#IBMInsight Session presentation "Transforming your Enterprise to Get Value from BigData and Analytics: How to Get Started". Transforming Your Enterprise to Get Value from Big Data and Analytics: How to Get Started The Journey, The Value Analytics Drives, Analytics Leadership and Governance, Analytics Case Studies, Best Practices for Getting Started More at ibm.biz/BdEPRs

1. IBM IOD 2012 10/24/2014
Transforming your Enterprise to Get
Value from Big Data and Analytics:
How to Get Started
Session # 6371
Douglas Dow & Emily Plachy
October 27, 2014
© 2014 IBM Corporation
Transforming Your Enterprise to Get Value from Big Data
and Analytics: How to Get Started
• The Journey
• The Value Analytics Drives
• Analytics Leadership and Governance
• Analytics Case Studies
• Best Practices for Getting Started
• Conclusions
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2. IBM IOD 2012 10/24/2014
The Journey
IBM’s Transformation Journey has been years in the
making with the analytics portion starting in 2004
• Deliver a signature IBM client experience with an engaged workforce
• Build a Smarter enterprise with data, cloud and systems of engagement
• Make IBM essential to clients, partners, investors and communities
Making things smarter
Analytics
Early years analytics applied to physical assets, i.e. manufacturing, supply chain
Then analytics applied to non physical processes / functional side i.e. sales
Now expansion more broadly used across the enterprise
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Transformation
designed to:
Sharing & partnering
Globally integrating
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3. IBM IOD 2012 10/24/2014
IBM’s Analytics Transformation is focused on business
outcomes
“Analytics will form a
silver thread that weaves
through the future of
everything we do.” Ginni
Rometty, Chairman and CEO,
IBM Corporation
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Fundamental Principles
• Pragmatic approach
• Focus on business outcomes
• Analytics is a way of doing business
Basic Building Blocks
• IBM Institute for Business Value Papers
• Great base for transformation with
value services structure
• Motivated leadership to make IBM
smarter and essential
The Value Analytics Drives
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4. IBM IOD 2012 10/24/2014
First, what do we mean by analytics?
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IDC – Independent Financial Impact Studies
“The median ROI for the
projects that incorporated
predictive technologies was
145%, compared with a
median ROI of 89% for those
projects that did not.”
Source: IDC, “Predictive Analytics and ROI:
Lessons from IDC’s Financial Impact Study”
Update: 2011 study shows ROI for predictive analytics at 250%!
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5. IBM IOD 2012 10/24/2014
Understanding how to create value from data has been the
focus of IBM’s analytics studies for 5 years.
A blueprint for value
2010 2011 2012 2013
Extracting value
from data and
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Analytics:
The new path to value
Operationalizing
analytics in
sophisticated
organizations
Analytics:
The widening
divide
Mastering analytic
competencies
Analytics:
The real world use
of big data
Fundamentals
of big data
Analytics:
analytics
The intelligent enterprise
and
Breaking away with BAO
2009
Defining analytics
as a strategic
asset
MIT Sloan Management Review & IBM Institute of Business
Value teamed up in 2010
IBM Institute for Business Value
•Surveyed 3,000 executives, managers and
analysts plus extensive interviews
•Respondents represent more than 30
industries in 108 countries
•Interviews with IBM and MIT thought leaders
•Analysis by IBM and MIT Sloan Management
Review team
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+
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6. IBM IOD 2012 10/24/2014
The use of analytics correlates to performance
3x 5.4x
Top Performers are more
likely to use an analytic
approach over intuition*
*within business processes
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Organizations that lead in
analytics outperform those
who are just beginning to
adopt analytics
Source: Analytics: The New Path to Value, a joint MIT Sloan Management Review and IBM Institute of Business Value study. Copyright © Massachusetts
Institute of Technology 2010.
Organizational obstacles, not data or financial concerns
are holding back adoption
Source: Analytics: The New Path to Value, a joint MIT Sloan Management Review and IBM Institute of Business Value study. Copyright © Massachusetts Institute of Technology 2010.
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7. IBM IOD 2012 10/24/2014
Where analytics are performed within an organization is not a single
fixed location, but rather a combination of complementary
arrangements.
Location analytics
performed
Centralized
analytic units
LOB analytic
units
At point-of-
need
IT department
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Progression of analytics location as demand grows
87%
Transformed
(21%)
71%
Experienced
(46%)
47%
Aspirational
(33%)
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Frequency of
analytics use
Source: Analytics: The New Path to Value, a joint
MIT Sloan Management Review and IBM
Institute of Business Value study. © 2010
Massachusetts Institute of Technology.
Nearly two out of three respondents reports realizing a
competitive advantage from information and analytics.
Realizing a competitive advantage
63%
58%
37%
2012
2011
2010
70%
increase
Respondents were asked “To what extent does the use of information (including big
data) and analytics create a competitive advantage for your organization in your
industry or market.” Respondent percentages shown are for those who rated the
extent a [4 ] or [5 Significant extent]. The same question has been asked each year.
Total 2010 and 2011 datasets © Massachusetts Institute of Technology respondents n = 1144
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 Competitive advantage
enabler
 A majority of respondents
reported analytics and information
(including big data) creates a
competitive advantage within their
market or industry
 Represents a 70% increase
since 2010
 Organizations already active in
big data activities were 15%
more likely to report a
competitive advantage
 A higher-than-average
percentage of respondents in
Latin America, India/SE Asia and
ANZ reported realizing a
competitive advantage
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8. IBM IOD 2012 10/24/2014
Analytics Leadership and
Governance
IBM’s Analytics Transformation Governance Model
Network of Analytic Communities
Data
Strategy
Shared Services
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Analytics
Communities Enterprise
Analytics
Practices
Information
Management
CIO Office
Global
Integrated
Enterprise
Big Data &
Analytics
University
Business
Performance
Services
Line of Business
Analytic Groups
IBM Research
Addressing business
challenges in LOBs
Enterprise
Transformation
Initiatives
Development
Groups
Infrastructure
Initial
Deployment
Channel
Education Portal
Scaling
Deployment
Product and
Solution offerings
Deep
mathematical
skills
Apply and solve business problems
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9. IBM IOD 2012 10/24/2014
Business Analytics Transformation
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Mission
Drive the Widespread Use of Analytics across IBM to Elevate Business
Performance
Long Term BAT Strategy
Transform for Growth using Analytics via 4 E's:
Evangelize – promote the value gained by using analytics
Educate – provide training in consuming and doing analytics
Enable – assist business leaders in solving their business
challenges with analytics
Empower – establish business structures to support people
implementing analytics
We leverage a rich set of products internally for Business
Analytics
computing The Customer-centricity
revolution
Social Network Analysis
Social Media Analysis
Voice of Customer
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Bring analytics to strategic
decisions
Collaboration Management
Highly scalable & big data
Smart Appliance
Stream Computing
Parallelization
Entity Analytics
Cloud Computing
Deployment to
decision makers
Dashboarding
Reporting
Visualization
Financial Data
Searching
IBM Analytics
Ecosystem
Core Analytics
Technologies
DataStage
Multi-channel deployment & management
Complex Event Processing, Business Process Management
Business Rules & Events (ILOG)
GBS
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10. IBM IOD 2012 10/24/2014
Analytics Case Studies –
Emily Plachy
Human Resources: Tailored analytics-driven
recommendations reduces attrition of high-value
employees
Business problem: Retaining high-value employees.
Solution:
 Identify drivers of attrition and risk level for every
 Leverage the model to create a customized retention
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$85M
Estimated net benefit through
reduced attrition in IBM’s growth
market employee population
325% ROI
For 2012-2013 investment
Solution components:
- IBM® SPSS Modeler
- IBM® Cognos BI
- IBM® ILOG CPLEX
employee.
plan for each high value employee.
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11. IBM IOD 2012 10/24/2014
Finance: Identify and mitigate acquisition risk by
leveraging data and analytics
Business problem: Acquisitions ‘synergies’ are
challenging to quantify and realize and can significantly
alter performance & financial expectations.
Solution: Use acquisition data and advanced analytics
models to create tailored risk profiles for each
contemplated deal and address throughout the
acquisition lifecycle.
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80+ Acquisitions
benefited from headlights into
execution risks to affect both
deal pricing and integration plan
End-to-End Risk
Management across the
acquisition portfolio
Streamlined tracking and
reporting to identify systemic risk
and address challenges
Solution components:
- IBM® SPSS Modeler, Statistics
- IBM® Cognos BI
- IBM®WebSphere
Sales: Boost sales effectiveness by applying advanced
analytics to align resources to the market opportunity.
Business problem: Deploying sellers for maximum revenue growth
by account.
Solution:
• Advanced analytical models predicting customer profit contribution
based on historic revenue growth and opportunity for every
account.
• Recommendation engine provides Increase / Decrease / Maintain
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resource shift recommendations at a client level.
$300M
estimated additional revenue
during 2013 due to sales force
productivity increase
3000% ROI
for 2013, based on a yearly
ongoing investment of $10M
Solution components:
- IBM® SPSS Modeler, Statistics
- IBM® Cognos BI
- IBM® Netezza
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12. IBM IOD 2012 10/24/2014
Services: Develop new business using analytics and social
media.
Business problem: Developing new business in an existing
market, finding new customers, finding new products and
services for existing customers.
Solution: IBM Global Technology Services and IBM
Research developed the Long-Term Signings Platform.
• Analyzed over 30 data sources
• Discovered associations between clients and products
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$M
Millions of dollars in increased
revenue and millions of dollars in
cost savings
Solution components:
- IBM® SPSS Modeler, Text Analytics
Best Practices for Getting
Started
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13. IBM IOD 2012 10/24/2014
Developing and deploying an analytics solution consists of
three elements: data, math/analytics, business process.
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How do we optimize a dynamic, Big
Data environment?
Highly automated optimization
solutions that get smarter over time
What should we do about it?
Prescriptive
Cognitive
Collaborate for maximum business
value, informed by advanced analytics
Big Data = All Data
Veracity (data in doubt)
Variety (many forms of data)
Velocity (data in motion)
Volume (data at rest)
What will happen?
Understand the most likely future
scenario, and its business implications
What happened?
Descriptive
Predictive
Get in touch with reality, a single
source of the truth, visibility
Developing an analytics blueprint is the first step to
converting data and analytic insights into results.
Instill a sense of purpose
 Is there a common agenda for analytics among leaders?
 Are your investments aligned to value delivery?
 Does the funding structure support cross-silo initiatives?
 Are data and analytics skills nurtured within your organization?
 Are data management practices strong enough to instill confidence?
 Is the analytic infrastructure architected for today’s challenges?
 Are data and analytics part of your decision making processes?
 Can your define the impact from analytics investments?
 Is the level of trust sufficient to rely on others for data and analytics?
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Strategy
Technology Architect for the future
Organization Enable the organization to act
Source: Analytics: A blueprint for value – Converting big data and analytics into results, IBM Institute for Business Value © 2013 IBM
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14. IBM IOD 2012 10/24/2014
“New path to value” – a five-point approach to
operationalize analytics
Recommendation 1:
Focus on the biggest
and highest value
opportunities
Recommendation 2:
Within each
opportunity, start with
questions, not data
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Recommendation 5:
Use an information
agenda to plan for
the future
Recommendation 4:
Keep existing
capabilities while
adding new ones
Source: Analytics: The New Path to Value, a joint MIT Sloan
Management Review and IBM Institute of Business Value study.
Copyright © Massachusetts Institute of Technology 2010.
Recommendation 3:
Embed insights to drive
actions and deliver value
Best Practice Approach to Analytics Projects
Think big Start small Deliver fast
• Put a stake in the
ground to progress the
project and get results
• Review and make
improvements
Iterative
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• Perfect data is a long
journey and you can’t
afford to wait
• Organize & cleanse
data incrementally so
that projects can start
Incremental
Collaborative
• To gain business value
start on analytic
projects right away
• Have business
analysts work with data
analysts
Data Analysts Business Acumen
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15. IBM IOD 2012 10/24/2014
Several themes have emerged from our analytics
solutions.
 Relationships inferred from data today may not be present in data
collected tomorrow.
 You don’t have to understand analytics technology to derive value from
it.
 Fast, cheap processors and cheap storage make analysis on big data
possible.
 Doing things fast is almost always better than doing things perfectly.
 Using analytics leads to better auditability and accountability.
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Nine levers: Capabilities that enable and enhance big data
& analytics value creation
Source of Value Measurement Platform
Enable
Basis for big
data and
analytics
Actions and decisions that
generate value
Evaluating impact on
business outcomes
Integrating capabilities
delivered by hardware and
software
Culture Data Trust
Drive
Needed to
realize value
Availability and use of data
and analytics
Data management practices Organizational confidence
Sponsorship Funding Expertise
Amplify
Boosts
value
creation
Executive support and
involvement
Financial rigor in analytics
funding process
Development and access to
skills and capabilities
Source: “Analytics:A blueprint for value – Converting big data and analytics into results,” IBM Institute for Business
Value © 2013 IBM
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16. IBM IOD 2012 10/24/2014
Several dynamics are underway that are shaping the
future for big data and analytics
• Growth of data – 2.5 billion gigabytes generated every
day
• Unstructured data – 80% of big data growth is
unstructured (social media, video, audio, images, data
from sensors).
• Cognitive computing – Just when we need it, the third era
of computing, cognitive, offers the promise of allowing us
to rapidly explore big data and uncover insights.
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‒ Think Ahead
‒ Tell a Story
‒Understand Your Business
‒Get Better Data
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17. IBM IOD 2012 10/24/2014
Conclusions
Conclusions
• The use of analytics correlates to organizational performance.
• Developing and deploying analytics solutions consists of three
elements: Data / Analytics / Business Processes
• You can operationalize analytics using a 5-point approach.
• Successful analytics deployments incrementally cleanse data,
have collaborative teams, and deliver iteratively.
• The most competitive companies will move from raw big data to
insight-driven actions with speed; and cognitive computing will
help do this.
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18. IBM IOD 2012 10/24/2014
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19. IBM IOD 2012 10/24/2014
Thank You
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