EMC Big Data: Become Data-Driven

Transcription

1 1 EMC Big Data: Become Data-Driven

2 2 What Is Big Data Exactly? Enterprise Internet

3 3 How Much Data Is There? 44 Zettabytes 1 ZB = 1B TBs 44 zettabytes is estimated to be 50 times the amount of all the grains of sand on all the beaches on earth. 7.6B people 200B things

4 4 Why Is Big Data Important? Understand Customer Behavior Optimize Operations Manage Risk Enable Innovation

5 5 Where Do I Focus? Understand Customer Behavior Optimize Operations Manage Risk Understand Customer Behavior Optimize Operations Manage Risk Enable Innovation Manage Risk Manage Risk Gartner 9/13: Survey Analysis: Big Data Adoption in 2013 Shows Substance Behind the Hype

6 6 EMC Customer Examples Leveraging Big Data To Understand Customer Behavior Easynet enables retailer to increase revenue per customer by 5% through improved customer loyalty program Knotice enables retailers to increase conversion rates by 700% on Black Friday through improved customer ad targeting Havas Digital enabled a Travel company to increase sales by 27% and ROI by 300% though better campaign optimization

7 7 Why Act Now? Through 2015, organizations integrating high-value, diverse, new information types and sources into a coherent information management infrastructure will outperform their industry peers financially by more than 20%. We built what looks like a software company and we're moving from silos to a single platform. The shift to digital requires a complete overhaul of banks technology it is a matter of survival...we now have a state of the art platform.

8 Keys To Success: Maximize Opportunities Current Situation Unclear business cases Skills deficits Lack of experience Rigid app dev procedures Complex app deployment Data silos Escalating data management costs People Process Technology Data-Driven Enterprise Optimal use cases Trained & experienced staff Agile dev methodology PaaS Data Lake Simplified data mgmt Copyright 2014 EMC Corporation. All rights reserved. 8

9 Keys To Success: People Current Situation Skills deficits Lack of experience Lack of relevant expertise People Process Technology EMC Solutions EMC Big Data Curriculum Pivotal Data Labs Copyright 2014 EMC Corporation. All rights reserved. 9

10 10 EMC Big Data Curriculum Gain Skills For Immediate & Effective Participation In Big Data Projects 90 min Introducing Data Science and Big Data Analytics for Business Transformation 1 day Data Science and Big Data Analytics for Business Transformation 5 days Data Science and Big Data Analytics

11 11 Pivotal Data Labs Gain Experience Through Big Data Projects Led By Expert Data Scientists Discovery Insights Results 1-12 week engagement

12 12 Customer Example: Staff Trained Objectives Better understand and serve customers utilizing high volume, new data sets Cost-effective means of accommodating database growth and complex data analysis Solutions EMC Data Computing Appliance (DCA) Pivotal Data Labs Services Results Improved customer retention through faster identification of at-risk customers Easily scaled from 6 to 11 terabytes of data

13 Keys To Success: Processes Current Situation Unclear business cases Rigid app development People Process Technology EMC Solutions EMC Big Data Vision Workshop Pivotal Labs Copyright 2014 EMC Corporation. All rights reserved. 13

14 Business Value Copyright 2014 EMC Corporation. All rights reserved. XXXX.XX XXXX.XX XXXX.XX Shop Hot Offer! > 14 EMC Big Data Vision Workshop Collaborative Process To Uncover Optimal Big Data Use Cases Monetize Customer Usage Behaviors Hi F A E B C D Lo Implementation Feasibility Hi A Churn: Leverage customer usage data to improve Churn Predictive Model Effectiveness B Product Performance: Change network bandwidth based upon customer s usage patterns C Network Optimization: Optimize Network investments using customers apps usage patterns Standardization: Standardize tools, processes, D analytic models and hiring profiles across teams Recommendations: Create product E recommendations based upon usage behaviors Monetization: Leverage/package customer usage F data to drive new monetization opportunities What If Deliver Real-time, Personal Offers Integrating Customers Shopping Propensities And Current Location? What are the usage patterns of my most valuable card members? What are the usage patterns that indicate someone may churn? How do I leverage personalized offers to increase cardmember engagement and usage? How do I gain insights into cardmember s interests, passions, affiliations and associations? What additional insights would my Merchants value? Recommendation Machine sensor logs / error codes Digitalized Work Orders Machine vibration data Manufacturer Performance History Omega machine maintenance data Other providers maintenance data Location-based data Improve predictive models Ease of data Acquisition Cost of Acquisition Data Management / Preparation Research Analysis Ideation Prioritize Document 1 day workshop (2 week engagement)

15 15 Pivotal Labs Agile Methodology Shortens Development Cycle Agile development practices enable quick response to market changes Build QA Pivotal Tracker allows for full control over projects Collaborative, paired programming approach delivers better product in less time Code Releas e Define/Pr ioritize Feedback

16 16 Customer Example: Agile App Dev Objectives Create a SaaS solution with rich features around ever-expanding universe of social media data Provide a consistent and reliable architecture to gain insight from real-time data sourced from Twitter, Facebook, Tumblr, WordPress, Instagram and more Solutions Pivotal Labs (Agile development practices) Pivotal Tracker (Project management and collaboration) Results Helped launch service & define development practices GNIP able to lead the public social data ecosystem WW Captured the business of 90% of the Fortune 500

17 Keys To Success: Technology Current Situation Data silos Escalating data management costs Complex app deployment People Process Technology EMC Solutions EVP Data Lake Pivotal CF Copyright 2014 EMC Corporation. All rights reserved. 17

18 18 Today s Analytic Environment Silo d And Costly Data Sources Data Warehouse Enterprise Apps Prioritized Operational Processes Reporting Data Marts Cloud Services Non-Prioritized Data Provisioning

19 19 Architect For A Data Lake Centralize Data Storage, Processing, And Application Services Ingest Store Analyze Surface Act Capture data from a wide range of sources, traditional and new. Store everything in one environment for cross data-set analysis. Use advanced algorithms to discover new, predictive patterns. Share insight with business domain experts. Build data-driven applications that meet business needs.

20 20 EVP Data Lake Minimize Silos Through Support Of Diverse Application Requirements Multi-protocol support enables legacy applications use Existing data available for analytics through HDFS VELOCITY NoSQL VARIETY NFS SMB DATA DSSD VNX OTHER ViPR HDFS HDFS ANALYTICS GEMFIRE XD HAWQ NoSQL IN-MEMORY SQL SQL APPLICATIONS CLOUDFOUNDRY REALTIME INTERACTIVE Enables different data processing needs Modular architecture enables use of some or all components VOLUME NFS S3 SWIFT ATMOS ISILON ViPR ECS APPLIANCE HDFS HDFS D A T A L A K E PIVOTAL HD DCA MR HDFS BATCH VMWARE

21 Real Batch time 21 EMC Isilon HDFS-Enabled Storage Consolidate Data Storage Through Multi-Protocol Access Scale compute & storage independently HDFS-enable existing data: no ingest necessary HPC Shares Hadoop Easily import & export via next-gen communications: HDFS, NFS, SMB, HTTP, & FTP Mobile Analytics Fault-tolerant, end-to-end data protection Archive Surface Cloud Act

22 22 EMC ViPR Software Defined Storage Reduce Data Storage Silos Through Multi-Protocol Access Analytics-enable existing storage arrays HDFS, S3, Swift & Atmos API support Storage hardware choice: Enterprise, commodity, ECS appliance

23 23 Analytics: Pivotal HD Consolidate Analytic Silos Through Diverse Data Processing Services Support for all data processing needs: Real-time Interactive Batch Support for multiple application interface types: SQL MapReduce NoSQL In-Memory SQL Map-Reduce I/P & O/P Formatter Sensor Data / Feeds Re-evaluate Model Model Refresh Native Persistence GemFire XD Shared Data Online Apps Model Refresh Re-evaluate Model Analytic Apps HAWQ PXF HDFS Pivotal HD Enterprise Command Center

24 24 Tying It All Together: Hadoop Appliances Easily Deploy Hadoop With Pre-Integrated Appliances EMC DCA Pre-integrated data computing & storage: Pivotal GPDB, Pivotal HD, EMC Isilon VCE vblock Pre-integrated server, storage, networking, virtualization, & management Supports all major Hadoop distributions

25 25 Continuous App Delivery: Pivotal CF Delivers A Turnkey PaaS Experience With Leading App And Data Services Developers can focus on development, not infrastructure Divide AppDev from Operations Eliminate provisioning and deployment bottlenecks Public Hybrid Private

26 26 Customer Example: Data Lake Solution Objectives Rapidly launch new market intelligence service for fashion retailers Support large and growing volumes of Big Data Solutions Pivotal Greenplum Database Pivotal HD EMC Isilon Pivotal Data Labs Results Rapidly launched new service Delivered high performance & scalability with simple administration & management

27 27 Engage With EMC People: EMC offers experienced Big Data/Data Science practitioners to train your people for successful execution Processes: EMC offers proven methodologies to implement businessdriven, agile processes that deliver more value Technology: EMC offers the latest, bestof-breed infrastructure solutions to simplify your data architectures and evolve into a Data Lake

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29 29 Big Data Opportunities By Industry Eleven Industries Manu & N. Res. Media/Comm Services Government Education Retail Banking Insurance Health Care Transportation Utilities Gartner 9/13: Survey Analysis: Big Data Adoption in 2013 Shows Substance Behind the Hype

30 30 Big Data Opportunities By Industry Manufacturing And Natural Resources 69% Customer experience 64% 58% 56% 47% 44% 20% 20% 14% Process efficiency New products/models Cost reduction More targeted marketing Improved risk management Monetize information directly Regulatory compliance Enhanced security capabilities Gartner 9/13: Survey Analysis: Big Data Adoption in 2013 Shows Substance Behind the Hype

31 31 Big Data Opportunities By Industry Media/Communications 76% 71% 62% 57% 52% 38% 33% 29% 24% Customer experience Process efficiency More targeted marketing Cost reduction New products/models Improved risk management Monetize information directly Regulatory compliance Enhanced security capabilities Gartner 9/13: Survey Analysis: Big Data Adoption in 2013 Shows Substance Behind the Hype

32 32 Big Data Opportunities By Industry Services 75% 70% 61% 58% 46% 43% 36% 24% 17% New products/models Customer experience Process efficiency More targeted marketing Cost reduction Monetize information directly Improved risk management Enhanced security capabilities Regulatory compliance Gartner 9/13: Survey Analysis: Big Data Adoption in 2013 Shows Substance Behind the Hype

33 33 Big Data Opportunities By Industry Government 70% 59% 56% 48% 48% 44% 37% 33% 33% Process efficiency Cost reduction Improved risk management New products/models Customer experience Monetize information directly Regulatory compliance More targeted marketing Enhanced security capabilities Gartner 9/13: Survey Analysis: Big Data Adoption in 2013 Shows Substance Behind the Hype

34 34 Big Data Opportunities By Industry Education 85% 77% 69% 54% 54% 46% 31% 31% 23% Efficiency Customer experience Cost reduction More targeted marketing New products/models Regulatory compliance Improved risk management Enhanced security capabilities Monetize information directly Gartner 9/13: Survey Analysis: Big Data Adoption in 2013 Shows Substance Behind the Hype

35 35 Big Data Opportunities By Industry Retail 80% 73% 60% 47% 40% 40% 13% 13% 7% Customer experience More targeted marketing Cost reduction Monetize information directly Process efficiency New products/models Improved risk management Regulatory compliance Enhanced security capabilities Gartner 9/13: Survey Analysis: Big Data Adoption in 2013 Shows Substance Behind the Hype

36 36 Big Data Opportunities By Industry Banking 71% 61% 61% 56% 54% 46% 46% 27% 27% Customer experience More targeted marketing Improved risk management Process efficiency New products/models Cost reduction Regulatory compliance Monetize information directly Enhanced security capabilities Gartner 9/13: Survey Analysis: Big Data Adoption in 2013 Shows Substance Behind the Hype

37 37 Big Data Opportunities By Industry Insurance 74% 61% 61% 52% 45% 45% 32% 29% 23% Customer experience Process efficiency More targeted marketing New products/models Cost reduction Improved risk management Enhanced security capabilities Regulatory compliance Monetize information directly Gartner 9/13: Survey Analysis: Big Data Adoption in 2013 Shows Substance Behind the Hype

38 38 Big Data Opportunities By Industry Healthcare 58% 50% 50% 50% 42% 42% 33% 17% 17% Cost reduction Process efficiency Customer experience Improved risk management New products/models Regulatory compliance Enhanced security capabilities More targeted marketing Monetize information directly Gartner 9/13: Survey Analysis: Big Data Adoption in 2013 Shows Substance Behind the Hype

39 39 Big Data Opportunities By Industry Transportation 79% 71% 71% 71% 64% 29% 21% 21% 14% Process efficiency Customer experience New products/models Cost reduction More targeted marketing Monetize information directly Improved risk management Regulatory compliance Enhanced security capabilities Gartner 9/13: Survey Analysis: Big Data Adoption in 2013 Shows Substance Behind the Hype

40 40 Big Data Opportunities By Industry Utilities 80% 60% 60% 40% 40% 40% 40% 40% 0% Customer experience Process efficiency Cost reduction More targeted marketing New products/models Improved risk management Monetize information directly Regulatory compliance Enhanced security capabilities Gartner 9/13: Survey Analysis: Big Data Adoption in 2013 Shows Substance Behind the Hype