The Big Data phenomenon was all about the collection of masses and masses of data: it was a technology challenge. But for most of us, this is no longer a problem – we know how to collect the data – the challenge now is one of processing the data, to make smart data work for us. In this session, IBM’s Sameer Khan will outline an action plan to manage your data and make it smart. He will be ably supported by Andrew Bailey, who will bring his experience with using smart data for integrated marketing campaigns to show you how it is put into action at a company like FedEx.

1. Integrated Certificate
Integrate Your Big Data
Sameer Khan
Analytics, Marketing & Tech Leader, IBM
Andrew (Drew) Bailey
Marketing Principal, FedEx
10/28/2014

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3. Agenda
• The current landscape
• Big data success steps
• Case studies
• Questions

5. The demand for big data solutions is real
The healthcare industry spends roughly $250 billion on fraud, per
year. By 2016, this could grow to more than $400 billion a year.1
One rogue trader at a leading global financial services firm created
$2 billion worth of losses, almost bankrupting the company.
$93 billion in total sales is missed each year because retailers
don’t have the right products in stock to meet customer demand.
6 billion global subscribers in the telco industry are demanding
unique and personalized offerings that match their individual
lifestyles.2

6. We all have…

7. Goal: Maximize Big Data to Drive Revenue Through Sound
Centric Data Strategies
Executives
• Strategic Decisions
Managers
• Tactical Decisions
Employees
• Operational Decision
Five Steps to
A Data-Driven Future
1. Make Smart Investments
2. Recognize the Importance of
Human Ingenuity
3. Leverage Open Data
4. Make Good Public Policy
5. Avoid Databuse
www.uschamberfoundation.org/future-data-driven-innovation

8. Big Data Steps to Success
Step 1: Identify the business problems
Step 2: Seek on the needed data
Step 3: Standardize your data
Step 4: Pick the right model
Step 5: Validate and test
Step 6: Execute and measure results
8

9. Step 1: Identify the business problem (ask the right question).

10. Understanding the Business Challenges before
Developing Big Data Solutions
10
External
Content
(Twitter, News
Feeds...)
Internal Content
(CRM, Warehouses,
ERP, ECM...)
“I am monitoring all
angles – yet I
can’t connect the
dots.”
“I don’t know what I
don’t know –
where is my
business
exposed?”
“I can’t unlock the value
in my data to drive
economic value to
my business.”
“Innovation is falling
short as I am unable
to see the full
research picture.”
“I can’t find the right
answers fast
enough to support
my customers.”
?

11. Step 2: Look at the data elements you need.
STEP 2: SEEK ONLY NEEDED DATA

12. Identify the Types of Data Sources and its Value

13. Step 3: Standardize your data and associated practices.
STEP 3: STANDARDIZE YOUR DATA

14. STEP 4: PICK THE RIGHT MODEL

15. Improving the customer experience by better understanding
behaviors drives almost half of all active big data efforts
Customer-centric outcomes
 Digital connections have enabled
customers to be more vocal
about expectations and
outcomes
 Integrating data increases the
ability to create a complete
picture of today’s ‘empowered
consumer’
 Understanding behavior patterns
and preferences provides
organizations with new ways to
engage customers
Big data objectives
Top functional objectives identified by organizations with active big data pilots or
implementations. Responses have been weighted and aggregated.
Total respondents n = 1061
Customer-centric outcomes
Operational optimization
Risk / financial management
New business model
Employee collaboration
Source: Analytics: The real-world use of big data, a collaborative research study by the IBM Institute for
Business Value and the Saïd Business School at the University of Oxford. © IBM

16. Customer Centric Model Scenarios
Scenarios
1. Optimize By Channel Approach
2. Integrated Multi-Channel Approach
3. Master Data Management (MDM) Approach
4. Hybrid Approach
Source: CRM Ecosystem, Forrester

17. • Digital Analytics and
scoring
• Product Clustering
• Customer
Segmentation
General
/Descriptive
Analytics
Predictive
Analytics • Recommend offers
• Sales revenue & volume
• Profitability
• Customer lifetime value
• Recommend products
• Recommend prices
Prescriptive
Analytics
Integrated Multichannel Approach

18. Integrated Multichannel Approach Use Cases
General
/Descriptive
Analytics
Predictive
Analytics
Prescriptive
Analytics
Similar Behaviors
Similar Needs
Similar Web
Navigation Challenges
Predicting Sales
Volume
Everyday
Price
Market
Trend
Inventory
Market
Trend
Competitors
Seasonality Gift Cards
In-store
Displays
Cross channel
Products/Price
Recommendations

19. MDM provides the enterprise backbone for an extended 360 degree view
What is Master Data?
Master data is the high-value,
core information used to
support critical business
processes across the
enterprise
Master Data is business critical
information about customers,
suppliers, partners, products,
materials, employees,
accounts and more
Why is it important?
Master Data is at the heart of
every business transaction,
application and decision
Quality of data degrades over
time and negatively impacts
key business processes if
Master Data is inaccurate,
missing, duplicated or
incomplete
Customer
Office
Prospect
Person
Agreement
Citizen
Distributor
Member
Provider
Employee
Company
Trading
Partner
Organization Supplier
Vendor
Product
Service Financial
Account
Contract
Assets
Locations
Manufactured
Goods

20. Customer search:
MDM draws in all related
records: J Robertson, Janet
Robertson, Jan Baker
Janet Robertson
MDM enables a complete
purchase history,
including Jan Baker’s
records from before 2011
Customer’s
Products
from MDM
Customer info
from MDM
Indexed 3rd party
information related
to customer
Unstructured
internal information
related to customer

21. STEP 5: VALIDATE AND TEST

22. Putting the Selected Model to Test
Discover
Connect securely to all data sources
Provide unified search and navigation
Surface relationships & themes
Assess
Identify the value of the data
Recognize users of the data
Establish context of data usage
Collaborate
Augment the data with user knowledge
Create personalized views of the data
Identify ongoing integration points
Leverage
Build compelling applications using all of your
data

23. Integrated Big Data Infrastructure
Compelling applications
incorporating all data types
and sources
Integration leverages
core components of
the platform
Secure access to a
broad range of
enterprise systems
© 2013 IBM Corporation
Data Explorer App Builder
CM, RM, DM RDBMS Feeds Web 2.0 Email Web CRM, ERPFile Systems
Connector
Framework
BigInsights
Integration & Governance
UI / User
Streams Data Explorer Warehouse

24. Architecture Overview – Social Analytics Use Case
Visualization
Big Data Analytical services
Geospatial
Analysis
Social Data
Analytics
Notify of
event analysis
feed
Trending
Analytics (time
series)
(latitude, longitude); keywords
Existing social media
firehose
Discover Assess Collaborate Leverage
Apps

25. STEP 6: EXECUTE & MEASURE RESULTS

26. Four value pillars represent ROI potential for big data exploration
26
Improve
Productivity
Reduce Risk &
Improve Compliance
Leverage
Existing Assets Increase Revenue
Eliminate data
silos
Leverage
existing research
and knowledge
Eliminate/retire
unused systems
Extract value
from existing
assets
Reduce training
costs
Improve staff
retention
Improve
collaboration
Capture tribal
knowledge
Eliminate
redundant
projects
Equip sales and
service staff with
current, accurate
info
Increase upsell
and cross-sell
Reduce sales
cycle
Increase
customer lifetime
value
Recommendations
Reduce time to
monitor and
comply
Push relevant
regulatory
updates/alerts
Honor pricing,
NDAs, etc.
Single version
of the truth
Avoid penalties

27. BIG DATA SUCCESS STORIES

28. Duke University Health System
gets smarter for its patients
Need
• Transform healthcare to provide patients with the
capability to proactively manage their health;
adapt best practices to rapidly determine which
treatments and approaches work best – and which
don’t
Benefits
• Increased patient engagement from zero to
30,000 users in less than three months
• HealthView is now used by more than 150,000
patients, roughly one third of Duke’s overall
patient base
• Collected US $16 million in co-pays through the
new portal
28

29. Will you buy a car today? IBM
SPSS Statistics helps Fiat identify
the most likely customers and
prospects.
Need
• Determine the likelihood that future and
returning customers would buy specific
brands/models of Fiat cars, so dealers could
optimize available marketing funds. Also, needed
to better understand customer experience
w/dealerships and repair facilities.
Benefits
• Improved customer response rate to marketing
initiatives by 15-20 percent.
• Improved customer loyalty by 7 percent.
• Supports continuous improvement of dealerships
and repair facilities.
• Centralized analytical reporting and modeling
system enhances productivity and lowers costs.
• Efficiently works with large Oracle database
containing history on 64 million customers.
29

30. First Tennessee Bank: Analytics
drives higher ROI from
marketing programs
Need
• An accountability framework that looked at overall
marketing spending and the results that spending
generated for the bank.
Benefits
• 600% overall return on its investment through more
efficiently allocated marketing resources
• 3.1% increase in marketing response rate through
more accurate targeting of offers to high-value
customer segments
• 20% reduction in mailing costs and 17% reduction in
printing costs due to the ability to target the most
attractive segment for specific offers
30

31. QUESTIONS

32. Next Steps and Resources
Resources:
IBM Big Data Hub
http://www.ibmbigdatahub.com
BigDataUniversity.com
Books / analyst papers
Schedule an IBM Big Data Workshop
32
 Free of charge
 Best practices
 Industry use cases
 Business uses
 Business value assessment

33. Copyright and Trademarks
© IBM Corporation 2014. All Rights Reserved.
IBM, the IBM logo, ibm.com are trademarks or registered trademarks of
International Business Machines Corp., registered in many jurisdictions
worldwide. Other product and service names might be trademarks of IBM or
other companies. A current list of IBM trademarks is available on the Web at
“Copyright and trademark information” at www.ibm.com/legal/copytrade.shtml.