Ivan O Dwyer IBM VC Group 15 th December

Agenda § Why the spotlight on analytics? § What are the VC’s are telling us? A selection of players § What are the macro technology Trends in Analytics and “Sensemaking” Research § Privacy and Data Protection § Summary § Suggestion 2 © 2009 IBM Corporation

Advanced Analytics Focuses on the Prescriptive & Predictive Stochastic Optimization How can we achieve the best outcome including the effects of variability? Competitive Advantage Optimization How can we achieve the best outcome? Predictive modeling What will happen next if ? Forecasting Prescriptive What if these trends continue? Predictive Simulation What could happen…. ? Alerts What actions are needed? Query/drill down What exactly is the problem? Ad hoc reporting How many, how often, where? Standard Reporting What happened? Descriptive Degree of Complexity Based on: Competing on Analytics, Davenport and Harris, 2007 3 © 2009 IBM Corporation

It’s all about competition! “Every millisecond gained in our program trading applications is worth $100 million a year. ” Goldman Sachs, 2007 * Source Automated Trader Magazine 2007 5 © 2009 IBM Corporation

What the VC’s are telling us …. 6 © 2009 IBM Corporation

What’s Motivating VC Investment & New Business Models ? § On premise BI is complex, expensive – requires expensive consulting; long implementation cycles; inflexible; limited to large clients who can afford § SMBs / Mid-market have same need for analytics especially in tight economy § Increasing need for non-IT experts to implement simple analytics – easy to build, easy to use § First generation of startup innovation was data warehouse and management appliances – Netezza, Teradata, Greenplum § Second generation of startup innovation is delivering analytics – as-a-service (Saas BI, On Demand BI) -- Pivot. Link, Birst, Oco, etc. 7 © 2009 IBM Corporation

VC Trends - What are we seeing? § Analytics-as-a-service emerging as a clear and compelling model – Companies across the capability spectrum (including professional services) – Ecosystems – Cloud integrators embracing cloud BI platforms (e. g. , Appirio with Pivot. Link, Host Analytics) – New kinds of data aggregation and self-service models emerging (public or private cloud concepts). – Aaa. S infrastructures can drive/enable more coherent, uniform data models. § Opportunities seen in producing industry-level insights and benchmarks § Many start-ups focusing on on-line business analytics, especially e-commerce-related. – Combination of traditional analytics capability with Saa. S-type delivery models (e. g. , Rich. Relevance working with enterprise-class e-commerce sites like Sears. com, Walmart. com) § Open source tools growing in importance – e. g. , Talend, Pentaho, Cloudera (Hadoop-based) – Across the capability spectrum – Often with cloud-type infrastructure, leveraging services-focused models § Unstructured and independent of data warehousing (80% of all NEW Data is Unstructured) – Potential for a whole new range of applications on the “right” engine – Patient drug interaction and efficacy of trials over time – Every time you skip a track on a CD or Mp 3 album 8 © 2009 IBM Corporation

What VC’s are telling us (continued) § Edge device analytics as enabler for new applications. – Enable distributed devices to capture and send data to central analytics engines, and/or to perform analytics at the edge to send higher-level or highly-enriched information back to central location. – Examples – SW on mobile devices (Carrier. IQ), security systems (many video systems), smart building/home management (Tendril, etc. ). § Advanced text analytics poised to bear fruit – Strong area of continued investment. – Companies structured as enabling components with which broader solutions can be constructed (sentiment, certain kinds of patterns, etc. ) § Analytics-based capabilities being targeted to create “Smarter Networks” via centralized and edge-assisted analytics. – Current mobile networks lack intelligence or consistent data sets to properly monitor, correct, and improve them over time. – Investment targeted to produce new sources of data, data integration, and analytics that will allow for improvement of telecom network performance and greater end-user satisfaction. 9 © 2009 IBM Corporation

Analytics-As-A-Service Startups § New class of BI startups emerging that are providing end to end analytics as a service: data integration and loading, analytics platform and application § Software is fundamentally simpler and easier to deploy for SMB § Initially targeting the gap between enterprise analytics and end-user (desktop) analytics primarily implemented on Excel; Converting Excel users first. § Initial sweet spot: analytics applications for sales and marketing § For SMBs, rapid time to value – weeks / months vs. years; potentially reduces up to 70% of overall cost of BI * Diagram “Birst Brings Big BI to Business”, Richard Hackathorn, July 10, 2009, Boulder BI Brain Trust Blog * “On Demand Business Intelligence Takes Off, ” Information Management, Brad Peters, July 7, 2009” (refers to startups implementing integrated Saas BI deployments) 10 © 2009 IBM Corporation

Horizontal Applications Risk & Fraud Analytics & Compliance 41 st Parameter E-Glue e. Bureau Guardian Analytics ID Analytics Texert 11 Security / Surveillance (Video Analytics) Agent Video Intelligence Axon. X Cernium Intellio Mate Intelligent Video Omni. Perception Video. IQ Vidient Systems CRM Analytics Call Miner E-Glue Enkata Hub. Spot KXEN Lattice Engines Xtract Austin Logistics Clickfox Energy Analytics & Optimization for Enterprise Clear Standards Optimal Technologies Intl Planetmetrics Prenova Integral Analytics Tendril Green. Box Energy Hub © 2009 IBM Corporation

Vertical Applications Financial Services & Insurance Analytics Austin Logistics Derivix DFA Capital Management Eagle Eye Analytics Fin. Analytica Firm 58 Mantara Razorsight Reval Valen Technologies Healthcare / Pharma Analytics Casenet Decision. View Health. Data. Insights Health Monitoring Systems Logical Images Mede. Finance Medical Insight 12 Media & Entertainment Advertising & Other Analytics 33 Across Anvato Clickable Crowd Science Digitalsmiths Media. Bank Meteor Solutions Teracent Tube. Mogul Visible Measures Retail Analytics Agilence Alpha Bay Dacps Software Intelli. Q Rival. Watch Searchandise Commerce E-Commerce Analytics 7 Billion People Bazaarvoice Infopia Marketlive Supply Chain Optimization Axxom Software Delfoi Rock. Blocks Group Roll. Stream Ship. Logix © 2009 IBM Corporation

Examples of VC backed emerging companies 13 © 2009 IBM Corporation

Macro Trends in Data and Sensemaking Analytics 14 © 2009 IBM Corporation

Agenda § Next wave content-centric web Apps---Massive Mashups § Semantic Web § Text Analytics, Sentiment Analysis, § Stream Processing Engines § Space Time Travel Data – The Super. Food of Analytics § Context Engines § Sensemaking Infrastructure § Data finding Data …. . Relevance finding the User 15 © 2009 IBM Corporation

A Yottabyte? § What is a Yottabyte? § 1000 GB = 1 Terabyte (TB) 1000 TB = 1 Petabyte (PB) 1000 PB = 1 Exabyte (EB) 1000 EB = 1 Zettabyte (ZB) 1000 ZB = 1 Yottabyte (YB) In other words, a Yottabyte = 1, 000, 000 GB. 16 © 2009 IBM Corporation

New Class of Analytic Applications to unlock new insight by leveraging Unstructured Information § Volume of data in enterprises is doubling approximately every three years (Forrester Research) – Includes structured and unstructured data, excludes rich media § This content is an untapped value for business insights & intelligence § § § 17 Databases are great when you know what you’re looking for - not so if you’re attempting to discover business opportunities Frequency of Change Increasing - an enterprise’s ability to capture, warehouse and collect insights from massive amounts of data - quickly & easily - will be disruptive Success will be measured by enterprises that can slice & dice data into consumable, remixable content for their business ecosystem Enterprises need to leverage the broader internet for all relevant content • • • Cross division Ecosystem User generated (News) Feeds my. Space Facebook Twitter Audio/Video Wikis. . . © 2009 IBM Corporation

What is Stream Processing? § Stream is all about…… – Very complex analytics… on – Incredible volumes and variety of streaming data. . With – Sub-millisecond latency and response time. . While – Data is still in motion… and – Runs on a wide variety of Hardware Platforms… to – Provide organizations with a very flexible yet extremely powerful solution to remain highly competitive and productive 19 Info. Sphere Streams is a result of an ongoing software research project at IBM Research known as System S. The System S research is ongoing and will result in additional enhancements to the Streams Platform © 2009 IBM Corporation

Domains for Competitive Advantage Human Capital Fastest Sensemaking First Data 20 Tools © 2009

Trend: Organizations are Getting Dumber Computing Power Growth All Digital Data Growing Dumber Sensemaking

The Way Forward Computing Power Growth All Digital Data Context Engines Sensemaking Algorithms Time

Introducing … Persistent Context “Remembering in a database (persistent) how things relate to each

Sensemaking Feature Extraction & Classification Context Analysis Relevance Detection Persistent Context Notice 24 Publish

“In the Future Everybody will have Privacy for 15 minutes” § Privacy and Space with respect to Space-Time. Travel Data and your mobile § Privacy by Design – The 7 Principles § UK Data Protection Act is nearly 10 yrs old§ To Anonymize or not to Anonymize that is the question. § If we get Privacy right huge benefit accrues § If we don’t get it right ……. § “Privacy A Manifesto- Wolfgang Sofsky Diagram “Birst Brings Big BI to Business”, Richard Hackathorn, July 10, 2009, Boulder BI Brain Trust Blog 26 © 2009 IBM Corporation

A Summary § The convergence of business imperatives, the coming of age of technologies like in line Stream, Semantic Web, massive mahsups, and elastic , price optimized cloud delivery all point to a very exciting few years ahead in analytics. Context accumulation technology is particularly exciting. § Investment dollars are beginning to flow to Analytics-as–a-Service model Startups. We are watching developments here very closely. Better Data models may result from these new types of business models and the effect of social collaboration around them § In Telco Industry analytics around CVM, CEM, SNA , segmentation are beginning to really prove their value but analytics can also be put to good use to operationally optimize many different aspects of Telco Networks and internal processes. § And they said the internet meant location wouldn’t matter anymore! Space- Time –Travel Data is when mashed up with tertiary data will enable a whole range of optimization applications § Privacy remains a concern , but clearly not for everyone. More progress needs to be(and is being) made on the anonymization of analytics. If a company can achieve same results with anonymization than why wouldn’t it make anonomize all of its analytics, and potentially gain a brand/ competitive advantage in doing so… § Data is the only resource mankind has where the act of consumption creates more of the resource. § In the future the data will find that data and the relevance will find the user! 28 © 2009 IBM Corporation

A Suggestion…. . Organise a Research Symposium to examine in detail research areas of mutual interest and benefit in Smart Analytics ……aim for Q 1 event… Output of event would be collaboration projects to be run by the VCC / SPTC/ Service Innovation Co. E’s Service Creation IBM VCGroup VF Ventures ISV’s ? IBM Research Entity Analytics 29 ISV’s ? ? VCC Co. E VF Research Service Integration © 2009 IBM Corporation