The document discusses the challenges faced by Reliance Life Insurance in leveraging data for analytics, highlighting issues such as data insufficiency, lack of integration, and poor data quality that hinder effective decision-making and marketing strategies. It emphasizes the need for a structured approach to enterprise implementation, focusing on enhancing data management and analytics capabilities. Key takeaways include the importance of data quality, the establishment of a strong operational data store, and the need for a techno-functional project management approach to drive success.

1. THE ROAD TO AN ANALYTICAL ENTERPRISE
Srinivasan Iyengar, COO – Reliance Life Insurance
Confidential
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2. Dynamics today…..
Connect the Dots ??? Disparate Sources of Data
Low campaign results resulting in
high acquisition costs v/s low
acquisitions
Build the Blocks ??? Data Insufficiency
Nothing, But the Truth ??? No Single Version of Truth
Inadequate Information for
Analytically Inferred
Strategy
Non-Integrated
Analytics resulting in
Inferential Decisioning
Inaccurate & Inconsistent
Information Dissemination
Where is Eminence ??? Poor Quality of Data
WHO is my Customer ??? No 360 degree View
Inability to deploy segmentbased campaigns and
customized messaging
High Marketing &
Distribution Management
Spends
Spend less and bag more ??? Non-automation of key process
Confidential
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3. Dynamics today…..
Connect the Dots ??? Disparate Sources of Data
Low campaign results resulting in
high acquisition costs v/s low
acquisitions
Non-Integrated
Analytics resulting in
Inferential Decisioning
Build the Blocks ??? Data Insufficiency
Advent of Big Data…
Nothing, But the Truth ??? No Single Version of Truth
Inadequate Information for
Analytically Inferred
Strategy
Inaccurate & Inconsistent
Information Dissemination
Where is Eminence ??? Poor Quality of Data
WHO is my Customer ??? No 360 degree View
Inability to deploy segmentbased campaigns and
customized messaging
High Marketing &
Distribution Management
Spends
Spend less and bag more ??? Non-automation of key process
Confidential
Slide

4. Approach to Enterprise Implementation
Strategize
Monitor
Confidential
Design
Deploy
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5. Approach to Enterprise Implementation
Identification of
Analytical Areas
Sales
Transformation
BIU - Business
Technology
Integration
On Demand Reports
Propensity Modeling
Strategize
Design
Monitor
Customer
Profitability
Deploy
Knowledge
Management
Management
Dashboards
Enterprise
Information
Visualization
Phased
Deployment
Enterprise Participation
Intelligence & Ad-Hoc Analysis
Confidential
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6. Approach to Enterprise Implementation
What To Do???
Sales
Transformation
Recruitment Profiling
Product Time To Market
On Demand Reports
Acquisition v/s Win-back
Product Mix Gaps
Propensity Modeling
Competitor Product Launch Impact
Strategize
Customer Life-Time Value
Customer
Channel Analytics
Profitability
Product Profitability Modeling
Does it
Management Address the Needs
Dashboards
Board
Monitor
Enterprise
Information Management
Visualization
Employees
Intelligence & Ad-Hoc Analysis
Customers
Confidential
First ThingsIdentification of
First…
Analytical Areas
Integration or Implementation
BIU - Business
Quick Wins Technology
Integration
Let
Design the Others Not Stay Far ☺
Knowledge
Design to Factor nextManagement
stages also
Execution
Deploy Management Commitment
Phased
Deployment
Enterprise Programs
Enterprise Participation
Cultural Transformation
Training / Awareness
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7. Illustrative End State Model
Confidential
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8. Insurance Industry ......Scenario
Competition
Regulatory Challenges
Confidential
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9. The Results ………It’s a Journey
New Business Revenue
Persistency
Customer Retention
Renewal Collection
Advisor Activization
Reduction in Sales Attrition
Confidential
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10. Some Takeaways......
Amidst all these don’t loose focus on data quality – its
key & iterative
Strong robust ODS (or EDW) is the key foundation layer
Build/maintain computed value elements in ODS layer
and not in presentation layer
It’s a Myth that EDW is a pre-requisite for good analytics
results
Critical to have techno-functional PM to achieve success
to these projects
Confidential
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11. Thank you
Confidential
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