How the Information Tidal Wave is

Transcription

1 How the Information Tidal Wave is Di Driving i New Business Opportunities Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

2 Agenda Overview of Big Data Where the industry is going and client use case examples Opportunities with Big Data Where Clients are today and next steps 2 HP Find Confidential more Vertica information Portal/Marketplace

3 Overview of Big Data Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

4 Overview of Big Data New Data Sources Many types Data Analyst Objective Competitive Advantage Efficiencies Etc. 4 HP Confidential Analytics Database Call Center Data Analytics Customer Transactions/Lists Inventories Purchases Web Logs Sensor/Machine Data from Web LOB Data EDW Reports and Information Best Selling Product Best Customer Most Returned Product Path to Purchase Forward-looking Information Where are the markets? Many other questions Information Consumers

5 Big Data, Fast Data, Dark Data it is time for you to capitalize on Big Data Every 60 seconds 98,000+ tweets 695,000 status updates 11million instant messages 698,445 Google searches 168 million+ s sent 1,820TB of data created 217 new mobile web users 5 HP Confidential

6 A Real-World Example: Sensor data collected from US commercial jet engines during 1 year 20 TB , terabytes of information per engine every hour twin-engine Boeing 737 Average duration for US flights in hours # of commercial flights in the sky in the United States on any given day days in a year 1,041,600,500 TB 6 HP Confidential

7 Sentiment Analysis What does it tell us? 7 HP Confidential

8 Where is the industry going and client use case examples Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

9 Where s the industry going? Yesterday s s data warehouse and analytic infrastructure Proprietary Expensive Centralized, Monolithic Process Laden Batch Summary Slow Today s data warehouse and analytic infrastructure Industry Standard Cost Effective Distributed Self Service (Easy to Use) Real Time Raw Data, Deep Granularity Agile DAYS HOURS SECONDS 9 HP Confidential

10 Challengers.

11 HAVEn the #1 Big Data platform Approaching Big Data with an intelligent software architecture HAVEn Hadoop/ Autonomy Vertica Enterprise n Apps HDFS IDOL Security Catalogue massive volumes of distributed data Process and index all information Analyze at extreme scale in real-time Collect & unify machine data Powering HP Software + your apps 11 Social media Video Audio Texts Mobile Transactional data Documents IT/OT Search engine Images HP Confidential

12 The HP Big Data Platform 12 HP Confidential

13 Opportunities for clients to utilize Big Data w/ existing EDW Bringing big data analytics to legacy data warehouses and dark data Lines of business Data sources Call center Suppliers Point of sale Legacy data warehouse Reporting and analytics Operational analytics Finance Human resources Sales/CRM Operations Machine data Customer sentiment ( s, social media forums) Hadoop HAVEn Dashboards Cost effective Real-time Deep granularity Fast 13 HP Copyright Confidential 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

14 HP Big Data Platform Use Cases Customer: Bank of America Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

15 Proving the value of Vertica - Use Cases Use Case Vl Value Customers EDW modernization Customer analytics Drive down the cost of enterprise data warehouses (Oracle and Teradata), dramatically reduce footprint (10-30x data per server), and infinitely and easily scale Improve effectiveness of Web properties and increase conversions and sales Target individuals for upsells and cross-sells based on actual purchase patterns Understand how customers use different banking programs to develop targeted campaigns that increase customer satisfaction, increase profits, and reduce churn Sensor data analytics Operations l ti Proactively manage maintenance, improve reliability, reduce unplanned service work Offer drivers better rates based on driving behavior with usage-based insurance Offer competitive differentiation and improve retention with smart metering Optimize business and reduce expenses by identifying costs, revenue leakage analytics Better segment customers to provide more targeted marketing spend Improve operational efficiencies by predicting capacity issues Patient analytics Optimize processes to speed delivery of care and eliminate waste and error Predict healthcare and utilization costs and track comparative benchmarks Improve effectiveness of health systems, drive deeper insights into their systems and patient treatments and improve effectiveness of physicians and hospitals they support 15 HP Confidential

16 Use Case: Real-time Credit Scoring Customer: Bank of America Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

17 Real-time Credit Scoring Bank of America: Billion USD (2013), 2 nd largest Bank of US Credit Card Applications SLAs cannot be met with existing Data Warehouse solution. 17 Bank of America Struggling to respond timely (2-3 mins) to applications HP Confidential Affinity Card program at Retail Outlets (typically 2-3 mins response rate is expected)

18 Real-time Credit Scoring Much faster with Vertica Bank of famerica from 3 Hours 45 Mins. Approvals for Applications to 59.7 Seconds Running 1.5 Petabytes on Vertica & moved Global Markets & Risk technology processing to Vertica 18 HP Confidential

19 Why Vertica? Real-time Credit Scoring 200x $ 14M 80% Faster response to Credit Card Applications by faster Customer Analytics Significant improvement in acquiring new customers with near real time analytics Savings by replacing MBNA s mainframe-- based DB2 solution with HP Vertica (after acquisition of MBNA by BofA). Faster Performance with 1/3 of Cost, HP Vertica on a 4 node cluster was 80% faster than a 12 node Netezza cluster at one-third the cost ($6 million for IBM vs. $2 million with HP). 19 HP Confidential

20 Use Case: EDW Modernization for Faster Credit ditrisk Assessment Customer: Credit Suisse Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

21 EDW Modernization for Faster Analytics Credit Suisse: 24 Billion CHF, operations in 50+ Countries Net Exposure risk? Regulators Solution Delivery & Implementation Roadmap Loans / X-actions Credit Suisse 3rd party Financial Inst. Quants & Biz. Ops. Solution Delivery & Implementation Roadmap 21 HP Confidential

22 Vertica for Faster Data Preparation & Analytics Data Preparation & Analytics Ad-Hoc Queries 22 HP Confidential

23 Why Vertica? Value of Vertica for Credit Risk Assessment Process 200% Performance Improvement Fastest data providing for Automated System Tests for Risk Assessment e & Enabling Intraday Risk Calculation 23 Better hedging, risk-taking mgmt., identify rogue traders, trade-book visibility, fraud mgmt., stress testing, portfolio health, etc. HP Confidential

24 Use Case: Cerner Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

25 Cerner Corporation Vertica helps to optimize Health Information Solutions Cerner Corporation s Millennium solution platform not only provides Electronic Health Records for health care providers, but also helps those providers optimize processes to speed the delivery of care and eliminate waste and error. Millennium+ delivers a new user experience that is fast, smart and easy. It provides personalized, intuitive and relevant clinical workflows via the desktop, tablet and smartphone. 25 HP Confidential

26 Challenge Cerner Corporation Cerner collects billions of RTMS (Response Time Measurement System ) records every month, analyzes them, and uses that information to address performance issues in Millennium that may impact care delivery. Cerner s legacy data warehouse solution was not able to process the volume of data as quickly as it needed to. We couldn t analyze the data fast enough to proactively optimize Millennium timer data at an optimal rate. There was just too much data to successfully process it as it was generated, 26 HP Confidential

27 Solution with Vertica Cerner Corporation Approach Cerner Corporation moved from an existing ggeneral-purpose p database IT Business to the HP Vertica Analytics Platform 20 mins <20 secs SLAs kept through more proactive management of Clients 6 million performance timers Millennium hosting environment 6000% improvement 450 simultaneous users 6 billion 10 billion records/month Ability to scale analytics capabilities as demand grows. User workflow analysis to improve efficiency and quality of patient care, supporting government regulations. 27 HP Confidential

28 Some Health Facts users would issue a query at 5 p.m. as they leave for the day, hoping they would have a result when they return at 8 a.m. the next morning. With HP Vertica, those query times are down to two or three minutes. Dan Woicke, director of Enterprise System Management at Cerner 28 HP Confidential

29 Use Case: Zynga Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

30 Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

31 Zynga Leveraging Big Data to make games more fun and social World s leading social game provider And growing rapidly web and mobile 3rd party games on the Zynga Platform 31 HP Confidential

32 The Challenge Three key metrics that drive the economics of social gaming Churn In this context, churn is defined as the loss rate of game players. Social gaming, because of its very nature, can have an extraordinarily high churn rate. Typical estimates are that, on average, social games have a churn rate of 50% per month meaning that half of the new players signing up today will be gone in a month. Viral Coefficient e The viral coefficient is a measure of how effective current game players are at drawing New players a key capability enabled by social network platforms. For example, if 100 Mafia Wars users are likely to cause five of their friends to sign up in a given month, that would be a viral coefficient of Revenue Per User Finally, there is expected revenue per user. This is an estimate of the lifetime revenue that a game player will generate, based on an estimate of monthly revenue per user and the churn. For example, if the average monthly revenue is $5 per user, and churn is 50%, the expected revenue can be estimated as ($5 (the first month) + $2.50 (the second month) + $1.25, etc.) or approximately $ HP Confidential

33 Time to get Smart! 33 HP Confidential

34 Zynga by Numbers Vertica Solution Users Game Data Server Data ~260 million MAUs ~60 million avg DAUs worldwide Vertica driven ~60 billion rows/day ~10TB daily semistructured data ~1.5PB source data Largest 230 2U nodes 13TB per day raw logs from server and app logs Vertica or Hadoop for archives 34 HP Confidential

35 Solution HP Zynga Graph Analysis Social Games have different social graph than social network platform itself. Improving these interactions by guiding players to communicate appropriately with these two different types of relationships helps to increase revenue, reduce churn, and increase virality. In other words, to make every aspect of the game more profitable by improving the player experience significantly. The first thing the Zynga team did was evaluate graph engines (dedicated software for graph analysis), however, none of the solutions they evaluated would operate at the necessary scale or performance. They quickly realized that Vertica would meet their needs, in the words of Dan McCaffrey Director of Analytics Engineering at Zynga, Vertica has MPP and scale solved. We can process data daily to produce an optimized graph. 35 HP Confidential

36 Use Case: Facebook Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

37 for Facebook s CIO, Tim Campos, to get on stage in Europe and declare that, "A partner like HP Vertica thinks like we do and is a key part of Facebook s big data capabilities, is one the best endorsements, err likes, that any modern IT infrastructure vendor could hope for. - Dana Gardner, Briefings Direct 37 HP Confidential

38 Boosting Mobile Advertising Profits with Big Facebook Data Revenue Impact 60% Increase in Revenue $ 1.8 Billion - Q3 Revenue The company said growth had been fuelled by advertising income, which leapt 66 per cent year-onyear. Q Facebook did not have any mobile advertising revenue 18 months ago. %49 of Revenue is Mobile Advertising Data analysis had a huge impact on the development of mobile advertising Ken Rudin - Head of Analytics, Facebook 38 HP Confidential

39 Technology Challenges Facebook Hadoop is just one of many Big Data technologies employed at Facebook. To answer a specific query, data is often pulled out of the warehouse and placed into a table so that it can be studied, he said. Mr. Rudin s team also built a search engine that indexes data in the warehouse. These are just some of many technologies that Facebook uses to manage and analyze information. Hadoop is not enough, he says. Ken Rudin - Head of Analytics, Facebook Technology & Data 300 PB Data Analysis Warehouse Hadoop 1000 node Cluster 500 TB / day 39 HP Confidential

40 Solution with HP Vertica Facebook in Production 6 queries take 1dayin Hadoop PoC with Exadata, Teradata and Vertica 3.5 PB Data on Vertica Exadata could not scale-up, Teradata too 6 querieses run in 1 minute 300 nodes x2 Vertica Cluster expensive Analysts are back on track with HP Vertica 40 HP Confidential

41 Data is incredibly important: it provides the opportunity to create new product enhancements, business insights, and a significant competitive advantage by leveraging the assets companies already have. At Facebook, we move incredibly fast. Its It's important for us to be able to handle massive amounts of data in a respectful way without compromising speed, which is why HP Vertica is such a perfect fit." The people side of this is really, really incredible, he stated. This is why we are excited to work with the Vertica team. They have meshed well with our culture. It is a tremendous partnership. Tim Campos, Facebook CIO HP Discover 2013 Barcelona Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

42 Where Clients are today and next steps Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

43 From Hindsight to Insight to Foresight Advanta age Predict Real-Time Proactive Optimized Decisions Decisions Predictive Modeling Com mpetitive Forecast Statistical Analysis and Forecasting Automatic Alerts & Actions Identify Exact Problem Degrees of Intelligence 43 HP Confidential

44 Sentiment Analysis What is going on outside? 44 HP Confidential

45 Typical Customer Evolution - Data has become a product Researching Keeping all of the internal Data Modernizing Data Warehouse Building Data Lake Buying data from the outside New Roles Emerge -Chief Data Officer -Director of Data -Data Product Manager 45 HP Confidential

46 Big Data Opportunities across industries and use cases Big data use cases are business-driven and cut across a wide range of industries & functions Finance Government Telecom Manufacturing Energy Healthcare Fraud detection Anti-money laundering Risk management Sentiment analysis Law enforcement Counter terrorism Traffic flow optimization Social CRM / network analysis Churn mitigation Brand monitoring Cross and Up sell Loyalty & promotion analysis Web application optimization Broadcast monitoring Churn prevention Advertising optimization Supply chain optimization Defect tracking RFID Correlation Warranty management Horizontal Use Cases Marketing campaign optimization Brand management Social media analytics Pricing optimization Internal risk assessment Customer behavior analysis Revenue assurance Weather forecasting Natural resource exploration Logistics optimization Drug development Scientific research Evidence based medicine Healthcare outcomes analysis Clickstream analysis Influencer analysis IT infrastructure analysis Legal discovery Equipment monitoring Enterprise search 46 HP Confidential Sources: IDC: Worldwide Big Data Technology and Services Forecast: Through 2015, Gartner: Big Data Drives Rapid Changes in Infrastructure and $232 Billion in IT Spending Through 2016

47 Opportunities for Clients to leverage Big Data What goes in? Raw Data What comes out? Meaningful Information Data Analyst 47 HP Confidential Analytics Database Call Center Data Analytics Customer Transactions/Lists Inventories Purchases Web Logs Sensor/Machine Data from Web LOB Data EDW Reports and Information Best Selling Product Best Customer Most Returned Product Path to Purchase Forward-looking Information Where are the markets? Many other questions Information Consumers

48 HP Portals Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

49 Resources Data Sheets, Solution Briefs Case Studies Customer Testimonials Videos & Recorded Webcasts Upcoming Webinars Training Marketplace! 49 HP Confidential

50 MyVertica Community 50 Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

51 Let s have a Big Data discussion about your business More than 2500 customers & OEMs in multiple industries are finding answers Promotional Testing Behavior Analytics Claims Analyses Click Stream Analyses Patient Records Analyses Network Analyses Clinical i l data Analyses Fraud Monitoring Financial Tracking Tick data back-testing Customer Analytics Compliance Testing Loyalty Analysis Campaign Management com/customers/case-studiesstudies 51 Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

52 Thank you! Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

53 Back-up Slides Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

54 Management Console HP Vertica Database management tool Provides unified view of HP Vertica cluster Create/import/manage/monitor multiple clusters and databases Manage user info and monitor their activity on MC Configure database parameters and user settings dynamically View dynamic metrics about your database cluster Perform query level performance analysis Access by pointing browser to: 54 HP Confidential

55 From Hindsight to Insight to Foresight Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

56 HP Vertica Competitive Analysis Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

57 The players in the past the PLAYER OF THE FUTURE Yesterday s s data warehouse and analytic infrastructure Proprietary Expensive Centralized, Monolithic Process Laden Batch Summary Slow Today s data warehouse and analytic infrastructure Industry Standard Cost Effective Distributed Self Service (Easy to Use) Real Time Raw Data, Deep Granularity Agile DAYS HOURS SECONDS 57 HP Confidential

58 How Did We Win? Speed, Lower TCO, Scale, Openness! Superior Performance swapped out EMC GreenPlum 80% Faster at 1/3 Cost displaced IBM twice! Query time reduced by 5 hours to 5 minutes Lower TCO with commodity hardware support 30% reduction in operation costs Knocked out incumbent Oracle! Proven scalability with less RAM good bye ParAccel! Ease of use, scalability without massive PS engagement beat Teradata Aster, among others

59 Use Case #1: Deriving maximum value from your Enterprise data warehouse Drive down cost with the HP Vertica Analytics Platform

60 Use case #2: Customer analytics Customer insights with the HP Vertica Analytics Platform Online and mobile Improve effectiveness of Web properties and increase conversions and sales Retail Target individuals for upsells and cross-sells based on actual purchase patterns (not guess work) Financial services Understand how customers use different banking programs to develop targeted campaigns that increase customer satisfaction, increase profits, and reduce churn Using the HP Vertica Analytics Platform as your analytics engine, you can turn your mountains of raw data into a complete understanding of your customers, allowing you to improve the customer experience and your bottom line. Collect, manage, and analyze massive amounts of customer data from disparate sources, including Web logs, third-party analytics tools, social media, and traditional CRM and customer records from enterprise systems.

61 Use case #3: Operations analytics From Big Data to knowledge with the HP Vertica Analytics Platform Optimize business and reduce expenses by identifying costs and revenue leakages Better segment customers to provide more targeted marketing spend with insight to predict churn, cross-sell opportunities, and the quality of customer experience and customer value Improve operational efficiencies by predicting capacity issues and impact of a new service launch With the HP Vertica Analytics Platform, your organization can derive benefits relating to capacity management, performance, scalability, and availability. You can achieve all of these benefits and much more with the proven HP Vertica Analytics Platform as the core operations data analytics foundation. Analyze and make informed decisions in near real time with unparalleled efficiency, performance, and scalability.

62 Use case #4: Sensor data analytics Unlocking the massive potential of sensor data with the HP Vertica Analytics Platform Predictive maintenance Proactively manage maintenance, improve reliability, reduce unplanned service work, and mitigate t risk Usage-based insurance Offer drivers better rates based on their actual driving behavior Smart metering Offer competitive differentiation and improve retention by enabling customers to monitor and view consumption Manage and analyze massive volumes of sensor data to predict and prevent operational issues and reduce service costs, improve customer satisfaction by extending operational uptime, and bring revenue-generating machine to machine (M2M) solutions to market. With the proven HP Vertica Analytics Platform as the core sensor data analytics foundation, your organization can achieve all of these benefits and much more.

63 Use case #5: Patient analytics Deliver improved patient care with the HP Vertica Analytics Platform Optimize processes to speed delivery of care and eliminate waste and error Predict healthcare and utilization costs and track comparative benchmarks for hundreds of quality measures, including Medicare requirements Improve effectiveness of health systems, drive deeper insights into their systems and patient treatments and improve effectiveness of physicians and hospitals they support Healthcare and life science companies have successfully used the HP Vertica Analytics Platform to help prevent patient complications, increase the effectiveness of treatments, model health care reform implications, develop new drugs, and manage predictive care. Your health and life sciences organization can achieve these benefits and many more with the proven HP Vertica Analytics Platform as the foundation to your patient analytics program.

64 Big Data for Communications Service Providers What does Big Data consist of? Customer phone numbers, addresses, Internet usage, application downloads, travel history Customer smartphone usage Large communication networks, switches, billing systems, and service departments generate millions of CDRs daily 64 Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

65 Improving Mobile Services KDDI Corporation Speeds up analysis of Our mission is to make it increasing volumes of call possible for customers to use a data to help maintain and variety of applications and improve customer service content on networks and devices that are easy to connect and enjoyable to use. We also Queries went from three aim to continue providing high- minutes to ten seconds for quality customer services and much faster resolution of we anticipate that HP Vertica will service problems continue to support our Rapid identification and resolution of service problems helps improve customer service and increases competitiveness mission. Takahiro Yasunaga, Manager, Head of OSS Development Section, Core Network Development Depart., Nt Network ktechnical hi ldevelopment tdiv., KDDI Corporation Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

66 Powering CSP & MSO Data Services Kansys Challenges at Kansys New clients and increasing demands from existing clients was straining the existing data warehouse Experiencing delays in supporting user reporting requests - required huge CAPEX dollars to meet Existing data warehouse solution did not perform well for in-depth reporting, ad-hoc queries requirements for demanding customers HP Vertica Solution Real-time analytics capabilities deliver complex analysis and reporting quickly Massive scalability and storage capacity provide access to more data covering longer time periods Easily add new nodes or new clusters for new clients Increased customer satisfaction through faster response and flexible reporting capabilities 66 Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

67 Medical Innovation Saves Lives and Money BlueCross BlueShield Association 54M lives, 3.5B man-months of health care data Unpredictable customer demands, long batch queues With Vertica, response time reduced to minutes/seconds, batch wait time is 0 Now being used for modeling of healthcare costs, chronic illness prevention, detection of claims fraud, off-brand drug use, and more Started small with one research effort, result set came back so fast that we thought the queries had failed! Today we ve moved the entire analytics stack to Vertica! Thrilled to be an HP Vertica customer! Doug Porter, Senior Vice President and CIO of BlueCross BlueShield Association 67 Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

68 Improved Customer Loyalty for Banks Cardlytics 40-80x faster and 5% of the overall cost of legacy platform 90% reduction in operational overhead ROI in three months Increased revenue, resulting from more merchants participating in rewards programs HP Vertica enables us to fine-tune and personalize offers, and we are providing this service at hyper-speed. - Scott Grimes, CEO, Cardlytics 68 Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

69 Top 5 Analytic use cases Customer Sensor Patient Operations Enterprise data warehouse modernization Transform customer Analyze massive Prevent patient Understand systems data into actionable information volumes of sensor data Understand operational complications Increase the performance metrics to meet SLAs Better understand customer buying issues and reduce service costs effectiveness of treatments React to unexpected events and predict Analyze and visualize real time customer sentiment via social media activity Extend uptime Update processes for health care reform implications Develop new treatments future issues (predictive analytics) 69 Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

70 Real-time Retail Analytics Guess?, Inc. The HP Vertica backbone powers BI reporting and analytics at Guess? in North America Hundreds of employees depend on regularly scheduled and ad hoc reports, while 100 more non-traditional BI users rely on an easy-to-use G-Mobile ipad app Better sales tracking due to essential daily store reports generated 90 to 400 times faster Improved merchandise allocation and distribution of inventory across retail locations 70 Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

71 Analyzing Billions of Clicks Challenge at HP.com Millions of visitors generate billion clicks per month Must store 5 years worth of data to get full value of yearover-year clickstream analysis Oracle database had sluggish performance queries took 48 hours after each day s transactions Extremely complex website many pages are generated dynamically creating complex clickstream trails HP Vertica Solution Queries run in hours or even minutes; 48x 100x faster Industry-standard SQL accelerated acceptance and proficiency Speed of HP Vertica allows iterative and recursive analysis for deeper dives HP can build functionality tailored to individual interactions based on nuanced understanding of user behavior at an individual level 71 Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

72 Optimize Health Information Solutions Cerner Corporation HP Vertica helped improve Cerner Millennium, the company s flagship product that integrates nearly 60 solutions into a patient-centric suite focused on the Electronic Health Record and clinical workflows Cerner chose the HP Vertica Analytics Platform based on best-in-class performance and concurrency SLAs kept through more proactive management of Millennium hosting environment 72 Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

73 Gaming data analytics Supercell adopts HP Vertica Company evaluated data analytics solutions, conducted a successful Proof of Concept, and implemented HP Vertica Solution met cloud-deployment requirement Queries reduced from two to four hours to minutes or seconds Analytics improved customer service by augmenting player support 73 Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

74 The Knowledge from a Single View Comcast Network performance monitoring for millions of devices for QoS, analyzing billions of metrics HP Vertica data mart now larger than corporate EDW Follow-on projects to analyze CDR, IPDR, and VoD data Vertica opened doors to analyses that otherwise were either too time- intensive or impossible. A larger team of business managers now have faster, easier access to more information. That knowledge is invaluable in an aggressively competitive market like ours. Extensive use of HP Vertica s time series analytics - Brian Harvell, Network Ops 74 Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

75 Good Fits for Vertica Struggling to meet reporting / analytics SLAs Takes too long to generate the information the business requires Increasing data growth has strained current system Increasing expectations for real time information Current solution won t allow for reporting flexibility OLAP (online analytical processing) workloads Biggest Opportunities with Big Data 75 HP Confidential

76 Why customers choose HP Vertica. Superior Performance swapped out EMC GreenPlum 80% Faster at 1/3 Cost displaced IBM twice! Query time reduced by 5 hours to 5 minutes Lower TCO with commodity hardware support 30% reduction in operation costs Knocked out incumbent Oracle! Proven scalability with less RAM good bye ParAccel! Ease of use, scalability without massive PS engagement beat Teradata Aster, among others