Big data voor Sociale Secretariaten
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Big data voor Sociale Secretariaten

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Inspiratie Sessie voor Sociale Secretariaten (Februari 2014).

Inspiratie Sessie voor Sociale Secretariaten (Februari 2014).
Zet uw lean Big Data bril op en kijk de toekomst weer vol vertrouwen in de ogen!

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    Big data voor Sociale Secretariaten Big data voor Sociale Secretariaten Presentation Transcript

    • www.realdolmen.com BIG DATA INSPIRATION SESSION 7/2/2014 FEBRUARY 10, 2014 | SLIDE 1
    • AGENDA      Setting the scene. Defined. Avoiding pitfalls. Practical approach. Open discussion. Use cases & reality. FEBRUARY 10, 2014 | SLIDE 2
    • FEBRUARY 10, 2014 | SLIDE 3
    • BIG DATA interaction data transaction data ? FEBRUARY 10, 2014 | SLIDE 4 data integration data processing data analytics
    • BIG DATA interaction data transaction data data integration data processing data analytics Social Media FEBRUARY 10, 2014 | SLIDE 5
    • BIG DATA interaction data transaction data Machine/Device data integration data processing data analytics Scientific/Genome ClickStream Data Call Detail Records FEBRUARY 10, 2014 | SLIDE 6
    • BIG DATA interaction data transaction data data integration data processing data analytics Online Transaction Processing Online Analytical Processing & Datawarehouse Appliances FEBRUARY 10, 2014 | SLIDE 7
    • BIG DATA interaction data transaction data data integration data processing data analytics FEBRUARY 10, 2014 | SLIDE 8
    • BIG DATA interaction data transaction data data integration data processing data analytics FEBRUARY 10, 2014 | SLIDE 9
    • BIG DATA interaction data transaction data data integration data processing data analytics FEBRUARY 10, 2014 | SLIDE 10
    • BIG DATA & YOUR MARKET REACH FEBRUARY 10, 2014 | SLIDE 11
    • BIG DATA & YOUR MARKET REACH Marketing Mix Direct Retail ATL Online + Big Data = Better insight & Value FEBRUARY 10, 2014 | SLIDE 12
    • USE CASE #1 – SOCIAL TV ANALYTICS FEBRUARY 10, 2014 | SLIDE 13
    • USE CASE #1 – SOCIAL TV ANALYTICS FEBRUARY 10, 2014 | SLIDE 14
    • USE CASE #1 – SOCIAL TV ANALYTICS FEBRUARY 10, 2014 | SLIDE 15
    • USE CASE #1 – SOCIAL TV ANALYTICS FEBRUARY 10, 2014 | SLIDE 16
    • FEBRUARY 10, 2014 | SLIDE 17
    • FEBRUARY 10, 2014 | SLIDE 18
    • USE CASE# 2 – RETAIL CUSTOMER ANALYTICS FEBRUARY 10, 2014 | SLIDE 19
    • USE CASE# 2 – RETAIL CUSTOMER ANALYTICS FEBRUARY 10, 2014 | SLIDE 20
    • USE CASE# 2 – RETAIL CUSTOMER ANALYTICS Brand preference Political learnings Reading habits Charitable giving Number of cars FEBRUARY 10, 2014 | SLIDE 21
    • USE CASE# 2 – RETAIL CUSTOMER ANALYTICS FEBRUARY 10, 2014 | SLIDE 22
    • USE CASE# 2 – RETAIL CUSTOMER ANALYTICS FEBRUARY 10, 2014 | SLIDE 23
    • USE CASE# 3 – PERSONAL LIFE PREDICTION Interactions People FEBRUARY 10, 2014 | SLIDE 24 Data patterns
    • USE CASE# 3 – PERSONAL LIFE PREDICTION Relationship Status Changes Data patterns FEBRUARY 10, 2014 | SLIDE 25
    • USE CASE# 3 – PERSONAL LIFE PREDICTION Photo tags Check-in Page & photo views Messaging history Interactions People Type of relationship Friendship history Love history Friends’ network FEBRUARY 10, 2014 | SLIDE 26 33% accuracy Data patterns Relationship status changes Wall-writing patterns relationship strenght Flirtation index
    • Big Data defined FEBRUARY 10, 2014 | SLIDE 27
    • FEBRUARY 10, 2014 | SLIDE 28
    • FEBRUARY 10, 2014 | SLIDE 29
    • FEBRUARY 10, 2014 | SLIDE 30
    • VOLUME Interactions Transactions Source: An IDC White Paper - sponsored by EMC. As the Economy Contracts, the Digital Universe Expands. . 2011 2,7ZB FEBRUARY 10, 2014 | SLIDE 31 2015 8ZB 2020 35ZB
    • VARIETY FEBRUARY 10, 2014 | SLIDE 32
    • VELOCITY FEBRUARY 10, 2014 | SLIDE 33
    • FEBRUARY 10, 2014 | SLIDE 34
    • BIG DATA OPPORTUNITIES Velocity Big Data Integration enables an Organization to: Volume  Do things they could not do before  Do things that they have been doing much more cost effectively 1 Business Expects Big Benefits FEBRUARY 10, 2014 | SLIDE 35 Variety 2 Traditional Paradigms cannot support all of Big Data Challenges Complexity 3 A New Big Data processing Platform emerged to support Big Data requirements 4 Complimentary to other technologies
    • DEFINING BIG DATA EXPLOSIVE GROWTH OF DATA – VOLUME, VARIETY, VELOCITY Velocity Business Value Volume Data Volume Across Time Scales Source: IDC Years Variety FEBRUARY 10, 2014 | SLIDE 36 Latency Sub-Second
    • FEBRUARY 10, 2014 | SLIDE 37
    • PAST GENERATION TECHNOLOGIES WERE NEVER DESIGNED FOR BIG DATA VOLUMES AND TYPES Entities Securities Customer Other Why did it happen? Reference Data What happened? Payments, Trade Data warehouse Mainframes, Apps Documents and Emails Social Media, Web Logs Machine & Device Data FEBRUARY 10, 2014 | SLIDE 38 Based on traditional relational database technologies • • • Data mining Data analysis Business Intelligence What will happen?
    • GROWING ADOPTION IN HADOOP Entities Securities Customer Other Reference Data Payments, Trade Mainframes, Apps • Highly scalable: Any data volume/size Documents and Emails Social Media, Web Logs Machine & Device Data FEBRUARY 10, 2014 | SLIDE 39 • Flexible: Any Data Type (Structure and Unstructured) • Cost effective: Leverages commodity hardware
    • INVESTMENTS IN DATA ANALYTICS, DATA MINING, DATA VISUALIZATION TECHNOLOGIES • Improve User Experience vs. previous generation offerings • Flexible Delivery (On-premise and Cloud) • Pre-packaged Analytics and Rules FEBRUARY 10, 2014 | SLIDE 40
    • BIG DATA TECHNOLOGIES AT WORK Entities Securities Customer Data Mining, Predictive Analytics Other Reference Data Next Gen. Data Warehouse Payments, Trade Sentiment Analysis Mainframes, Apps Documents and Emails Process, Score, Analyze All Data Social Media, Web Logs Machine & Device Data FEBRUARY 10, 2014 | SLIDE 41 Consume Results Real-time Data Visualization
    • More Data Volumes Requires Scalable Data Integration Capabilities FEBRUARY 10, 2014 | SLIDE 42
    • INTEGRATING DATA WITH HADOOP REQUIRES DEEP PROGRAMMING EXPERTISE • Hadoop Programming language is complex to integrate data • Requires significant knowledge and expertise • Development costs can significantly lower expected business value FEBRUARY 10, 2014 | SLIDE 43
    • BIG DATA MANAGEMENT Entities Securities Customer Data Mining, Predictive Analytics Other Payments, Trade Mainframes, Apps Documents and Emails Social Media, Web Logs Machine & Device Data FEBRUARY 10, 2014 | SLIDE 44 Big Data Integration & Masking Reference Data Data Warehouse Business User Process, Score, Analyze All Data Sentiment Analysis Consume Results Real-time Data Visualization
    • More Data Sources and Types Increases Risk of Data Quality Errors FEBRUARY 10, 2014 | SLIDE 45
    • BIG DATA MANAGEMENT Data Analyst Entities Other Developer Data Owner Securities Customer Data Steward Data Quality & Governance Payments, Trade Mainframes, Apps Documents and Emails Social Media, Web Logs Machine & Device Data FEBRUARY 10, 2014 | SLIDE 46 Big Data Integration & Masking Reference Data Data Mining, Predictive Analytics Data Warehouse Business User Process, Score, Analyze All Data Sentiment Analysis Consume Results Real-time Data Visualization
    • Managing More Data Increases the Risk of an Unwanted Data Breach FEBRUARY 10, 2014 | SLIDE 47
    • BIG DATA MANAGEMENT Data Analyst Other Data Owner Data Mining, Predictive Analytics Securities Customer Developer Data Quality & Governance Payments, Trade Mainframes, Apps Documents and Emails Social Media, Web Logs FEBRUARY 10, 2014 | SLIDE 48 Big Data Integration & Masking Reference Data Data Warehouse Business User Process, Score, Analyze All Data Consume Results Data Privacy Enforcement Entities Data Steward Sentiment Analysis Real-time Data Visualization
    • Dealing with Big Data Requires an Effective and Efficient Archiving Solution FEBRUARY 10, 2014 | SLIDE 49
    • BIG DATA MANAGEMENT Data Analyst Other Data Owner Data Mining, Predictive Analytics Securities Customer Developer Data Quality & Governance Payments, Trade Mainframes, Apps Documents and Emails Social Media, Web Logs Big Data Integration & Masking Reference Data Data Warehouse Business User Process, Score, Analyze All Data Consume Results Big Data Archiving and Retention FEBRUARY 10, 2014 | SLIDE 50 Data Privacy Enforcement Entities Data Steward Sentiment Analysis Real-time Data Visualization
    • BIG DATA SUPPLY CHAIN – HOW TO MAKE IT WORK FEBRUARY 10, 2014 | SLIDE 51
    • CHALLENGES FEBRUARY 10, 2014 | SLIDE 52
    • BIG DATA ARCHITECTURE BIG TRANSACTION DATA Online Transaction Processing (OLTP) Online Analytical Processing (OLAP) & DW Appliances BIG INTERACTION DATA Social Media Data The Power of Hadoop    Other Interaction Data Store huge amounts of data Scaling & cost advantage Complex data analytics & Extensible Call detail records, image, click stream data Scientific, genomic BIG DATA INTEGRATION Machine/Devic e Sales & Marketing Data mart Operational MDM Customer Service Portal BIG DATA PROCESSING BIG DATA ANALYTICS BIG DATA PROCESSING • Extract & Load Data from & into Hadoop (HDFS/Hive) • Parse Complex files in Hadoop framework (Map & Reduce ) Access Discover FEBRUARY 10, 2014 | SLIDE 53 Product & Service Offerings Social Media Big Data Processing Cleanse Integrate Deliver Marketing Campaigns Customer Service Logs & Surveys Account Transactions
    • SOLUTION FLOW FOR BIG DATA REFERENCE ARCHITECTURE Sources Low Latency Update & Distribution Push-down Transformation Access Virtualization Facts EDW Dimensions Transformation DW BI Reports and Dashboards Data Marts Operational MDM Analytic Applications and Portals Proactive Alerts FEBRUARY 10, 2014 | SLIDE 55
    • APPROACH FEBRUARY 10, 2014 | SLIDE 56
    • 10 WAYS BIG DATA IS USED TODAY           FEBRUARY 10, 2014 | SLIDE 57 Understanding and Targeting Customers Understanding and Optimising Business Processes Personal Quantification and Performance Optimisation Improving Healthcare and Public Health Improving Sports Performance Improving Science and Research Optimising Machine and Device Performance Improving Security and Law Enforcement Improving and Optimising Cities and Countries Financial Trading
    • FEBRUARY 10, 2014 | SLIDE 58
    • FEBRUARY 10, 2014 | SLIDE 59
    • BIG DATA OPPORTUNITIES BROAD SPECTRUM OF OPPORTUNITY Enterprise Functions Use Cases Benefits Marketing • • • • Brand Monitoring & PR Gain Customer Insight Campaigns & Events Competitive analysis • Increased campaign effectiveness • Better Brand recall • • • • Service & Support • • • Innovation & Collaboration • • Gain Sales Insight Referrals Mining Lead Capture Lead Generation Gain Support Insight Rapid Response Peer-to-Peer Support armies Customer-led innovation “Exterprise” collaboration • Increased Lead conversion rates • Increased revenue from “Native Social” leads Loyalty & Customer Experience Finance • • • • VIP Experience Loyalty & Incentives Risk Management Fraud Detection Human Resources • Recruitment • Background Verification Sales FEBRUARY 10, 2014 | SLIDE 60 • Better control of online “epidemics” • Lower support costs due to community Knowledge base • Effective product launches • Collaborative R&D with 3rd parties • Employee and partner productivity • Increased loyalty • Unpaid Brand Evangelism • Additional channels for risk forecasting/modeling • Cost-effective recruitment • Better fitting candidates
    • Q&A Dimitri Maesfranckx Division Manager Business Insights +32 479 730131 dimitri.maesfranckx@realdolmen.com Luc Delanglez Solutions Manager Business Insights, MDM +32 474 309053 luc.delanglez@realdolmen.com FEBRUARY 10, 2014 | SLIDE 61
    • REALDOLMEN BUSINESS INSIGHTS – TURN DATA INTO MEANINGFUL INFORMATION BUSINESS INFORMATION FEBRUARY 10, 2014 | SLIDE 62