3 джозеп курто превращаем вашу организацию в big data компанию
- 1. Transforming your organization into a Big Data company BIG DATA | Digital October 2015 Josep Curto | Professor, IE Business School | CEO, Delfos Research
- 2. 2 Agenda Digital disruption (or why Big Data is one of the key strategies for your organization) Business Models (The multiple personalities of Big Data) Implementing Big Data (The moment of truth)
- 4. 4 Context
- 5. 5 Have you ever wonder…. Why Amazon, Apple, Google, Facebook and Netflix are so successful?
- 6. 6 They understand one major thing
- 8. 8 In particular,… Your digital footprint
- 9. 9 Let’s think for a moment Every object, person and organization has a digital information halo
- 10. 10 And the data… … is becoming more robust, valuable and complex
- 11. 11 This is the new normal Business changes Technology changes
- 13. 13 Challenges
- 14. 14 Challenge 1: Capture Source: Bitly http://blog.bitly.com/post/9887686919/you-just-shared-a-link-how-long-will-people-pay Faster data, reduce lifespan
- 15. 15 Challenge 2: Storage 0.8$ ZB$ *Ze#abyte:)1,000,000,000,000,000,000,000)bytes) (#1#trillion#gigabytes)# 2009$ 2020$ 40## Ze5abytes*# Source: IDC IDC Digital Universe Study, 2012, Sponsored by EMC More data! what is relevant?
- 16. 16 Challenge 3: Analisis The more sources we have, the more complex to extract the value Source: IDC IDC Digital Universe Study, 2012, Sponsored by EMC 2011: 50.07 Tb/s 2012: 86.54 Tb/s Data in transit: 856 Tb/s
- 17. 17 Challenge 4: Visualization New data sources and formats and increased volume need new communication techniques
- 18. 18 Challenge 5: IT Dependence Technology is deeply embedded into our business processes
- 19. 19 Challenge 6: Create a new culture • Attitude • Habits • Knowledge
- 20. 20 Big Data
- 21. What is big data Volume Velocity Variety
- 22. 22 Velocity Variety Volume · Batch · Near Real Time · Real Time · Streaming · Structured · Semi-structured · Unstructured Volume + Variety Volume + Velocity Velocity + Variety · Terabyte · Petabyte · Exabyte · Zettabyte · Yottabyte Volume + Velocity + Variety
- 23. 23 More Vs
- 24. The most important V
- 25. 25 Big Data is not new KB Files Statistics COBOL GB Tables OLAP Cubes SQL TB Semi-structured Apps XML PB Dynamic Variety Mahout (& other) NoSQL Big Data Analítica Language 60s 80 - 96 97 - 07 07 - ?
- 26. 26 3Vs are not enough Velocity Variety Volume · Batch · Near Real Time · Real Time · Streaming · Structured · Semi-structured · Unstructured Volume + Variety Volume + Velocity Velocity + Variety · Terabyte · Petabyte · Exabyte · Zettabyte · Yottabyte Volume + Velocity + Variety • Horizontal scalability • Relational constraints
- 27. 27 Relational Constraints Non Relational Schema-free Distributed 4 Types: Key-value, column-oriented, graph, document Relational Data is normalised and static Relational schema Data in one repository
- 29. What is big data (II) Batch Processing (Volume) Stream Processing (Velocity) NoSQL (Variety)
- 31. 31 Hadoop is the most famous technology
- 32. 32 Big Data RDBMS Data Visualization Predictive Analytics Hadoop + Content Search & Analytics In-memory Streaming Technologies Object & Graph Databases It is not the only one
- 33. 33 We are moving from… Information Systems Data Mobile Data Machine Data Social Media Data Audio, video, text Stream Data Sources Corporate Information Factory / Data Warehousing Storage& Processing Information Management Data Governance Master Data Management Data Management Analysis Analytics Operational Intelligence Business Intelligence
- 34. 34 to a new architecture Information Systems Data Mobile Data Machine Data Social Media Data Audio, video, text Stream Data Sources Corporate Information Factory / Data Warehousing Storage& Processing Information Management Data Governance Master Data Management Data Management Analysis Analytics Operational Intelligence Business Intelligence Big Data NoSQL In- memory MPP HPC Data Products
- 36. 36 The Big Data market is growing Source: IDC IDC Worldwide Big Data Technology and Services 2010 – 2015 Forecast, March 2013 0 10 20 30 40 2011 2012 2013 2013 2015 2016 2017 32.4 25.7 20.4 16.1 12.6 9.8 7.4
- 37. 37 Hadoop is leading the way 1991 1992 1994 1995 1997 2000 Project emerge Community creation Code is available, community grows First companies Ecosystem emergence Mainstream, M&A Starts 2006 2007 2008 2009 2012 2015
- 38. 38 Many tecnologies Source: 451Research Database Landscape Map - February 2014
- 39. 39 From data to business Source: IDC
- 40. 40 The number of companies is growing Source: Delfos Research Worldwide Data Market 1836 - 2015
- 41. 41 But our expectations are high Fuente: Gartner Hype Cycle for Big Data,2012 Fuente: Gartner Gartner HypeCycle 2013
- 42. 42 New needs
- 44. 44 Our data needs Available Accesible Quality Right time Security Information
- 45. 45 Which technology is required? Data Market BD BA BI DM DT
- 46. 46 Which capacities do we need?
- 47. 47 How do I have to compete? Decoding the digital footprint
- 48. 48 Combining different factors Amplificator Interface Algorithms Data Business model
- 49. 49 We need to compete on innovation Wow moments Great virtual design Great physical design
- 52. 52 How to generate value
- 54. 54 Data-driven Business Models Source: University of Cambridge A Taxonomy of Data-driven Business Models used by Start-up Firms Data-driven Business Model Key Activities Customer Segment Revenue Model Key Resources Cost Structure
- 55. 55 Data the taxonomy 14 Data Sources Internal existing data Self-generated Data External Acquired Data Customer provided Free available Open Data Social Media data Web Crawled Data Source: University of Cambridge A Taxonomy of Data-driven Business Models used by Start-up Firms
- 56. 56 Activities Dimension: Activities 15 Key Activity Data Generation Crowdsourcing Tracking & Other Data Acquisition Processing Aggregation Analytics descriptive predictive prescriptiveVisualization Distribution Source: University of Cambridge A Taxonomy of Data-driven Business Models used by Start-up Firms
- 57. 57 Value proposition 16 Offering Data Information/Knowledge Non-Data Product/Service Source: University of Cambridge A Taxonomy of Data-driven Business Models used by Start-up Firms
- 58. 58 Revenue model 17 Revenue Model Asset Sale Lending/Renting/Leasing Licensing Usage fee Subscription fee Advertising Source: University of Cambridge A Taxonomy of Data-driven Business Models used by Start-up Firms
- 59. 59 Customers 18 Target Customer B2B B2C Source: University of Cambridge A Taxonomy of Data-driven Business Models used by Start-up Firms
- 60. 60 Some types are emerging activities and key data sources 29 Type F Type A Type D Type E Type B Type C Aggregation Analytics Data generation Free available Customer provided Tracked& generated Key activity KeyDataSource 6 significant Business Model types were identified 28 Type B: “Analytics-as-a-Service” Type C: “Data generation & Analytics” Type D: “Free Data Knowledge Discovery” Type A: “Free Data Collector & Aggregator” Type E: “Data Aggregation-as-a-Service” Type F: “Multi-Source data mashup and analysis” Source: University of Cambridge A Taxonomy of Data-driven Business Models used by Start-up Firms
- 61. 61 One example: CBInsights Data-driven Business Model Data Processing, Aggregation & Analysis Investors, media Subscription fee External Companies Data Platform, data scientists
- 62. 62 Some examples
- 63. 63 Big Data around the world Credit Suisse Netflix TescoWalMart General Motors Disney Metlife Apple Caesars Entertainment SpotifyHouston Rockets NFL
- 64. 64 The dark side of Big Data
- 65. 65 The dark force is strong in your company
- 66. Google knows about you more than you think A graph to rule them all NSA 66 Where is the privacy?
- 67. 67 Even your TV watches you!
- 70. Internal limits Skills Culture Integration 70
- 71. External limits Regulation Data ≠ Correlation Models ≠ Reality 71
- 74. Many reasons to fail IMPLEMENTATION FAILURE COST OPERATIONAL STRATEGIC Lack of top management commitment Unavailability of subject matters experts Unavailability of key users for UTA Poor quality of testing Poor Knowledge Transfer Cost Overrun Unrealistic ROI TECHNOLOGY PEOPLE Poor Data Quality Over Customization Inadequate data sources knowledge Poor IT infrastructure Poor ETL Quality Poor BI product selection User resistance To change High rotation of Project team members Inadequate resources Poor user involvement TACTIC Inadequate training and education Non-empowered decision-makers Poor departmental alignment Inappropriate timing to go live Poor communication Unrealistic expectations Inadequate functional requirements Inadequate project team composition Poor project management Unrealistic project scheduling Ineffective organizational change management 74
- 75. How to start
- 76. Framing the problem • Identify business needs • Conceptualise business opportunities • Determine Big Data type • Define Big Data Strategy
- 77. Pilot and beyond • Develop model • Identify data set • Build / Buy / Subscribe to big data architecture • Create pilot • Scale pilot
- 78. Communication • Comunicate outcome • Identify next steps
- 79. 79 Your future big data project
- 81. 81 What we want Time Time to action Lostvalue Data latency Analysis latency Decision-making latency Business Event Data ready for analysis Information Action Business Value
- 82. 82 When we need them BI (mature) BA (mature) Big Data (emerging) Tools Query, reporting, OLAP, alerts Forecasting, regression and modeling and/ or BI Machine learning, visualization Focus What happened, how many, how often, what is the problem, what action is needed Why is this happening, what if these trends continue, what will happen next, what is the best that can happen Capture, storing and analyzing data: all Use Reactive Proactive / Predictive / Prescriptive All / none Types of data structured Structured / semi-structured All Data Complexity Low Low / medium High Scope Management Processes Vertical / processes
- 84. 84 Traditional Knowledge, Literacy and Skills Computer Literacy Analytic Proficiency Data Proficiency Operational Proficiency Total Information Proficiency Building traditional capabilities and skills Mastering technology Automating clerical work Reengineering business processes Building ubiquitous knowledge bases Optimizing all decisions We want to create digital competencies
- 85. 85 We need to measure the value (McDonald 2004) ValueCreated Overall Success of the Initiative Implementation Success • On- time • On-budget User Success • User adoption • User Satisfaction • Data Problems Operational Success • Productivity Improvements • Process efficiency and effectiveness • Key Performance Indicators Business Success • Return on Investment • Economic Value Add • Revenue increases • Cost Savings • Customer / Corporate profits • Enables Business Strategy and Competitive Advantage • Create a formal, continuos process for measuring success and value generated • Identify and measure results of each project phase • Establish realistic goals and expectations based on capability / maturity
- 86. 86 We need to become data-driven Source: Thomas H. Davepnort