The document discusses the transformative power of big data in corporate settings, highlighting its ability to improve decision-making by analyzing large volumes of diverse data. It outlines the significance of analytics in uncovering insights from unstructured data and emphasizes the necessity of developing an analytical culture within organizations for competitive advantage. Additionally, it provides a roadmap for implementing big data analytics to enhance business operations and customer engagement.

1. Transforming Big Data
into Corporate Gold
Louis M Fernandes
Director, Market Development
Copyright © 2012, SAS Institute Inc. All rights reserv ed.

2. Who do you trust?
2
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3. What is “Big Data”?
“When volume, velocity and variety of data
exceeds an organisation’s storage or compute
capacity for accurate and timely decision-making”
3
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4. Data Vectors
olume
ariety
elocity
isibility
alue
4
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5. What is “Analytics”? TEXT ANALYTICS
Finding treasures in unstructured data
like social media or survey tools
that could uncover insights
FORECASTING about consumer sentiment
Leveraging historical data
to drive better insight into
decision-making
for the future
BUSINESS
INTELLIGENCE
OPTIMISATION
Analyse massive
DATA MINING amounts of data in
Mine transaction databases order to accurately
for data of spending patterns identify areas likely to
that indicate a stolen card produce the most
profitable results
STATISTICS 5
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6. Environment
CULTURE
BEST
BUSINESS
OUTCOMES
DATA
INFRASTRUCTURE
TECHNOLOGY
6
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7. Culture & Maturity
7
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8. Competitive
Advantage
Culture & Maturity
Analytic Reporting Alerts
What actions are needed?
Query
drill down Where exactly is the problem?
Ad hoc
Standard reports How many, how often, where?
Clean reports
Raw data What happened?
data
Degree of Intelligence 8
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9. Culture & Maturity
What is the best that can happen? Optimise
Predictive Analytics
What will happen next? Predict
& Optimisation
Competitive
Advantage
Why is this happening? Explore
Alerts
What actions are needed?
Query
drill down Where exactly is the problem?
Ad hoc
Standard reports How many, how often, where?
Clean reports
Raw data What happened?
data
Degree of Intelligence 9
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10. ANALYTICAL
Culture & Maturity ANALYTICAL
COMPETITOR
COMPANY STAGE 5
Organization
ANALYTICAL
4 is routinely
Business & IT Alignment
ASPIRATIONS STAGE reaping the
Enterprise-wide benefits of
LOCALIZED
ANALYTICS STAGE 3 analytics
capability
enterprise-wide
analytics
Senior is under
ANALYTICALLY
IMPAIRED STAGE 2 executives are
committed to
development
as a corporate
Focus on
Line-of-business analytics continuous
priority improvement
STAGE 1 management
are driving Resources
Limited interest analytics are aligned to
in analytics momentum create a broad
by senior
analytics
management
capability
Analytic Maturity
10
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11. ANALYTICAL
Culture & Maturity ANALYTICAL
COMPETITOR
COMPANY STAGE 5
Organization
ANALYTICAL
4 is routinely
Business & IT Alignment
ASPIRATIONS STAGE reaping the
Enterprise-wide benefits of
LOCALIZED
ANALYTICS STAGE 3 analytics
capability
enterprise-wide
analytics
Senior is under
ANALYTICALLY
IMPAIRED STAGE 2 executives are
committed to
development
as a corporate
Focus on
Line-of-business analytics continuous
priority improvement
STAGE 1 management
are driving Resources
Limited interest analytics are aligned to
in analytics momentum create a broad
by senior
analytics
management
capability
Analytic Maturity
11
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12. Infrastructure
Increase Loyalty, Interaction, Reach and Penetration
Deliver relevant content and increase audience
Evolve from a “Product Centric” through to “Consumer Centric” and “Lifecycle Focused” organisation
Analyse, Understand & Plan
Predict what products and offerings best fit consumer at any point in their particular lifecycle
Measure Use & Effectiveness
Understand when, how and why consumers use your products and services
Manage the end-to-end “back-office” process
Integrate the various data-sources into a central single data “hub”
and use it to power the various online and offline consumer touch-points
12
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13. Company
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14. Unit/Channel
Company Manager
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15. Unit/Channel Business Unit/
Company Manager Communication Channel
Email
Mobile
Web Personalisation
Point of Sale/Service
Social Media
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16. Unit/Channel Business Unit/
Company Manager Communication Channel Customer
Email
Mobile
Web Personalisation
Point of Sale/Service
Social Media
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17. Unit/Channel Business Unit/
Company Manager Communication Channel Customer
Email
Mobile
Web Personalisation
Point of Sale/Service
Social Media
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18. Unit/Channel Business Unit/
Company Manager Communication Channel Customer
Email
Mobile
Web Personalisation
Point of Sale/Service
LEGACY DATA
(e.g. Purchase History,
POS, etc) Social Media
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19. SINGLE CUSTOMER VIEW
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20. QUESTIONS QUESTIONS QUESTIONS
QUESTIONS QUESTIONS
1. How does my value 1. How do I find new 1. How do I interact with my
proposition relate to my customers? customers?
1. Who are my customers? 1. Who has interacted with
various customers? 2. What are the best 2. How do I ensure
2. What do they “look” like? my campaigns?
2. What aspects of its utility/ channels to reach them consistency across
3. What customer needs am I 2. What have they done?
service will they value? through? interaction channels?
satisfying? 3. How much have I sold?
3. How should I position it? 3. How do I execute 3. How do I optimise the
4. What is the market 4. How much will I sell
4. How should I engage with campagins? customer experience?
opportunity? tomorrow?
my prospects and 4. How do I optimise 4. How do my customers
customers? campaigns? engage with me?
INTERACTION &
MEASUREMENT
EXPERIENCE &
ENGAGEMENT
EXPERIENCE
EXPERIENCE
MARKETING
& ANALYSIS
CUSTOMER
CUSTOMER
CUSTOMER
CUSTOMER
STRATEGY
PLANNING
DESIGN
SINGLE CUSTOMER VIEW
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22. Email/Mobile
Point of Sale
Web Personalisation
Point of Sale/Service
Social Media
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23. Email/Mobile
Point of Sale
Business rules
Web Personalisation
Point of Sale/Service
Social Media
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24. Email/Mobile
Point of Sale
Content Distribution
Business rules
Web Personalisation
Point of Sale/Service
Social Media
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25. Email/Mobile
Point of Sale
Content Distribution
Business rules
Web Personalisation
Point of Sale/Service
Social Media
SCV
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26. Email/Mobile
Point of Sale
Content Distribution
Business rules
Web Personalisation
Point of Sale/Service
Social Media
SCV
The “Closed loop”
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27. Email/Mobile
Point of Sale
Content Distribution
Business rules
Web Personalisation
Point of Sale/Service
Data clean, merge & append
Social Media
SCV
The “Closed loop”
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28. Email/Mobile
Point of Sale
Content Distribution
Business rules
Web Personalisation
Point of Sale/Service
Reporting /
Data clean, merge & append
Analytics
Social Media
SCV
The “Closed loop”
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29. Modelling “Sandpit” Email/Mobile
Point of Sale
Content Distribution
Business rules
Web Personalisation
Point of Sale/Service
Reporting /
Data clean, merge & append
Analytics
Social Media
SCV
The “Closed loop”
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30. Modelling “Sandpit” Email/Mobile
Point of Sale
Content Distribution
HIGH
Business rules
Web Personalisation
PERFORMANCE
ANALYTICS Point of Sale/Service
Reporting /
ENVIRONMENT
Data clean, merge & append
Analytics
Social Media
SCV
The “Closed loop”
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31. High Performance Analytics
Operational Business
Constraints Objectives
Optimise
Relationship Conversation E-mail, SMS, Digital
Call Centre
Manager Centre MMS, Push Strategy
HIGH VALUE HIGH TRANSACTION
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32. Technology
32
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33. Big Data Analytics Deployment Roadmap
Driving Value Customer Management Align Incentives
Across The
Business Insight Led Online Experience SMART Services
Insight Led Credits / Product Bundles Engaging in Social Media
Monthly Campaign Reports
Insight Led Direct
Marketing Optimisation Pilot Opt. Roll-out Multi-channel Opt. / BNA Real Time Decisions
BNA Test and Learn Process
Insight Governance
Effective Insight
Processes Ways of Working New Insight / Business Partner Model
Self-Serve Reporting Capability Self-Serve Insight Capability
Segmentation & Customer Journeys SMART / Big Data Insights
360o Actionable
Insight Customer Lens Reporting
Propensity Models Self Calibrating Models
Data Governance
Big Data
Management Etiquette / SCV for Campaigns SCV – SMART / Digital SCV – Voice, Social Media
SCV – Partner Ecosystem
Start +6 months +12 months +18 months +24 months +30 months +36 months
33
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35. Business Impact
Act
Orient
YOUR
Decide Decide COMPETITIVE
MARKET ADVANTAGE
OPPORTUNITY Orient
Act
Observe
35
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36. Value
Past Now Future
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37. Value
Past Now Future
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38. Value
Predict
Window of opportunity
Past Now Future
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39. Value
Optimise
Value
Predict
Window of opportunity
Past Now Future
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40. Value Optimise
Value
Predict
Window of opportunity
Past Last Week Now Future
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41. Customer Lifetime Value
Customer
Profitability
Standard Customer Journey
Optimised Customer Journey
Missed Opportunity
R.O.I
Time
Cost of Acquisition
Acquisition Welcome In-Life Retention Post-Life
41
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42. Market Performance
300% Source:
250%
200%
Analytical Shakers
150%
100%
50%
S&P 500 Index
0%
2002 2003 2004 2005 2006 2007 2008 2009
Market Outperformance Advanced Analytics Data Access vs. Productivity
64% 77% 54%

44. Emerging Stable Best in Class Transformational
Find the Most  Legacy systems  Marketing running on  Single customer view  Institutional memory
 Isolated data sources operational systems  Marketing data marts  Corporate resource
Profitable Growth  Outsourced platforms  Partial centralisation of  Specialist marketing service  Customer data enterprise-wide
Opportunities  Product and channel centric customer data providers  Full understanding of
insight  Some data matching  Customer-centric insight individual customer
 Customer to product / channel  Segmentation by customer profitability
insight behaviour  Develop for customer
 Product segmentation  Granular segments  Customer pricing
 Basic customer value
Take The Right  List and product-based  Product and customer targeted  Customer targeting  Integrated real-time, multi-
targeting  Few channel focus  Optimised Multi-channel channel contact management
Marketing Action  Product-based contact strategy  Campaign programs partly contact management  Full personalisation of products
 Short-term focus multi-stage  Single-channel real-time / services based on customer
 Fixed event triggers  Campaign automation value
 Dynamic event triggers
Maximise Cross  Success defined by volume of  Standard and insight based  Test and learn embedded  Insight informs a customer led
product / service sales reporting  Integrated reporting strategy
Business Impact  Basic reporting  Test and learn processes  Dynamic marketing dashboard  Insight is used to inform all of
 Ad-hoc reports  Consolidated reports the value levers of the
business
Product Customer
Centricity Centricity
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45. Summary
 Why Big Data use in business intelligence and visualisation is
changing how business runs
 How Big Data is being applied to make business decisions in real
time
 How Big Data is improving business outcomes
 How organisations are developing a multi-dimensional view of their
businesses
 How to build an analytical and insight-driven organisation that
encourages continuous improvement.
45
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46. Thank You!
Louis M Fernandes
Director, Market Development, SAS UK
E: louis.fernandes@sas.com
T: +44 7958 292616
Copyright © 2012, SAS Institute Inc. All rights reserv ed.