Data Analytics & HPE Haven Big Data Platform. Michael Flower

Transcription

1 Data Analytics & HPE Haven Big Data Platform Michael Flower

2 Accelerating innovation and time to value Every 60 seconds kaggle SolidFire Pandora Sc ribd. Amazon Music ihandy DocuSign SmugMug Burroughs IBM Unisys NEC Fijitsu Hitachi Product Configurator Sales track ing & Mark eting Bills of Material Payroll SCM Mainframe Kilobytes Bull Costing ERP Manufacturing Projects CRM Quality Control HCM SAP Engineering HP MRM Inv entory EMC Cost Management Cash Management Time and Expense Order Entry HCM Fixed Assets Accounts Receivable Billing Ac tiv ity Management Training Time & Attendance Serv ic e Commissions Database Claim Processing Data Warehousing Joyent DCC CCC NetSuite Google Hosting.com Tata Communications NetDocuments Datapipe Alterian OpenText NetReach Microsoft Xerox Serif SLI Sy s tems OpSource Av id Elemic a Yahoo Kinaxis Microsoft SugarCRM Saba PPM Sonar6 Kenexa Quadrem Sonar6 Saba Softs c ape NetSuite Intac c t IntraLink s Adobe Cornerstone ondemand Softs c ape Plex Sy s tems ebay Quickbooks Client/Server Megabytes Rostering PLM Corel Volusion Ariba Workscape ADP VirtualEdge FinancialForce.com Zoho Yahoo! CyberShift Exact Online Sage salesforce.com Finance SuperCam Snapfis h Urban AppFog Travel Xactly Dragon Diction Pars e Taleo LinkedIn UPS Mobile Reference Facebook PingMe Lifestyle Bromium Atlass ian GoGrid Hyland Splunk Qvidian SCM CyberShift PaperHost Amazon Web Services Viber BrainPOP RightScale Social Networking MobileFrame.com CYworld YouTube Business Jive Software myhomework Tumblr. Qzone Fring Toggl News Amazon dotcloud Cookie Doodle Xing New Relic Mozy MailChimp PingMe Utilities Zynga Ah! Fasion Girl As s ociatedcontent BeyondCore SmugMug Rackspace buzzd The Internet Gigabytes Paint.NET Scanner Pro Zynga LimeLight Workday Baidu Navigation Renren Succ ess Factors Education Atlass ian Atlass ian Foursquare Hootsuite Flickr nebula ischedule Khan Academy MobilieIron Twitter cloudability CloudSigma HP eprint Fed Ex Mobile Zillabyte Games Productivity TripIt Yandex Twitter box.net Workbrain Yandex Mixi Sport Sc alextreme Heroku Pinterest Mobile, Social, Big Data & The Cloud Zettabytes Photo & Video Yammer Entertainment Answers.c om 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 Yottabytes

3 Big Data shift Mobile apps Human data System logs Volume of data Data centers Compliance archives Internet of Things Sensors Social networking Technology gap Machine data Business data Photo sharing Data Wearable devices Time

4 Big Data Platform Guiding Principles Fully Harness 100% of data Seamlessly deploy and consume anywhere Scale AND speed without compromise Open, extensible & easy to adopt Economics that work

5 Industry-leading breadth & depth of capabilities Haven Big Data Platform Contextual search Core big data business capabilities Access Explore Enrich Analyze Predict Serve Act And more... Data exploration Image/video analytics Accelerated analytics Geospatial analytics Sentiment analysis SQL on Hadoop Predictive analytics On-premise In the Cloud

6 HPE Intelligent Data Operating Layer (IDOL) The OS for human information Single processing layer to handle the continuum of human information Connect Access virtually any source of information Understand Act & Automate Form an understanding of information, including docs, s, databases, social media, rich media, etc. Over 500 functions to derive actionable insights

7 HPE IDOL - unique platform for Information Only IDOL can handle the continuum of Human Information Single processing layer for all data Continuous learning ability Superior speed, scalability, and simplicity Built in security & compliance functionality 400+ seamless connections to data repositories 1,000+ file types support Language independent Ability to understand meaning makes us unique in the market

8 HPE Vertica Analytical Data Warehouse Why is HPE Vertica different from other Analytical Platforms? Columnar storage Compression MPP scale-out Distributed query Projections Speeds query time by reading only necessary data Lowers costly I/O to boost overall performance Provides high scalability on clusters with no name node or other single point of failure Any node can initiate the queries and use other nodes for work. No single point of failure Combine high availability with special optimizations for query performance CPU Memory Disk CPU Memory Disk CPU Memory Disk A B D C E A

9 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 Law enforcement Counter terrorism Traffic flow optimization Broadcast monitoring Churn prevention Advertising optimization Supply chain optimization Defect tracking RFID Correlation Warranty management Weather forecasting Natural resource exploration Drug development Scientific research Evidence based medicine Healthcare outcomes analysis Sentiment analysis Social CRM / network analysis Churn mitigation Brand monitoring Cross and Up sell Loyalty & promotion analysis Web application optimization Horizontal Use Cases Marketing campaign optimization Brand management Social media analytics Pricing optimization Internal risk assessment Customer behavior analysis Revenue assurance Sources: IDC: 2012 Worldwide Big Data Technology and Services Forecast: , Gartner: 2012 Big Data Drives Rapid Changes in Infrastructure and $232 Billion in IT Spending Through 2016 Logistics optimization Clickstream analysis Influencer analysis IT infrastructure analysis Legal discovery Equipment monitoring Enterprise search

10 Leaders don t make compromises

11 Use case: Smart / Safe Cities Improving public safety by detecting & investigating high-risk events & threats Deployment Environment Haven Ingest data from 2,000+ CCTV cameras in Auckland NZ View network of road and environmental sensors Social media trending, broadcast monitoring, and real time web news Phase 1: HP IDOL scene analysis and license plate recognition Phase 2: HP Vertica to uncover breaking trends and facilitate incident responses HP IDOL eduction sends interesting data to HP Vertica for statistical analysis and slice/dice Combine HP Vertica s pattern-matching and graph-analysis at scale with HP IDOL s ability to model concepts and enrich data Award* winning video analytics powered by HPE IDOL 2014 Best Video Analytics - Security Industry Association

12 Supercell Mobile gaming company leads with creativity supported by data Challenge Adopt real-time gaming data analytics platform Solution HPE Vertica Analytics Platform Result Queries reduced from two to four hours to minutes or seconds Solution successfully met cloud deployment requirement Expanded data capacity from a few months to whole lifetime of data Analytics improved customer service by augmenting player support

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14 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

15 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 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 $10.

16 Time to get Smart!

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

18 Solution HPE 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.

19 Why Partner with HPE Big Data Platform Technology based on land-and-expand strategy Industry-leading technologies platforms Value-Adds Services e.g. consultancy, development, data scientist Additional tools Tableau, Talend, Logi Analytics Hardware, hosted and/or managed services Comprehensive Enablement (sales/technical training & resources)

20 Thank you Michael Flower: