Richard Lee is an executive consultant who advises companies on predictive analytics and becoming predictive enterprises. Predictive analytics uses modeling, machine learning and data mining to analyze past and present data to predict future events. It has evolved from descriptive analytics in the past to now aiming to embed predictive analysis into real-time applications. Becoming a predictive enterprise requires using all data sources and predictive models across the organization to gain insights.

1. Predictive Analytics: From Back
Office to Front Office
Richard R. Lee, Executive Consultant
The Speyside Group

2. Professional Profile
 Richard Lee – “Executive Consultant in Business
Informatics & Advanced Analytics”
 I Guide & Advise Senior Executive Teams in their
pursuit of “The Predictive Enterprise”
 Worked in many “Information-driven Verticals” over
long consulting career;
• Banking & Insurance
• Utilities
• Consumer Goods & Retail
• Telecoms
• Government
• Technology
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3. What is Predictive Analytics??
pre·dic·tive Adjective
• “Relating to or having the effect of predicting an event or
result”
a·nal·y·sis Noun
• “Detailed examination of the elements or structure of
something, typically as a basis for discussion or interpretation.”
Predictive Analytics:
“Modeling, machine learning, and data mining that
analyze current and historical facts to make predictions
about future, or otherwise unknown, events.”

4. The Evolution of Analytics
10’s
Embedded
00’s
Analysis
Predictive
90’s Analysis
Descriptive
Analysis
80’s
Statistical
Analysis
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5. Predictive Analytics & Big Data(1)
 Traditionally, Predictive Analysis was focused on
well-defined data sets
• Customer Churn & Up Sell/Cross Sell
• Risk & Fraud
• Actuarial (Claims & Underwriting)
 Finely Tuned Models were employed
• Extensive A/B & Champion Challenger
Testing/Development
• Maintained by PhD Statisticians, Actuaries and Decision
Scientists
 Analysis was time consuming and always “after the
fact”
• Months to Years of Development & Optimization
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6. Predictive Analytics & Big Data(2)
 Creating a “Real-time Predictive Enterprise” is the
goal of every “Analytics Centric” organization today.
• Forward Looking pervasive use of all Information aka Big
Data
• Embedded Real Time Analysis of Customer Behavior,
Opportunities & Risks and Sources of Competitive
Advantage
• Leveraging deep knowledge of the Past and the Present to
“Predict” with high accuracy “what will happen next” and to
exploit this knowledge in real time to;
– Delight the Customer & Win his/her Loyalty
– To Mitigate Risks while Maximizing Opportunities
– To Clearly Differentiate your Services & Products from the
Competition
– To Maximize your Operational Efficiencies and Productivity.
6 © 2011 Ziff Davis, Inc. - All Rights Reserved. PCMag.com • ExtremeTech • Geek.com • LogicBUY • BuyerBase

7. Predictive Analytics & Big Data(3)
 Critical to the Success of the “Predictive Enterprise”
is the pervasive use of all Information Resources
across your Enterprise by an Analytics-driven
Executive Team and Workforce”
• Big Data & the deep historical data repositories working in a
“collective fashion” to present a 360 view.
• Predictive Analytics engines embedded in Enterprise
Applications
• Analytics Tools are available for everyone to exploit from
anywhere.
• Deep competencies in Analytics to support Front Line
Users and Analysts.
 Creating and Driving “A Culture of Analytics” across
the Enterprise should be the CEO’s #1 Priority &
Responsibility.
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8. Industry Segments: Banking & Insurance
 Banking & Insurance are the most mature segments
in terms of using Predictive Analytics.
• Customer Insights & Opportunities
• Fraud & Risk Management
• Quantitative Finance
• Pricing and Claims
 Case Study in Detail – North American P&C Insurer
• Transformational Approach
• Move from “Backwards looking” to “Predictive view” of all
aspects of the business.
• Goal was “Near Real Time, Holistic Management”
• Predictive Analysis embedded in all business functions.
8 © 2011 Ziff Davis, Inc. - All Rights Reserved. PCMag.com • ExtremeTech • Geek.com • LogicBUY • BuyerBase

9. Predictive Insurance
Broker
Strategic
P&C Goals
Moments of Insurer
The
Customer Truth Analytically-Optimized Improve
Business Processes Customer
I buy/renew Perception
Customer Insights Competitive Pricing
I license
Improve
Predictive Claims Financial Oversight Employee
I claim
Experience
Underwriting Licensing
I amend
Maintain
ERM/Solvency II
I cancel Financial
Security
Analytics- Driven Foresight
Retention
Customer Attractive and Fraud Best Offers Customer Employee Risk
LTV Products Upsell/Cro Mitigation (Claims) Experience Engagement Assessment
ss Sell
Business Intelligence Advanced Analytics Performance & Risk Mgmt.
COE
Enterprise Information Subject Areas & Analytical Models
Information Delivery
Information Foundation Information Integration
Information Sources (Structured & Unstructured)
9

10. Predictive Claims Processing
Predictive Analysis: Create:
• Customer Behaviours, Linkages, etc. • Intelligently scripted/generated questions
• Current claims activities • Empower Knowledge Workers
• Claims history • Drive Operational Excellence
• External data (Partners, MD, BI, Gov. 3rd •Delight the Customer
parties, etc.) Service
Fast
Claims Application Track
Environment
First
Request
Notification
Predictive Additional
of Loss
Claims Information
(FNOL) Platform
(Embedded)
SIU
Information Infrastructure
Suspect
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11. Other Industry Verticals:
 Predictive Analysis has/is becoming pervasive in virtually
all Industry Segments;
• Fast Moving Consumer Goods & Retail
• Healthcare
• Government
• Energy & Utilities
• Transportation
• Telecommunications
• Entertainment
• Publishing
• Etc., etc.
 Leaders within each of these Verticals have created
unique Sources of Competitive Advantage for their
organization as they have become “Predictive
Enterprises”.
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12. The Democratization of Analytics
 How do we “move Predictive Analysis from the Back
Office to Your Office?
• For far too long PA has been the domain of the so-called “data
geeks” and “Bayesians”.
– We need to make PA more pervasive by moving it to everyone’s
“Work Platform” e.g. Desktop, Laptop, Tablet, Smart Device, etc.
– We must bootstrap everyone’s skills and knowledge to be
“Analytics Literate”
• We must “unleash the power of all of our sources of Information”
to create unique and actionable insights for everyone to leverage.
– We must move away from “batch driven & IT-lead “ to a “real-time
& Business-lead” Operational Model for delivering Information-
based, Analytics-driven Outcomes.
– We must build Governance into all Analytics endeavors as we
have done with our Information-related ones, using a holistic
approach.
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13. Looking Forward
 The distant future as seen from 10-years ago has
become manifest already;
• Smart Devices and Embedded Applications have accelerated the
“inflection point” for creating massive streams of Information (aka
Big Data) and the Pervasive use of Analytics to drive decision
making.
 Predictive Analysis will be embedded in all Enterprise
Applications and Services going forward.
• Decision Making at the Point of Contact is paramount.
• An integrated view of the Customer is essential to “Delighting him
or her”
• Windows of Opportunity are shrinking to zero.
 Creating a “Culture of Analysis” along with Delivering
“Analytics as a Service” to Everyone is the Critical Path
• A, integrated, long-range Analytics Strategy is the key to driving a
successful outcome to this Top Down Vision.
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14. Summary and Hand-Off
 Predictive Analytics is a mature discipline, but
currently confined to an “elite community of analysts”
in most, if not all Enterprises.
 The so-called “Democratization of Analytics” will
remove these barriers to broad Enterprise Adoption.
 A long-range, business driven Analytics Strategy
(aligned with the Organization’s overall strategy) is
required to insure a successful “Transformation into
a Predictive Enterprise”
• People, Process, Technology & Culture
 A critical first step in this transformational journey is
“To Move PA from the Back Office to Your Office”
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