process Improvement BAO Governance Dashboards & Scorecards Planning, Budgeting, & Forecasting Business Analytics & Reporting Advanced Analytics Analytic Applications Predictive Modeling Business Optimization Visualization Data Integration Data Quality Data Architecture Master Data Management Document & Records Management Web 2.0 / Web Content Management Digital Asset & Rights Management Archiving & Record Management 120320 Mills Smarter Analytics Event NYC

The BAO Reference Architecture Overview Steve Mills 03/20/12 The BAO Reference Architecture Overview Content Management Master Data Management Data Integration Data Repositories BI / Performance Monitoring Advanced Analytics Access Sources Enterprise Apps Unstructured Data Stores Informational External Web Structured Data Stores Devices Master / Reference Data Document Management Services Federation Ingestion Base Services Records Extraction Reference Data Management Operational Orchestration Components Data Load Components CRUD Transactional Operational Data Store Data Warehouse Time Persistent Repository Dimensional Layer Master Data Store Content Store Staging Area Reporting Planning, Forecasting, Budgeting Scorecards Guided Analysis Dashboards Querying Monitoring Simulation Optimization Visualization Predictive Analytics Data Mining Text Analytics BATCH Extract / Subscribe REALTIME Web / Services Data Quality Portal Device Transform Composite Application Load / Publish Collaborative Application TRANSACTIONAL Productivity Application Enterprise Search Business Unit Application Business Process Management Service Management Information Governance Collaboration Security, Privacy & Compliance Transport & Delivery Infrastructure 120320 Mills Smarter Analytics Event NYC