Dallas Digital Summit: 6 Steps to Big Data Success

© 2014 IBM Corporation
Big Data: 6 Steps to Success

Sameer Khan
Senior Product Marketing Manager
& Digital Leader
IBM Customer Analytics
@sameerkhan
© 2014 IBM Corporation @IBMExpOne | #smartercommerce

IBM Acknowledgements & Disclaimers
 Availability. References in this presentation to IBM products, programs, or services do not imply that they will be available in all countries in which IBM operates.
 The workshops, sessions and materials have been prepared by IBM or the session speakers and reflect their own views. They are provided for informational purposes only, and
are neither intended to, nor shall have the effect of being, legal or other guidance or advice to any participant. While efforts were made to verify the completeness and accuracy
of the information contained in this presentation, it is provided AS-IS without warranty of any kind, express or implied. IBM shall not be responsible for any damages arising out of
the use of, or otherwise related to, this presentation or any other materials. Nothing contained in this presentation is intended to, nor shall have the effect of, creating any
warranties or representations from IBM or its suppliers or licensors, or altering the terms and conditions of the applicable license agreement governing the use of IBM software.
 All customer examples described are presented as illustrations of how those customers have used IBM products and the results they may have achieved. Actual environmental
costs and performance characteristics may vary by customer. Nothing contained in these materials is intended to, nor shall have the effect of, stating or implying that any
activities undertaken by you will result in any specific sales, revenue growth or other results.
– U.S. Government Users Restricted Rights - Use, duplication or disclosure restricted by GSA ADP Schedule Contract with
Sunil S. Patel
Technical Sales Manager
IBM Sales & Distribution
sspatel@us.ibm.com
@docsocial
 © Copyright IBM Corporation 2011. All rights reserved.
IBM Corp.
– Please update paragraph below for the particular product or family brand trademarks you mention such as WebSphere, DB2,
Maximo, Clearcase, Lotus, etc.
 IBM, the IBM logo, ibm.com, [IBM Brand, if trademarked], and [IBM Product, if trademarked] are trademarks or registered trademarks of
International Business Machines Corporation in the United States, other countries, or both. If these and other IBM trademarked terms are
marked on their first occurrence in this information with a trademark symbol (® or ™), these symbols indicate U.S. registered or common
law trademarks owned by IBM at the time this information was published. Such trademarks may also be registered or common law
trademarks in other countries. A current list of IBM trademarks is available on the Web at “Copyright and trademark information” at
www.ibm.com/legal/copytrade.shtml
 Other company, product, or service names may be trademarks or service marks of others.

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

Disruptive forces impact long standing business models across
industries
Shift of power to the
consumer
“Data is the new oil.
Data is just like crude.
It’s valuable, but if unrefined
it cannot really be used.”
– Clive Humby
“We have an economy based on a
resource that is not only renewable,
but self-generating. Running out is
not a problem, drowning in it is.”
– John Naisbitt
Pressure to do more
with less
Proliferation of big
data

Lots of data, not enough answers
The departmental analytic approaches of the past are
Only 26%of companies
have a well-developed
strategy in place for
improving customer
experience.*
58%of companies
have limited or no
understanding of which
usability issues affect
conversion*
91%of companies
have limited or no
understanding of why
people leave their site
without converting*
* Econsultancy - 2013
insufficient for the needs of today

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

Shift In Algorithms: Generalized to Personalized
http://en.wikipedia.org/wiki/Medical_algorithm

We all have…

Big Data Steps to Success
10
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

Step 1: Identify the Business Problem
11

Understanding the Business Challenges before Developing Big Data
Solutions
“I can’t find the right
answers fast
enough to support
my customers.”
12
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.”
?

Step 2: Seek only needed data
13

Identify the Types of Data Sources and its Value

Step 3: Standardize Your Data
15

Leveraging data still faces many obstacles
Source: Analytics: The New Path to Value, a joint MIT Sloan Management Review and IBM Institute for Business Value study. Copyright © Massachusetts
Institute of Technology
have a limited
understanding of how to
use analytics
38%
Self-service analytics and
expectations to drive better
data-driven decisions are
rising
of the time is spend
80% on data preparation
Making decisions rapidly is
no longer a goal; it’s an
imperative
find it difficult to get
24% data
Access to required
data sources is critical while
maintaining governance
standards

Even a simple analytics project has multiple steps and people
Data
Access
Business IT
Analysts
Data
Preparation
Analysis
Validation
Reporting
Collaboration
Data Scientists
and Statisticians
Business Users

Step 4: Pick the Right Model
18

Improving the customer experience by better understanding behaviors drives
almost half of all active big data efforts
Big data objectives
Customer-centric outcomes
Operational optimization
Risk / financial management
New business model
Employee collaboration
Top functional objectives identified by organizations with active big data pilots
or implementations. Responses have been weighted and aggregated.
Total respondents n = 1061
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

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

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

MDM provides the enterprise backbone for an extended 360 degree view
Customer
Office
Prospect
Person
Agreement
Provider
Citizen
Distributor
Member
Employee
Company
Trading
Partner
Organization Supplier
Vendor
Product
Service Financial
Account
Contract
Assets
Locations
Manufactured
Goods

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

Step 5: Validate and Test
25

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

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

Integrated
social business
IBM Watson Analytics
Unified analytics experience
Visual
storytelling
Intelligent
automation
Data access
and
refinement
Report and
dashboard
creation
Guided
analytic
discovery
28
Free Trial At
WatsonAnalytics.com

Architecture Overview – Social Analytics Use Case
Visualization
Big Data Analytical services
Geospatia
l
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

Step 6: Execute and Measure Results
30

Four value pillars represent ROI potential for big data exploration
31
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

Big Data Success Stories
32

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
33

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.
34

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
35

Questions?
36

Schedule an IBM Big Data Workshop
Next Steps and Resources
Resources:
IBM Big Data Hub
http://www.ibmbigdatahub.com
WatsonAnalytics.com
BigDataUniversity.com
Books / analyst papers
 Free of charge
 Best practices
 Industry use cases
 Business uses
 Business value assessment
37

© 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.
Copyright and Trademarks

Dallas Digital Summit: 6 Steps to Big Data Success

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Viewers also liked

Viewers also liked (20)

Similar to Dallas Digital Summit: 6 Steps to Big Data Success

Similar to Dallas Digital Summit: 6 Steps to Big Data Success (20)

More from Sameer Khan

More from Sameer Khan (6)

Recently uploaded

Recently uploaded (20)

Dallas Digital Summit: 6 Steps to Big Data Success