“The New Intelligentsia” A look at the Landscape in Analytics

1. Ivan O’Dwyer IBM VC Group 15th December 2009 “The New Intelligentsia” A look at the Landscape in Analytics

2. 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

3. Stochastic Optimization Optimization Predictive modeling What will happen next if ? Forecasting What if these trends continue? Competitive Advantage 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? Degree of Complexity Advanced Analytics Focuses on the Prescriptive & Predictive How can we achieve the best outcome including the effects of variability? Prescriptive How can we achieve the best outcome? Predictive Descriptive Based on: Competing on Analytics, Davenport and Harris, 2007

4. 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. What the VC’s are telling us ….

6. 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) -- PivotLink, Birst, Oco, etc.

7. 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 PivotLink, Host Analytics) • New kinds of data aggregation and self-service models emerging (public or private cloud concepts). • AaaS infrastructures can drive/enable more coherent, uniform data models. • Opportunities seen in producing industry-levelinsights and benchmarks • Many start-ups focusing on on-line business analytics, especially e-commerce-related. • Combination of traditional analytics capability with SaaS-type delivery models (e.g., RichRelevance 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 Mp3 album

8. 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 (CarrierIQ), 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. 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. Horizontal Applications Security / Surveillance (Video Analytics) CRM Analytics Energy Analytics & Optimization for Enterprise Risk & Fraud Analytics & Compliance Call Miner E-Glue Enkata HubSpot KXEN Lattice Engines Xtract Austin Logistics Clickfox Agent Video Intelligence AxonX Cernium Intellio Mate Intelligent Video OmniPerception VideoIQ Vidient Systems Clear Standards Optimal Technologies Intl Planetmetrics Prenova Integral Analytics Tendril GreenBox Energy Hub 41st Parameter E-Glue eBureau Guardian Analytics ID Analytics Texert

11. Vertical Applications Financial Services & Insurance Analytics Media & Entertainment Advertising & Other Analytics Retail Analytics Agilence Alpha Bay Dacps Software IntelliQ RivalWatch Searchandise Commerce Austin Logistics Derivix DFA Capital Management Eagle Eye Analytics FinAnalytica Firm58 Mantara Razorsight Reval Valen Technologies 33Across Anvato Clickable Crowd Science Digitalsmiths MediaBank Meteor Solutions Teracent TubeMogul Visible Measures E-Commerce Analytics 7 Billion People Bazaarvoice Infopia Marketlive Healthcare / Pharma Analytics Casenet DecisionView HealthDataInsights Health Monitoring Systems Logical Images MedeFinance Medical Insight Supply Chain Optimization Axxom Software Delfoi RockBlocks Group RollStream ShipLogix

12. Examples of VC backed emerging companies

13. Macro Trends in Data andSensemaking Analytics

14. Agenda • Next wave content-centric web Apps---Massive Mashups • Semantic Web • Text Analytics, Sentiment Analysis, • Stream Processing Engines • Space Time Travel Data – The SuperFood of Analytics • Context Engines • Sensemaking Infrastructure • Data finding Data …..Relevance finding the User

15. 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 = 1,000,000,000,000,000 GB.

16. 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 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 New Class of Analytic Applications to unlock new insight by leveraging Unstructured Information Enterprises need to leverage the broader internet for all relevant content • Cross division • Ecosystem • User generated • (News) Feeds • mySpace • Facebook • Twitter • Audio/Video • Wikis • ...

17. Macro Trends

18. 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 InfoSphere 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

19. Fastest Sensemaking First Domains for Competitive Advantage Human Capital Tools Data

20. All Digital Data Growing Dumber Sensemaking Algorithms Trend: Organizations are Getting Dumber Computing Power Growth Time

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

22. “Remembering in a database (persistent) how things relate to each other (context).” Introducing … Persistent Context

23. Observations Structured Unstructured Audio/Video Geospatial Biometrics Etc. CONSUMERS Operational Systems Business Intelligence Data Marts Data Mining Pattern Discovery Predictive Modeling Case Management Visualization Etc. Feature Extraction & Classification Context Analysis Relevance Detection Publish Persistent Context Questions Search, Discovery, Context Requests Etc. Answers to questions Respond Notice Sensemaking

24. Privacy

25. “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. 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!

27. IBM VCGroup VF Ventures ? IBM Research Entity Analytics VF Research A Suggestion….. Organise a Research Symposium to examine in detail research areas of mutual interest and benefit in Smart Analytics ……aim for Q1 event… Output of event would be collaboration projects to be run by the VCC / SPTC/ CoE’s Service Innovation Service Creation ISV’s? ISV’s? Service Integration VCC CoE

28. ? ? ? Thank You ! Ivan O’Dwyer IBM VCG ivan.odwyer@ie.ibm.com