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 3
BIG DATA

interaction data
transaction data

?
FEBRUARY 10, 2014 | SLIDE 4

data integration
data processing
data analytic...
BIG DATA

interaction data
transaction data
data integration
data processing
data analytics

Social Media
FEBRUARY 10, 201...
BIG DATA

interaction data
transaction data

Machine/Device

data integration
data processing
data analytics
Scientific/Ge...
BIG DATA

interaction data
transaction data
data integration
data processing
data analytics
Online Transaction Processing
...
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,...
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 c...
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 ...
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 Un...
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
b...
DEFINING BIG DATA
EXPLOSIVE GROWTH OF DATA – VOLUME, VARIETY, VELOCITY

Velocity
Business Value

Volume

Data Volume
Acros...
FEBRUARY 10, 2014 | SLIDE 37
PAST GENERATION TECHNOLOGIES WERE NEVER DESIGNED FOR BIG
DATA VOLUMES AND TYPES
Entities

Securities

Customer

Other

Why...
GROWING ADOPTION IN HADOOP
Entities

Securities

Customer

Other

Reference Data

Payments, Trade

Mainframes, Apps

• Hig...
INVESTMENTS IN DATA ANALYTICS, DATA MINING,
DATA VISUALIZATION TECHNOLOGIES

• Improve User Experience vs. previous genera...
BIG DATA TECHNOLOGIES AT WORK
Entities

Securities

Customer

Data Mining, Predictive
Analytics

Other

Reference Data

Ne...
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 da...
BIG DATA MANAGEMENT
Entities

Securities

Customer

Data Mining,
Predictive
Analytics

Other

Payments, Trade

Mainframes,...
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 ...
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

D...
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

D...
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 ...
SOLUTION FLOW FOR BIG DATA
REFERENCE ARCHITECTURE
Sources

Low Latency Update
& Distribution
Push-down

Transformation
Acc...
APPROACH

FEBRUARY 10, 2014 | SLIDE 56
10 WAYS BIG DATA IS USED TODAY













FEBRUARY 10, 2014 | SLIDE 57

Understanding and Targeting Customers
...
FEBRUARY 10, 2014 | SLIDE 58
FEBRUARY 10, 2014 | SLIDE 59
BIG DATA OPPORTUNITIES
BROAD SPECTRUM OF OPPORTUNITY
Enterprise Functions

Use Cases

Benefits

Marketing

•
•
•
•

Brand ...
Q&A

Dimitri Maesfranckx
Division Manager Business Insights
+32 479 730131
dimitri.maesfranckx@realdolmen.com
Luc Delangle...
REALDOLMEN BUSINESS INSIGHTS –
TURN DATA INTO MEANINGFUL INFORMATION

BUSINESS
INFORMATION

FEBRUARY 10, 2014 | SLIDE 62
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Big data voor Sociale Secretariaten

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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

  1. 1. www.realdolmen.com BIG DATA INSPIRATION SESSION 7/2/2014 FEBRUARY 10, 2014 | SLIDE 1
  2. 2. AGENDA      Setting the scene. Defined. Avoiding pitfalls. Practical approach. Open discussion. Use cases & reality. FEBRUARY 10, 2014 | SLIDE 2
  3. 3. FEBRUARY 10, 2014 | SLIDE 3
  4. 4. BIG DATA interaction data transaction data ? FEBRUARY 10, 2014 | SLIDE 4 data integration data processing data analytics
  5. 5. BIG DATA interaction data transaction data data integration data processing data analytics Social Media FEBRUARY 10, 2014 | SLIDE 5
  6. 6. 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
  7. 7. 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
  8. 8. BIG DATA interaction data transaction data data integration data processing data analytics FEBRUARY 10, 2014 | SLIDE 8
  9. 9. BIG DATA interaction data transaction data data integration data processing data analytics FEBRUARY 10, 2014 | SLIDE 9
  10. 10. BIG DATA interaction data transaction data data integration data processing data analytics FEBRUARY 10, 2014 | SLIDE 10
  11. 11. BIG DATA & YOUR MARKET REACH FEBRUARY 10, 2014 | SLIDE 11
  12. 12. BIG DATA & YOUR MARKET REACH Marketing Mix Direct Retail ATL Online + Big Data = Better insight & Value FEBRUARY 10, 2014 | SLIDE 12
  13. 13. USE CASE #1 – SOCIAL TV ANALYTICS FEBRUARY 10, 2014 | SLIDE 13
  14. 14. USE CASE #1 – SOCIAL TV ANALYTICS FEBRUARY 10, 2014 | SLIDE 14
  15. 15. USE CASE #1 – SOCIAL TV ANALYTICS FEBRUARY 10, 2014 | SLIDE 15
  16. 16. USE CASE #1 – SOCIAL TV ANALYTICS FEBRUARY 10, 2014 | SLIDE 16
  17. 17. FEBRUARY 10, 2014 | SLIDE 17
  18. 18. FEBRUARY 10, 2014 | SLIDE 18
  19. 19. USE CASE# 2 – RETAIL CUSTOMER ANALYTICS FEBRUARY 10, 2014 | SLIDE 19
  20. 20. USE CASE# 2 – RETAIL CUSTOMER ANALYTICS FEBRUARY 10, 2014 | SLIDE 20
  21. 21. USE CASE# 2 – RETAIL CUSTOMER ANALYTICS Brand preference Political learnings Reading habits Charitable giving Number of cars FEBRUARY 10, 2014 | SLIDE 21
  22. 22. USE CASE# 2 – RETAIL CUSTOMER ANALYTICS FEBRUARY 10, 2014 | SLIDE 22
  23. 23. USE CASE# 2 – RETAIL CUSTOMER ANALYTICS FEBRUARY 10, 2014 | SLIDE 23
  24. 24. USE CASE# 3 – PERSONAL LIFE PREDICTION Interactions People FEBRUARY 10, 2014 | SLIDE 24 Data patterns
  25. 25. USE CASE# 3 – PERSONAL LIFE PREDICTION Relationship Status Changes Data patterns FEBRUARY 10, 2014 | SLIDE 25
  26. 26. 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
  27. 27. Big Data defined FEBRUARY 10, 2014 | SLIDE 27
  28. 28. FEBRUARY 10, 2014 | SLIDE 28
  29. 29. FEBRUARY 10, 2014 | SLIDE 29
  30. 30. FEBRUARY 10, 2014 | SLIDE 30
  31. 31. 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
  32. 32. VARIETY FEBRUARY 10, 2014 | SLIDE 32
  33. 33. VELOCITY FEBRUARY 10, 2014 | SLIDE 33
  34. 34. FEBRUARY 10, 2014 | SLIDE 34
  35. 35. 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
  36. 36. 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
  37. 37. FEBRUARY 10, 2014 | SLIDE 37
  38. 38. 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?
  39. 39. 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
  40. 40. 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
  41. 41. 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
  42. 42. More Data Volumes Requires Scalable Data Integration Capabilities FEBRUARY 10, 2014 | SLIDE 42
  43. 43. 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
  44. 44. 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
  45. 45. More Data Sources and Types Increases Risk of Data Quality Errors FEBRUARY 10, 2014 | SLIDE 45
  46. 46. 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
  47. 47. Managing More Data Increases the Risk of an Unwanted Data Breach FEBRUARY 10, 2014 | SLIDE 47
  48. 48. 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
  49. 49. Dealing with Big Data Requires an Effective and Efficient Archiving Solution FEBRUARY 10, 2014 | SLIDE 49
  50. 50. 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
  51. 51. BIG DATA SUPPLY CHAIN – HOW TO MAKE IT WORK FEBRUARY 10, 2014 | SLIDE 51
  52. 52. CHALLENGES FEBRUARY 10, 2014 | SLIDE 52
  53. 53. 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
  54. 54. 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
  55. 55. APPROACH FEBRUARY 10, 2014 | SLIDE 56
  56. 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
  57. 57. FEBRUARY 10, 2014 | SLIDE 58
  58. 58. FEBRUARY 10, 2014 | SLIDE 59
  59. 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
  60. 60. 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
  61. 61. REALDOLMEN BUSINESS INSIGHTS – TURN DATA INTO MEANINGFUL INFORMATION BUSINESS INFORMATION FEBRUARY 10, 2014 | SLIDE 62
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