ENTERPRISE
DATA STRATEGY
CAS Ratemaking Seminar
March 2004
1
Agenda
 Introductions
 Data
as a Corporate Asset
 Defining an Enterprise Data Strategy
–
–
–
–
A Standards Organization Perspective
An Insurer Perspective
An Actuarial Perspective
An Industry Organization Perspective
 Conclusions
and Questions
2
Panelists
Pete Marotta, Principal Data Management
Consulting, ISO
 Kim McMillon, Program Manager, ACORD
 Gary Knoble, Vice President, The Hartford
 Nathan Root, Assistant Vice President, CNA

3
Data as a Corporate Asset
4
Data - A Corporate Asset
Data, like all corporate assets, requires
managing to ensure the maximum benefit is
achieved by the organization
 Well-managed, high-quality data aids good
corporate governance by providing
management with a cohesive and objective view
of an organization’s activity and promotes data
transparency
 Poorly-managed can result in faulty business
decisions

5
Data and the Strategic Planning
Process
Data supports corporate decision-making –
 In providing a cohesive and objective view of
corporate activities
 In viewing the external landscape
 In predicting the future
 In developing the corporate strategic plan
 In identifying process improvements and other
efficiencies
 In measuring results
6
PWC Study
“Data is the currency of the new economy.” PWC
“Companies that manage their data as a strategic
resource and invest in its quality are already
pulling ahead in terms of reputation and
profitability from those that fail to do so.”
Global Data Management Survey 2001,
PriceWaterhouseCoopers
7
PWC Study
“…over the past two years, nearly seven out of ten
companies have become increasingly reliant on
electronic data to make company decisions and
implement processes. Yet the survey points to
dangerous levels of complacency regarding data
management issues within these organizations.”
“Three quarters of companies surveyed had expressed
significant problems as a result of faulty data.”
8
PWC Study Findings
1/3 of business fail to bill or collect receivables as
a result of poor data management
 4 out of 10 businesses have a documented, board
approved data strategy
 Where data strategies exist, they tend to consist of
a series of polices on areas such as privacy and
security, rather than addressing true strategic
issues, such as the value of data

9
Defining an Enterprise Data
Strategy
10
Enterprise Data Strategy
“Not having a data strategy is analogous to
a company allowing each department and
each person within each department to
develop their own charts of accounts.”
Data Strategy Initiatives by Sid Adelman,
Data Management Review 11/2001
11
Enterprise Data Strategy: A
Definition
A plan that establishes a long-term
direction for effectively using data
resources in support of and indivisible
from of an organization's goals and
objectives
12
Enterprise Data Strategy: A
Definition
In addition to supporting corporate
business goals, an Enterprise data strategy
facilitates IT planning by promoting and
maintaining clearly and consistently
defined data across the corporation
13
Enterprise Data Strategy
“An enterprise data strategy is a plan for
improving the way an enterprise leverages its
data, allowing the company to turn data into
information and knowledge which, in turn,
produces measurable improvements in business
performance.”
Information for Innovation: Developing an Enterprise
Data Strategy, by Nancy Muller, Data Management
Review 10/2001
14
Enterprise Data Strategy and IT
Architecture Supports Business
Strategy
A set of guiding
principles that
define why and
what we do
Data
Application
Infrastructure
Business Strategy
A set of guiding
principles that
define how we
do what we do
IT Architecture
15
Enterprise Data Strategy: A
Standards Organization
Perspective
16
Who Should be Involved with Strategic
Data Planning?
The data users, data definers and data enablers, including
 Business units
 Information Technology
 Finance and Accounting
 Actuaries
 Claims
 Government Affairs
 Sales and Marketing
 Research
 Data Management
17
Industry Resources
 Professional Associations:
IDMA, CAS,
etc.
 Trade Associations: RIMS, AIA, PAAS
 Technology Leaders: The Data
Warehouse Institute, Gartner, Celent, etc.
 Vendors & Consultants
 Industry Organizations: ACORD, ISO,
NCCI, etc.
18
The following may be standardized by the
industry through the ACORD Process








Paper or electronic forms (presentation)
Spreadsheet
Data element naming conventions
Data definitions
Codelists
Processes
Data relationships (is a coverage related to policy, location (state,
etc), unit at risk
Format for representation
– xml
– AL3


Implementation Guides
Not through the ACORD process
– Enveloping structure, wrappers (security, authentication, etc.)
19
Standards in the Insurance Process
Insurance cycle
Reinsurance cycle
Client
Reinsurer
Insurer
Intermediary
Insurance cycle
•e-business initiatives between
Intermediaries & carriers support
ACORD standards
Cedent
Reins. Broker
Reinsurance Cycle
•Reinsurance standards - international
•No gateway between insurance and
ceded systems
•With ACORD STP becomes possible
Quotes, contracts, premiums, claims, payment information
20
How ACORD Can Help




Central repository for industry:
– Data dictionary
– Data Models
Antitrust Protection
– Sponsoring standards development across industry
competitors
Networking
– Tackling industry implementation issues
– Identifying and meeting with key trading partners
– Evangelizing best practices
Managing relationships with other standards
organizations to achieve interoperability (accounting,
finance, human resources, collision repair)
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Implementation Success
 Standards
facilitate:
– Internal system integration
– Conversions
– Extending the life of legacy systems
– Streamlines business process flows
Policy
issuance to billing to claims
servicing…
22
Enterprise Data Planning: An
Insurer Perspective
23
Enterprise Data Management Practice
Mission:
Enable business generate value to its customers,
partners and shareholders through a holistic, realistic
and accurate view of enterprise information.
Vision:
A true practice that presents a cohesive set of
processes for enabling project teams to construct
enterprise class business applications, services the
information needs of the business and seamlessly
integrates into the overall P&C enterprise vision.
24
Enterprise Data Goals
Facilitate
alignment and traceability
of significant IT investments to their
respective business drivers
Provide a process and a set of tools
to facilitate Business and IT planning
and decision-making
Maintain a common and consistent
view of data that is shared company
wide
25
Participants

Actuarial
– Most likely sponsor
– Actuarial Standards No. 23 – Data Quality
– Custodians of information

Business Units
– Link data strategy to business strategy

Information Management
– Maintain tools
– Insure delivery of data

Data Management
–
–
–
–
Data quality
Data definitions
Data coordination
Compliance
26
Components
2
1. Organization: develop a body
suitable for supporting the mission
2. Process: using identified assets in
a meaningful and reusable way
EDMP
1
TECHNOLOGY
3. Technology: analyzing the needs
of the Organization and Process to
build a supporting technical
infrastructure
3
27
Target Reference Model
Enterprise Data Warehouse
Business Intelligence
Business Portal
Information
Distribution
Information
Products
Warehouse
Products
Data
Manufacturing
Extract – Transformation – Load
Information and Data Manufacturing
ETL
Source Data
Internal
Data
Systems of
Record
External
Data
Data Sourcing
Platform Infrastructure
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Initiatives: Source
 Common
Data Standards (ACORD
XML)
 Quality Standards
 Quality controls
 “Source of Record”
 Stewardship
 Meta Data Repository
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Initiatives: Manufacturing
 Information
Dictionary
 Data Warehouses
 Data Models
 Business Models
 Platform Migration
 Consolidation of Operating Systems
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Initiatives: Distribution
 Data
Marts
 Vendor Contacts
 Shared Licenses for data access software
 Knowledge Management
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Business Intelligence Ladder
Predictive Modeling
<GK
to add>
Tool Sophistication & Expense
Forecast Analysis
User Count
Advanced
Analytics
Trend Analysis
Analytics
Dimensional Data Analysis
Adhoc Reporting
Parameterized Query
Reporting
Static Reporting
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Enterprise Data Planning: An
Actuarial Perspective
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“There is no royal road to geometry”
-Euclid 300 B.C.
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What Do We Want?
 High
Quality Data
 Metrics and Coding Structure Which
Directly Support Business Strategy
 Standardized Definitions
 Broad Access to Information
35
Information Flow
Data
Sources
Data Warehouse
Reports/
Info
Decision
Makers
Policy
Claim
Billing
Data in
Data
Model
Metrics
from
Data
External
36
Why Actuaries?
 Value
of Good Data/Cost of Bad Data
 Insurance Expertise
 Technical Expertise
 Leadership and Communication Skills
 ASOP 23
37
Obstacles in Standardization
 Inertia
 Active
Resistance to Change
 Highly Complex Coding Systems
 Interdependent IT and Business Apps
 Varying Levels of Awareness of Multiple
Definitions
38
Keys to Standardization
 High
Level Management Support
 Clearly Defined Benefits
 Right People with Right Skills
 Experience with Current Coding
Structure
 Strong Communication Skills
 Enforcement
39
Key Lessons in Driving Change
 Don’t
take a ‘No’ from someone who
can’t give you a ‘Yes’
 Enter Data Once and Only Once
 Standardize, Standardize, Standardize
 The Right People Make the Difference
 Frame the Problem Before You Solve It.
40
Enterprise Data Planning: An
Industry Organization
Perspective
41
Objectives
Enable the re-use of data across the enterprise
to derive maximum value by creating new data
analytics, and decision support offerings
 Enable the enterprise and its trading partners
to easily exchange new and existing data with
minimal overlap to sustain and increase
enterprise value
 Enable the enterprise to protect its data assets
to ensure quality and our position as a trusted
intermediary

42
Solution Sets
 Data
Dictionary and Data Lab
 Data Leverage
 Data Acquisition
 Data Quality
 Data Administration
43
Data Dictionary and Data Lab
A knowledge management tool to cut through
data access issues
 A repository for:
– Standards, procedures, guidelines, business
rules, metadata
– Internal and external data elements
– Record layout, # records, data field
descriptions, usage limitations, data
elements/codes, database abstract
– Links to source documents to data feeds and
data stores
 Data Lab
 Business Intelligence

44
Data Leverage

Ability to merge different data sources to
increase their current value

3rd party matching referential linking

Linkage of current databases to create new
products

A holistic view of data

It is data integration
45
Data Acquisition: Components
 Extract, Transform
and Load (ETL)
 Enterprise Receipt and Acceptance
 New Data and Feeds
 Connect with 3rd Party Vendors (Policy
Mgt, Claims)
 Better Input to Business Cases and
Acquisitions
46
Data Quality
Data quality, management and guidelines
 Data accuracy, validity, completeness …
 Quality standard and actual quality by
application
 Document data quality parameters and criteria
at application level
 Documented measures of data quality
 Expand utility beyond current use
 “Enterprise" criteria for use Cross SBU quality
assurance

47
Data Administration
 The
“IO”s – EIO and SIO
 Managing
the processes related to data
 The
administration of the process put in
place for the other solution sets
 Standards
 Administering
changes
& coordinating data
48
CONCLUSIONS &
QUESTIONS
Addenda: References and IDMA
Value Statements – Actuaries
49
References, Resources & Studies


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

Celent “ACORD XML Standards in US Insurance”:
www.celent.com or www.acord.org
IDMA: www.idma.org
ACORD: www.acord.org
PWC “Global Data Management Survey 2001”:
www.pwcglobal.com
Gartner Research: www4.gartner.com
TDWI “Data Quality and the Bottom Line”: www.dwinstitute.com
CIO Magazine: “Wash Me: Dirty Data …” 2-15-01
edition, www.cio.com
Data Management Review: www.dmreview.com
50
Data Management Value Proposition Value to
Actuaries
Value: Data Quality
Good data management improves data:
 Validity—Are data represented by acceptable values?
 Accuracy—Does the data describe the true underlying situation?
 Reasonability—Does the data make sense? How does it compare
with similar data from a prior period?
 Completeness—Do you have all the data you need?
 Timeliness—Are the data current?
allowing the actuary to have more confidence in, and a better
understanding of, the data being used. This assists the actuary in
his/her professional responsibilities to certify data quality (e.g.,
Actuarial Standard 23 on Data Quality)
51
Data Management Value Proposition Value to
Actuaries





Value: Better Decisions
Better decisions result from better data.
Better priced risks—rates, increased limits, etc.—means improved
bottom line, greater customer satisfaction, improved customer
retention, increase in number of customers
Improved ability to explain, defend (and testify as necessary)
decisions with better data behind the decision, documented
controlled data management processes in place helps to prove the
value of data being used
Improved data integrity, data utility
As data is and can be sliced ever more finely, attention to quality,
privacy and confidentiality is critical. Data management skills can
ensure that.
52
Data Management Value Proposition Value to
Actuaries
•
•
•
Value: Better Decisions (continued)
The actuary’s time is freed up for more focus on core professional
responsibilities, decisions and analysis when data quality is
assured under the guidance of the data manager. Putting data
management under the responsibility of a data management
professional allows both disciplines to do what they do best and
are best trained to do.
In many cases, skilled data managers can assume handle
functions such as responding to special calls.
Predictive modeling is improved when better data are available,
allowing for better existing products and better new product
development.
53
Data Management Value Proposition Value to
Actuaries




Value: Internal Data Coordination
Reducing the cost and time associated with of data collection,
storage, and dispersal, making data available more quickly
Promoting the interoperability of data and databases, allowing for
better data integration thereby giving the actuary more options for
how data can be used
Managing data content and definition across the organization
Advocating industry and enterprise data standards which ensure
consistent definitions and values for enterprise data elements
 Ensuring the quality of the enterprise data, enterprise
communication among the various data sources
54
Data Management Value Proposition Value to
Actuaries






Value: Compliance
Protects the privacy and confidentiality of the
enterprise data
Ensures compliance with data reporting laws and
regulations
Assists in identifying solutions to data reporting issues
Communication/interface with regulators
Non-confrontational mechanism for dialog
Represents the company to the regulator and brings
back information on regulatory perspectives, allowing
for better decision making.
55