Accelerating the Value of Big Data Analytics for P&C Insurers with Hortonworks and Clarity Solution Group

© Hortonworks Inc. 2011 – 2014. All Rights Reserved
© 2015 Clarity Solution Group, LLC
The Modern Data Architecture for the Insurance Industry

Today’s Speakers
Tripp Smith, CTO Clarity Solution Group
Cindy Maike, GM-Insurance Hortonworks

The Insurance Industry Data Equation
…the current situation

Trends require a shift from product to prevention
HDP and Hadoop
allow insurers to shift
interactions from…
Reactive
Post-Transaction
Proactive
Pre-Decision
…to prevention services
customized for needs
From traditional coverage
…to proactive advisorsFrom siloed information
…to 1x1 targeting & engagementFrom “mass-market”
A shift in Customer Engagement
A shift in Products
A shift in Agent/Broker and Call Center Support
…to ‘valid and pay’, anomaly
detection and severity
From “a claim is a claim”
A shift in Claims Management

Consensus of Analysts estimate enterprise data growth of
50x year over year through 2020
Data is growing exponentially at unprecedented rates
0 5 10 15 20 25 30 35 40
2020
2018
2015
2013
The “Digital Universe” expressed in Zettabytes* 85% of growth from
new types of data with
machine-generated data
increasing 15x
*Multiples of Bytes
Kilobyte
Megabyte
Gigabyte
Terabyte
Petabyte
Exabyte
Zettabyte
Yottabyte

Clickstream
Capture and
analyze website
visitors’ data trails
and optimize your
website
Sensors
Discover patterns in
data streaming
automatically from
remote sensors and
machines
Server Logs
Research logs to
diagnose process
failures and prevent
security breaches
New types of data
Sentiment
Understand how
your customers feel
about your brand
and products –
right now
Geographic
Analyze location-
based data to
manage operations
where they occur
Unstructured
Understand patterns
in files across
millions of web
pages, emails, and
documents
Data is growing exponentially – causing IT delays

Enabling a Mature Enterprise Strength Hadoop
Opportunities with Using Hadoop
Information Agility Increased Opportunity
•  Single point of access to high-priority enterprise
assets, transactional assets and Dark Data
•  Centralization of data combined with
decentralization of analytic capabilities
Processing Horsepower Increased Capacity
•  Near-linear hardware capacity scalability
•  Portfolio of components that scale to data or
computational complexity
New or Expanded Analytics Expanded Capability
•  Increased depth of conventional analysis
•  Application of analytics to real-time needs
•  Deep machine learning and discovery analytics
Cost Containment Reduced Expense
•  Cheaper than enterprise SAN or proprietary RDBMS
•  Scalable with inexpensive hardware vs. expensive
optimization or recoding
"Out of the Box" Challenges
Manageability – Risk – TCO Spiral
•  Platform security and repeatable
processes for securing data
•  Common vocabulary and business
data definitions
•  Consistently applied data integration
and transformation processes
•  Transparent data quality and data
lineage
•  Ability to manage complex mixed
workloads and a variety of access
patterns to support disparate user
groups and use cases
•  Time which instead is spent on data
forensics rather than analysis
Operational and Interactive
Platforms

MESH achieves agility with integrity
The Mature Enterprise Strength Hadoop (MESH) framework addresses scaling a Hadoop ecosystem that meets
enterprise needs
Matrix of architecture, governance and enablement
capabilities
Integrated ecosystem addressing the full breadth of
enterprise analytics, users and use cases
Enterprise-strength security and achievable data
governance
Automation and acceleration across implementation,
governance and enablement vectors
Operational roadmap and tool kit to activate business
value through analytic agility
Managed
Raw Materials
Structured
Integration and
Discovery
Governed
End-User
Consumption
Organic,
Process-
Driven
Information
Refinement

3 Dimensions of Success: Architecture, Governance,
and Enablement
Security and Role
Based Access
Controls
Acquisition
and Ingestion
Archival Data
Management
Event
Processing
Data
Transformation
Master Data
Integration
Information
Delivery
Discovery
Analytics
Machine
Learning
Common
Vocabulary and
Data Definitions
Tool Rationalization
Process
Automation
Testing and Quality
Assurance
Resource and
Workload
Management
Processes
Data Quality and
Stewardship

The components beyond the warehouse
Iterative workspace for deep analytics and data discovery
Enablement and alignment
Streamlined service-driven data integration
Incremental enrichment of analytic data service
Governance and operational clarity

Big Data & Hadoop market drivers and opportunities
Business Drivers
•  From reactive
analytics to proactive
customer interaction
•  Insights that drive
competitive
advantage
and optimal returns
Financial Drivers
•  Cost of data systems,
as % of IT spend,
continues to grow
•  Cost advantages of
commodity hardware
and open source
software
$
Technical Drivers
•  Data is growing
exponentially and
existing systems are
overwhelmed
•  Predominantly driven
by NEW types of data
that can inform
analytics

The Modern Data Architecture
…use case examples of Insurers using Hortonworks Data Platform

Customers using HDP to meet Insurance challenges
Top 3 Challenges
Relevant Insurance Use Cases
Change in Customer
Engagement Model
Rising Claims Costs
(frequency and severity)
Data Explosion
(Complexity of Risk/
Underwriting Information)
Personalization / Next Best
Action
720º Degree Customer Visibility
Risk/Underwriting Profile
Analysis
Sensor-based Telematics
(Prevention Services, UBI)
ETL/EDW Optimization
Claim anomaly and fraud
detection

Use Case Analysis

Enhanced Insurance cross-sell
and catching fraudulent claims
Problem: ETL challenges with multiple data streams hampers analysis on ways to improve
customer service
•  Traditional and newer types of data were difficult to combine in the EDW, because of
“schema on write” architecture some data was discarded
•  Company missed data-driven ways to serve customers better
•  Poor data visibility hampered analysis separating legitimate from fraudulent claims
Solution: Data lake to improve up-sell and identify fraud
•  “Schema on read” architecture ingests more data sources for predictive analytics
•  Agents use new insights to provide higher service levels to valued customers
•  Claims analysts and underwriters process streaming data to quickly flag fraud risks and
fast-track legitimate claims
Insurance – Health
Large US medical insurer
IH2
Why Hadoop?
Data Systems
Optimization
Claim Anomalies & IT
Optimization

Improved risk analysis and margins for
usage-based car insurance
Problem: Slow ETL processing hampered speedy underwriting of usage-based car
insurance
•  Usage-based car insurance requires rapid ingest and analysis of sensor data
•  Volume, velocity and variety of incoming data taxed existing systems and the high cost
of storage eroded margins
•  ETL process only captured 25% of the dataset and took 5-7 days to complete
Solution: Faster time-to-insight, improved ETL & predictive analytics
•  Built Azure POC cluster to justify the big data project before launching HDP on site
•  Improved performance and predictive analytics with Apache Hive
•  Faster ETL in Hadoop now processes 100% of the data in three days or less
Insurance –
Property &
Casualty
Personal auto & other
property-casualty
insurance
IP1
Why Hadoop?
Predictive Analytics
Telematics/UBI
New Analytic Applications
Sensor Data and ETL Offload

Improved visibility and accuracy for
P&C Insurance claim analysis
Problem: Analysis of unstructured data did not keep pace with mature systems for
analyzing structured data
•  Large P&C insurance provider had systems for analyzing structured data at scale
•  Unstructured data from claims notes and social media data could add valuable
information to claims analysis, but is was unable to analyze this data at scale
•  Impartial data visibility hampered underwriting and claims, driving up costs, eroding
margins and blocking efforts to reduce fraudulent claims
Solution: Join structured and unstructured data for accuracy in claims processing,
reducing risk, processing costs and fraud
•  “Schema on read” architecture captures more data sources (text and social data)
•  Larger data sets fed to front-end business tools provided by Hortonworks partners: SAS,
Tableau and QlikView
Insurance –
Property &
Casualty
Major provider of
property casualty, life
and mortgage
insurance
IP2
Why Hadoop?
Data Discovery
Claims: New Analytic Applications
Structured, Social &
Unstructured Data

Case Study: 12-month Hadoop evolution at TrueCar
DataPlatformCapabilities
12 months execution plan
June 2013
Begin
Hadoop
Execution
July 2013
Hortonworks
Partnership
May ‘14
IPO
Aug 2013
Training
& Dev
Begins
Nov 2013
Production
Cluster
60 Nodes
2 PB
Jan 2014
40% Dev
Staff
Perficient
Dec 2013
Three
Production
Apps
(3 total)
Feb 2014
Three More
Production
Apps
(6 total)
12 Month Results at TrueCAR
•  Six Production Hadoop Applications
•  Sixty nodes/2PB data
•  Storage Costs/Compute Costs
from $19/GB to $0.23/GB
“We addressed our data platform capabilities
strategically as a pre-cursor to IPO.”
Leverage commodity hardware
for efficient data storage

Optimized marketing impact
with advanced customer analytics
Problem: Difficulty understand marketing effectiveness and customer behavior
•  25M customers and prospects conducting over 10M weekly corporate interactions
•  Lack of visibility into effectiveness of marketing spend and impacts on consumer
behavior
•  Difficulty modelling behavior across disparate data sources from internal enterprise
master data and external vendors
Solution: Advanced analytics and experiment design
•  Closed-loop marketing analytics across key enterprise business units
•  Informed tactical decisions that ensure efficient marketing spend
•  Quantification of marketing effectiveness and audience behavior
•  Strategic insight for enterprise marketing efforts to evolve cross-sell to customers and
drive revenue generation
Insurance
Large US-based financial
services and insurance
company
IH1
Why Clarity?
Advanced
Customer
Engagement
Personalization / Next Best
Action

Consumer 720 – Enterprise data with new data
for personalized customer engagement
720º Degree Customer Visibility
Enterprise Master Data Evolution
•  Companies continue to evolve the single
version of key Customer data for use
throughout the enterprise
Social and Interactive Data Challenges
•  Additional data from Social Media sources
provide additional consumer insight, but
data integration challenges prevent this
insight from turning into action
Limited Options for Enablement
•  There is no solution in the marketplace
today that allows companies to seamlessly
integrate core customer data needed to
manage a business along with the vast
amount of social data that these customers
use to express affinity
Inside the
Enterprise
Outside the
EnterpriseIn-store
Activity
Service
& Support
Data
Enrichment
Online
PurchasesEnterprise Social
360 360
Consumer Engagement
Roadblocks
•  Inability to accurately
identify customers across
the enterprise
•  Key marketing systems
(campaign management,
analytics, CRM, etc.)
unable to leverage holistic
customer data
•  Time and effort wasted with
validating, integrating and
managing consumer data
across the enterprise
ConsumerDataChallenges
Enterprise Customer
Profiles
Internal
Sources
3rd-parties
Consumer720
Enterprise Customer Attributes used for identity
Digital
Interactions
Personalized
Customer
Experience

Insurance business value matrix using HDP
NewTypes
ofAnalytics
New Types of Data
New Types of Data
New Analytics Apps
•  Sentiment
•  Click-stream
•  Sensor
•  Geographic
•  Server Logs
•  Unstructured
Existing Data
Existing
Analytics
RDBM
S
MPP
EDW
•  EDW & ETL data & load
balancing
•  Cost & flexibility
•  Building new skill sets
•  Scale out using
commodity hardware
•  Single-View of Customer
showing full 360-degree
profile and history
•  Clickstream analysis for
Next Best Action with
Customers
•  Analyzing submission and
claims models against
larger historical data sets
HDP
HDP
New Historical View
IT Optimization New Data Influencers
•  Collecting Sensor/Telematics
for Usage Based Insurance
•  Sentiment
•  Enhanced Loss Control /
Prevention Services
•  Needs based coverage vs.
traditional coverage
HDP
New Analytics Applications
•  Text Analytics and Link
Analysis for Claim Anomaly
and Fraud Analysis/Detection
•  Enhance Risk Analysis with
Related Party Network Link
Analysis
•  Enhanced Claim Severity and
Frequency Models using “new”
predictive data
HDP

Evaluation
Biz Value
Awareness
& Interest
Evaluation
Technical
Enterprise
Deployment
Enterprise
Production
Point
Deployment
Point
Production
* Timeline varies by company size. Often smaller or focused online businesses achieve milestones at the shorter end of the range.
1 – 2 months
2-6 months
9-15 months
18-36 months
Start small and grow over time…
1 2 3 4Potential Operational Strategic Data-Driven
Data Lake
Modern
Data
Architecture
Industry
Leadership

Clarity Solution Group and
Hortonworks Background and Focus
…how we can help the Insurance Industry

Elegant solutions for insurance business needs
Clarity helps top insurers tackle:
•  Telematics & UBI for pricing and risk
management
•  360-Degree Customer Views to improve cross-
selling
•  Multi-Channel Optimization to measure
marketing effectiveness and improve
customer experience
•  Distribution Channel Analysis to reduce costs,
improve retention and drive profitability
•  Underwriting Optimization to reduce loss and
improve pricing
•  Product Development to increase customer
satisfaction and target new markets
The largest independent US services firm
exclusively focused on data and analytics

Hadoop for the Enterprise:
Implement a Modern Data Architecture with HDP
Customer Momentum
•  230+ customers (as of Q3 2014)
Hortonworks Data Platform
•  Completely open multi-tenant platform for any app & any data.
•  A centralized architecture of consistent enterprise services for
resource management, security, operations, and governance.
Partner for Customer Success
•  Open source community leadership focus on enterprise needs
•  Unrivaled world class support
•  Founded in 2011
•  Original 24 architects, developers,
operators of Hadoop from Yahoo!
•  600+ Employees
•  800+ Ecosystem Partners

Only HDP delivers a Centralized Architecture for
the modern data needs
HDP is uniquely built around YARN serving as a data operating system that provides multi-tenant Resource
Management, consistent Governance & Security and efficient Operations services across Hadoop applications.
Hortonworks Data Platform
YARN
Data Operating System
•  A centralized architecture of
consistent enterprise services for
resource management, security,
operations, and governance.
•  The versatility to support multiple
applications and diverse workloads
from batch to interactive to real-time,
open source and commercial.
Key Benefits
•  Multiple applications on a shared data set
with consistent levels of service: a
multitenant data platform.
•  Provides a shared platform to enable new
analytic applications.
•  Delivers maximum cost efficiency for
cluster resource management. Fewer
servers fewer nodes.
Storage
YARN: Data Operating System
Governance Security
Operations
Resource Management
Existing
Applications
New
Analytics
Partner
Applications
Data Access: Batch, Interactive & Real-time

Customer partnerships matter Driving our innovation through
Apache Software Foundation Projects
Apache Project Committers
PMC
Members
Hadoop 27 21
Pig 5 5
Hive 18 6
Tez 16 15
HBase 6 4
Phoenix 4 4
Accumulo 2 2
Storm 3 2
Slider 11 11
Falcon 5 3
Flume 1 1
Sqoop 1 1
Ambari 34 27
Oozie 3 2
Zookeeper 2 1
Knox 13 3
Ranger 10 n/a
TOTAL 161 108
Source: Apache Software Foundation. As of 11/7/2014.
Hortonworkers are the architects and
engineers that lead development of
open source Apache Hadoop at the ASF
•  Expertise
Uniquely capable to solve the most complex
issues & ensure success with latest features
•  Connection
Provide customers & partners direct input into
the community roadmap
•  Partnership
We partner with customers with subscription
offering. Our success is predicated on yours.
27
Cloudera: 11
Facebook: 5
LinkedIn: 2
IBM: 2
Others: 23
Yahoo
10

Q&A: Please use the Q/A panel to ask your questions!
More Information:
Clarity Solution Group - clarity-us.com
Hortonworks - hortonworks.com
Industry email Updates
A Modern Data Architecture Whitepaper
The Rise of the Data First Enterprise
Speaker Contact Information:
Tripp Smith, CTO Clarity Solution Group: tsmith@clarity-us.com
Cindy Maike, GM-Insurance Hortonworks: cmaike@hortonworks.com

Our Missions:
Clarity Solution Group: We help businesses decrease
time to market and reduce costs by providing data
and analytics solutions to discover hidden trends and
find value in the data they already have.
Hortonworks: To enable Apache Hadoop to be the
enterprise data platform that powers the modern
data architecture and process half the worlds data

Accelerating the Value of Big Data Analytics for P&C Insurers with Hortonworks and Clarity Solution Group

Empfohlen

Empfohlen

Weitere ähnliche Inhalte

Was ist angesagt?

Was ist angesagt? (20)

Andere mochten auch

Andere mochten auch (20)

Ähnlich wie Accelerating the Value of Big Data Analytics for P&C Insurers with Hortonworks and Clarity Solution Group

Ähnlich wie Accelerating the Value of Big Data Analytics for P&C Insurers with Hortonworks and Clarity Solution Group (20)

Mehr von Hortonworks

Mehr von Hortonworks (20)

Kürzlich hochgeladen

Kürzlich hochgeladen (20)

Accelerating the Value of Big Data Analytics for P&C Insurers with Hortonworks and Clarity Solution Group