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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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FEBRUARY 10, 2014 | SLIDE 1
www.realdolmen.com
BIG DATAINSPIRATION SESSION
7/2/2014
FEBRUARY 10, 2014 | SLIDE 2
AGENDA
Setting the scene.
Defined.
Avoiding pitfalls.
Practical approach.
Open discussion. Use cases & reality.
FEBRUARY 10, 2014 | SLIDE 3
FEBRUARY 10, 2014 | SLIDE 4
BIG DATA
interaction datatransaction data
data integrationdata processingdata analytics
?
FEBRUARY 10, 2014 | SLIDE 5
BIG DATA
interaction datatransaction data
data integrationdata processingdata analytics
Social Media
FEBRUARY 10, 2014 | SLIDE 6
BIG DATA
interaction datatransaction data
data integrationdata processingdata analytics
Call Detail Records
ClickStream Data
Scientific/Genome
Machine/Device
FEBRUARY 10, 2014 | SLIDE 7
BIG DATA
interaction datatransaction data
data integrationdata processingdata analytics
Online Transaction Processing
Online Analytical Processing & Datawarehouse Appliances
FEBRUARY 10, 2014 | SLIDE 8
BIG DATA
interaction datatransaction data
data integrationdata processingdata analytics
FEBRUARY 10, 2014 | SLIDE 9
BIG DATA
interaction datatransaction data
data integrationdata processingdata analytics
FEBRUARY 10, 2014 | SLIDE 10
BIG DATA
interaction datatransaction data
data integrationdata processingdata analytics
FEBRUARY 10, 2014 | SLIDE 11
BIG DATA & YOUR MARKET REACH
FEBRUARY 10, 2014 | SLIDE 12
BIG DATA & YOUR MARKET REACH
Marketing Mix
Direct Retail ATL Online
+Big Data = Better insight & Value
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
USE CASE #1 – SOCIAL TV ANALYTICS
FEBRUARY 10, 2014 | SLIDE 17
FEBRUARY 10, 2014 | SLIDE 18
FEBRUARY 10, 2014 | SLIDE 19
USE CASE# 2 – RETAIL CUSTOMER ANALYTICS
FEBRUARY 10, 2014 | SLIDE 20
USE CASE# 2 – RETAIL CUSTOMER ANALYTICS
FEBRUARY 10, 2014 | SLIDE 21
USE CASE# 2 – RETAIL CUSTOMER ANALYTICS
Brand preferencePolitical learningsReading habitsCharitable givingNumber of cars
FEBRUARY 10, 2014 | SLIDE 22
USE CASE# 2 – RETAIL CUSTOMER ANALYTICS
FEBRUARY 10, 2014 | SLIDE 23
USE CASE# 2 – RETAIL CUSTOMER ANALYTICS
FEBRUARY 10, 2014 | SLIDE 24
USE CASE# 3 – PERSONAL LIFE PREDICTION
Data patterns
Interactions
People
FEBRUARY 10, 2014 | SLIDE 25
USE CASE# 3 – PERSONAL LIFE PREDICTION
Relationship StatusChanges
Data patterns
FEBRUARY 10, 2014 | SLIDE 26
USE CASE# 3 – PERSONAL LIFE PREDICTION
Data patterns
Interactions
People
Type of relationshipFriendship historyLove history
Friends’ network
Relationship status changesWall-writing patternsrelationship strenghtFlirtation index
Photo tagsCheck-in
Page & photo viewsMessaging history
33% accuracy
FEBRUARY 10, 2014 | SLIDE 27
Big Datadefined
FEBRUARY 10, 2014 | SLIDE 28
FEBRUARY 10, 2014 | SLIDE 29
FEBRUARY 10, 2014 | SLIDE 30
FEBRUARY 10, 2014 | SLIDE 31
Transactions
Interactions
2011 2,7ZB 2015 8ZB 2020 35ZB
Source: An IDC White Paper - sponsored by EMC. As the Economy Contracts, the Digital Universe Expands.
.
VOLUME
FEBRUARY 10, 2014 | SLIDE 32
VARIETY
FEBRUARY 10, 2014 | SLIDE 33
VELOCITY
FEBRUARY 10, 2014 | SLIDE 34
FEBRUARY 10, 2014 | SLIDE 35
Big Data Integration enables an
Organization to:
Do things they could not do before
Do things that they have been doing much more cost effectively
1Business Expects Big Benefits
2Traditional Paradigms
cannot support all of Big Data Challenges
3A New Big Data
processing Platform
emerged to support Big Data requirements
4Complimentary to other
technologies
Variety Complexity
Velocity Volume
BIG DATA OPPORTUNITIES
FEBRUARY 10, 2014 | SLIDE 36
Volume
Source: IDC
LatencyYears Sub-Second
Data Volume
Across Time Scales
Bu
sin
ess V
alu
e
DEFINING BIG DATA
EXPLOSIVE GROWTH OF DATA – VOLUME, VARIETY, VELOCITY
Velocity
Variety
FEBRUARY 10, 2014 | SLIDE 37
FEBRUARY 10, 2014 | SLIDE 38
Based on traditional
relational database
technologies
PAST GENERATION TECHNOLOGIES WERE NEVER DESIGNED FOR BIG
DATA VOLUMES AND TYPES
What happened?
Why did it happen?
• Data mining
• Data analysis
• Business Intelligence
Data warehouse
What will happen?
Social Media, Web Logs
Machine & Device Data
Documents and Emails
Mainframes, Apps
Payments, Trade
Customer
Entities
Reference Data
Other
Securities
FEBRUARY 10, 2014 | SLIDE 39
GROWING ADOPTION IN HADOOP
• Highly scalable: Any data volume/size
• Flexible: Any Data Type (Structure and
Unstructured)
• Cost effective: Leverages commodity
hardware
Social Media, Web Logs
Machine & Device Data
Documents and Emails
Mainframes, Apps
Payments, Trade
Customer
Entities
Reference Data
Other
Securities
FEBRUARY 10, 2014 | SLIDE 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 41
Real-time Data
Visualization
Sentiment Analysis
Data Mining, Predictive
Analytics
Next Gen. Data
Warehouse
BIG DATA TECHNOLOGIES AT WORK
Process, Score,
Analyze All Data
Consume Results
Social Media, Web Logs
Machine & Device Data
Documents and Emails
Mainframes, Apps
Payments, Trade
Customer
Entities
Reference Data
Other
Securities
FEBRUARY 10, 2014 | SLIDE 42
More Data Volumes
Requires Scalable
Data Integration
Capabilities
FEBRUARY 10, 2014 | SLIDE 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 44
Real-time Data
Visualization
Sentiment
Analysis
Data Mining,
Predictive
Analytics
Data Warehouse
BIG DATA MANAGEMENT
Process, Score,
Analyze All Data
Consume Results
Big
Da
ta I
nte
gra
tio
n &
Ma
sk
ing
Business User
Social Media, Web Logs
Machine & Device Data
Documents and Emails
Mainframes, Apps
Payments, Trade
Customer
Entities
Reference Data
Other
Securities
FEBRUARY 10, 2014 | SLIDE 45
More Data Sources
and Types Increases
Risk of Data Quality
Errors
FEBRUARY 10, 2014 | SLIDE 46
Real-time Data
Visualization
Sentiment
Analysis
Data Mining,
Predictive
Analytics
Data Warehouse
BIG DATA MANAGEMENT
Process, Score,
Analyze All Data
Consume Results
Big
Da
ta I
nte
gra
tio
n &
Ma
sk
ing
Business User
Data Quality & Governance
Data Analyst Data OwnerData Steward Developer
Social Media, Web Logs
Machine & Device Data
Documents and Emails
Mainframes, Apps
Payments, Trade
Customer
Entities
Reference Data
Other
Securities
FEBRUARY 10, 2014 | SLIDE 47
Managing More Data
Increases the Risk of
an Unwanted Data
Breach
FEBRUARY 10, 2014 | SLIDE 48
Real-time Data
Visualization
Sentiment
Analysis
Data Mining,
Predictive
Analytics
Data Warehouse
BIG DATA MANAGEMENT
Process, Score,
Analyze All Data
Consume Results
Big
Da
ta I
nte
gra
tio
n &
Ma
sk
ing
Business User
Data Quality & Governance
Da
ta P
riva
cy E
nfo
rce
me
nt
Data Analyst Data OwnerData Steward Developer
Social Media, Web Logs
Documents and Emails
Mainframes, Apps
Payments, Trade
Customer
Entities
Reference Data
Other
Securities
FEBRUARY 10, 2014 | SLIDE 49
Dealing with Big Data
Requires an Effective and
Efficient Archiving
Solution
FEBRUARY 10, 2014 | SLIDE 50
Real-time Data
Visualization
Sentiment
Analysis
Data Mining,
Predictive
Analytics
Data Warehouse
BIG DATA MANAGEMENT
Process, Score,
Analyze All Data
Consume Results
Big
Da
ta I
nte
gra
tio
n &
Ma
sk
ing
Business User
Data Quality & Governance
Big Data Archiving and Retention
Da
ta P
riva
cy E
nfo
rce
me
nt
Data Analyst Data OwnerData Steward Developer
Social Media, Web Logs
Documents and Emails
Mainframes, Apps
Payments, Trade
Customer
Entities
Reference Data
Other
Securities
FEBRUARY 10, 2014 | SLIDE 51
BIG DATA SUPPLY CHAIN – HOW TO MAKE IT WORK
FEBRUARY 10, 2014 | SLIDE 52
CHALLENGES
FEBRUARY 10, 2014 | SLIDE 53
BIG DATA ARCHITECTURE
The Power of Hadoop Store huge amounts of data
Scaling & cost advantage
Complex data analytics & Extensible
Online
Transaction
Processing
(OLTP)
Online Analytical
Processing
(OLAP) &
DW Appliances
Social
Media Data
Other
Interaction Data
Scientific, genomic
Machine/Devic
e
BIG TRANSACTION DATA BIG INTERACTION DATA
BIG DATA PROCESSING
Call detail
records, image,
click stream data
Big Data Processing• Extract & Load Data from & into Hadoop (HDFS/Hive)
• Parse Complex files in Hadoop framework (Map & Reduce )
BIG DATA INTEGRATION
Access Discover Cleanse Integrate Deliver
Product &
Service
Offerings
Social Media Customer
Service Logs &
Surveys
Marketing
Campaigns
Account
Transactions
Sales &
Marketing
Data mart
Customer
Service
Portal
Operational
MDM
BIG DATA PROCESSINGBIG DATA ANALYTICS
FEBRUARY 10, 2014 | SLIDE 55
SOLUTION FLOW FOR BIG DATA
REFERENCE ARCHITECTURE
EDWDimensions
FactsBI Reports and Dashboards
Analytic
Applications
and Portals
Sources
DW
Operational
MDM
Push-down
Transformation
AccessVirtualization
Transformation
Data
Marts
Proactive Alerts
Low Latency Update
& Distribution
FEBRUARY 10, 2014 | SLIDE 56
APPROACH
FEBRUARY 10, 2014 | SLIDE 57
10 WAYS BIG DATA IS USED TODAY
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
FEBRUARY 10, 2014 | SLIDE 60
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
Sales • Gain Sales Insight
• Referrals Mining
• Lead Capture
• Lead Generation
• Increased Lead conversion rates
• Increased revenue from “Native Social”
leads
Service & Support • Gain Support Insight
• Rapid Response
• Peer-to-Peer Support armies
• Better control of online “epidemics”
• Lower support costs due to community
Knowledge base
Innovation & Collaboration • Customer-led innovation
• “Exterprise” collaboration
• Effective product launches
• Collaborative R&D with 3rd parties
• Employee and partner productivity
Loyalty & Customer
Experience
• VIP Experience
• Loyalty & Incentives
• Increased loyalty
• Unpaid Brand Evangelism
Finance • Risk Management
• Fraud Detection
• Additional channels for risk
forecasting/modeling
Human Resources • Recruitment
• Background Verification
• Cost-effective recruitment
• Better fitting candidates
FEBRUARY 10, 2014 | SLIDE 61
Q&A
Dimitri Maesfranckx
Division Manager Business Insights
+32 479 730131
Luc Delanglez
Solutions Manager Business Insights, MDM
+32 474 309053
FEBRUARY 10, 2014 | SLIDE 62
REALDOLMEN BUSINESS INSIGHTS –
TURN DATA INTO MEANINGFUL INFORMATION
BUSINESS
INFORMATION