idma 2015 riding the tsunami of big data final-rev
TRANSCRIPT
Riding the Tsunami of Big Data & IoT 2015 IDMA Annual Conference
April 28, 2015, Philadelphia
Pat Saporito, SAP
Tom Barger, Zurich NA
© 2011 SAP AG. All rights reserved. 2
Agenda
Trends & Opportunities
Challenges
Best Practices
Claims Analytics Case Study
Data Management Role
© 2011 SAP AG. All rights reserved. 3
Big Data & IoT are disrupting all industries
1 billion Facebook
users
4 billion YouTube views
per day
Data
doubles Every 18
months
15
billion Web-enabled
devices
5 billion Emerging
middle class
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Tsunami Waves Leading to Data Chaos!
Big Data
Internet of Things
Self Service
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Big Data & Internet of Things Great Promise, Rarely Delivered
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Organizations need to mature analytics to attain value
Raw
Data
Cleaned
Data
Standard
Reports
Ad Hoc
Reports &
OLAP
Agile
Visualization
Predictive
Modeling
Optimization
What happened?
Why did it happen?
What will happen?
What is
the best that
could happen?
Use
r E
ng
ag
em
en
t
Maturity of Analytics Capabilities
Self Service BI
Generic
Predictive Analysis
Critical Success Factors:
End-to-end analytics
Easy adoption
Quick implementation
Business focused
Enable storytelling
Co
lle
cti
ve
In
sig
ht
© 2011 SAP AG. All rights reserved. 7
Nucleus Research, Gartner, Fortune Magazine
Dark Data The Missing Value
10%
75%
Use Analytics
Today
Need
Analytics
by 2020
Ability to manage
and consume all data
is getting harder
Not utilizing
all the
information
out there
Bottom Line: Not leveraging the
power of collective insight
Missing new
insights
IT is not agile enough
and the business
wants to get involved
=
Expect great demand from the “data democracy”, not just “data elite”
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Insurers’ Use of Data
Source: Novarica Big Data & Analytics Research, 2012
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Turning new signals into business value
:-) Brand Sentiment
360O Customer View
Product
Recommendation
Propensity to Churn Real-time Demand/
Supply Forecast
Predictive Maintenance
Fraud Detection
Network Optimization
Insider Threats
Risk Mitigation,
Real-time
Asset Tracking Personalized Care
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Potential Insurance Applications for IoT (Internet of
Things)
10
Smart Healthcare
Wellness & Disease
Mgmt.
Smart Equipment
Preventative
Maintenance
Smart Trucks
Fleet Mgmt
Smart Houses & Buildings
Home & Property Ins
Smart
Vending
Design Your
Own
Insurance
Connected Cars
Usage Based Insurance
Detect and analyze
data trends by
aggregating sensor
data
Benefit from more
real time risk data
enabling tailored
products, sales,
underwriting,
pricing and loss
prevention
Increase quality of
life through
intelligent vehicles,
buildings,
healthcare
© 2011 SAP AG. All rights reserved. 11
Internet of Things (IoT) / Machine to Machine (M2M)
Trends
Source: GSMA (Groupe Speciale Mobile Association)
www.gsma.com
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Infuse insights into Processes, People & Things
Customers Partners
Employees Processes
Big Data Applications &
Analytics
Agile
Visualization
Advanced
Analytics
Enterprise
Business Intelligence
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Impact of Big Data
New Risks e.g., Cyber risk
New Users, e.g., emerging middle class, demand for micro-
insurance
Good vs. Bad ways to look at data and analyze it
(best practice vs. mere enthusiasm)
Integrating New Data & Data Types
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Analytics Challenges
Staffing
and
Skills
Data
Governance
Cost vs.
Value
Culture &
Change
Management
Unsure of the
Technology
Requirements
Connect people to
information in the
moment they need it
and in the right
experience
Analytics
Governance
© 2011 SAP AG. All rights reserved. 15
Analytic Value Chain & Users Many needs for data managers to address
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Data Democratization Challenges
Analytic Skills
Enabling & Integrating New Analytic Users
Business Engagement in the Full Analytic Process
Underwriting vs. Claims Considerations
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Insurance Analytics Evolution
Where are you today? Where do you want to be?
Pricing & Underwriting
Traditional Class Rated
Portfolio Analysis Household Analysis, Tier Rating Plans
Risk Based Pricing, Ad-hoc or On Demand Rate Reviews
Data Poor Quality, Silo’d, Inaccessible Data
Data Assembled Across Product Lines/Historical
Consistent Enterprise View Knowledge/ Data Mining
Atomic Detail Data Wisdom/ Predictive
Product Development
One Product Fits All
Unbundled Coverages Cafeteria/ Menu Approach
Customer & Profitability Driven
Marketing
Product Value Customer Segment Value
Customer Lifetime Value
Dynamic Value Management
Accounting & Finance
Unit focused claims mgmt.
Integrated, but reactive claims mgmt.
Driver based historical claims mgmt.
Driver based predictive claims mgmt.
Metrics Silo’d, Functional, Lagging Metrics
SBU-Strategic Objective linked, historical drivers
Strategic & Cross-SBU objective linked, predictive drivers
Integrated predictive models & metrics
Claims
Traditional Planning & Budgeting
Driver Based Planning & Budgeting
Integrated Planning Predictive Planning
Less Advanced More Advanced
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Optimize Value with Integrated Analytics
Integrate and apply across all business
processes.
Business rules, data
and KPIs should be
leveraged across
business
Example: the
business rules used
in 1st party claim
fraud detection can
also be applied
upfront during the
underwriting process
7
© 2011 SAP AG. All rights reserved. 19
Objectives Business
Needs
Business
Benefit Technology Organization
Background and
Purpose
Current State and
History
BI Objectives and
Scope
Summary of BI
needs
Envisioned To-Be
State
Priorities and
Alignment
Value Proposition
of BI
Expected Benefits
– Future State KPI
Business Case
Information
Categories
Architecture and
Standards
BI Applications
Governance
Structure
Program
Management
Roadmap and
Milestones
Measurement
Education /
Training
Support
• BI Strategy & BI Competency Center Best Practices
• Data Management should have a role in the BICC
SAP BI Strategy Framework Data Management is a key part of a BI Strategy
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Workers’ Compensation Predictive Modeling Case Study
Claims Predictive Modeling: Zurich’s Approach
Focused strategy and resources to develop the best predictive tool
Generalized linear modeling (Multivariate) techniques
– Calculates the interaction of numerous variables driving the claim
Data from multiple sources
– Claims
– over a million records
– several years of developed data analyzed
– text mining
– customer information
– external data sources
Embedded into the workflow process
21
22
Claims Predictive Modeling: Business Goals
Predict claim severity at FNOL
Earlier identification of severity enhances opportunities to help:
– Match the right claim to the right File Handler
– Proactively manage the claim
– Mitigate risk
– Facilitate Nurse Case Management services
– Reduce total cost of risk for customers
– eliminates over-resourcing simple claims
– reduces costly, late claim transfers
Drive value for customers, improving service
– More precise, timely, and better focused interventions
– Better insights about loss costs and key drivers
– Facilitate customers managing their risk
© 2011 SAP AG. All rights reserved. 23
Data Management Role in Big Data and IoT
Ride the Big Data and Self Service Wave
Have a seat at the table in your BI / Analytics Governance Structure
Be part of a BI Competency Center
Get support from your Analytics Executive Business Sponsor
Push for the right data management tools (business glossary) and
processes
Sell data management based on value; tie to business cases, ideally
revenue ones
Use fear when logic doesn’t work
© 2011 SAP AG. All rights reserved. 24
Additional Information Governance Resources
Information Governance Framework Blog
Information Governance Self Assessment Tool
Information Governance Cost Calculator
Data Impact on Analytic Cost Calculator
Thank You
Tom Barger
VP, Zurich Claims CRM
Zurich NA
+1 (215) 605-0684
Pat Saporito
Global CoE for Analytics
SAP
+1 (201) 681-9671