Data Management Strategy

Maturing the Data Management Practice
A comprehensive analysis of process and organizational structure for delivering trusted, quality Business Intelligence
May, 2012
A Business Analysis CASE STUDY By David Ladd, Axis Business Strategist
A top-five Property and Casualty Insurance Carrier needed to improve the breadth and
accuracy of its Business Intelligence. The current platform was inflexible and limited
the carrier’s ability to respond to a dynamic business environment. The company
needed to perform comprehensive on-demand analysis to enable development of
quick yet thorough response strategies.
Maturing the Data Management Practice A Business Analysis CASE STUDY
The Client had been aggressively growing
its book of business over the past several
years. The objective of each new
acquisition was to integrate as efficiently as
possible, thereby accelerating the realization
of benefits from the merger. This lead to
systems and organizations that were stove-
piped and disjointed.
The goal of the assessment was to examine
how data is managed for systems and
organizations across the stove-piped
boundaries. This began with a high-level
inventory of current Data Management
practices and processes, as well as a
comparison to industry best practices.
The assessment process engaged key
subject matter experts to identify needed
business capabilities and describe the data
management roadblocks.
Based on the organization’s prioritized
needs, an initiative roadmap identified the
critical path items that would most benefit
the business.

A scoring system to establish
priorities and a Roadmap of
initiatives to show dependencies
and the critical path for delivery.
Although the client had an existing Data Management Practice, they wanted to meld industry
best practices into their organization to improve the ability of that Practice to serve the
business’ needs. This required thorough analysis of the current Data Management Practice
and an understanding of both the short-term pragmatic needs of the business as well as the
longer-term strategic needs. Subsequently, process and operational controls were required to
ensure that the business received data certified to meet tolerances for timeliness, accuracy,
and quality.
Introduction
Data Management Assessment


The System Development Life Cycle was extended to
deliver Data Quality Controls, Profiling, Data Architecture,
Security, and Metadata Capture.
Program Delivery
Job-Aid: MI Information Classifications
MI Data Elements are classified at two levels, Major and Minor. The Major
classifications tend to be business oriented and communicated in business terms.
They are expressed independent of technology and implementation. The Minor
classifications are based on existing technologies and implementation details.
Major Elements
Major elements are defined according to the following business-oriented
classifications:
 Measure
 Metric
 Dimension.
These are defined as follows:
Measure – The elements of information that managers use to monitor their
business. Measures are what business managers use to find new trends,
look for innovation opportunities, or quantify the success or failure of the
organization.
Example:
 Closure Rate – The % of Claims with a net incurred amount that
are closed.
Metric – A target or benchmark associated with a measure. Different
business units may use different metrics.
Examples:
 Business Unit 1 objective is 90% Closure Rate
Dimension – Dimensions are elements used to describe and add meaning
to measures. For example, dimensions can qualify measurements by
product, market, time, period, etc. When the dimensions are combined
with measures end users are empowered to answer specific business
questions.
Examples:
 by Region
 by Month
 by Product
Major Element Summary
Combining Measures, Metrics, and Dimensions will yield the basis for
management information that supports business analysis.
Example:
 The Number of Claims with a net incurred amount that closed
within the 90% threshold by region.
Minor Elements
Job-Aid: MI Information Classifications
MI Data Elements are classified at two levels, Major and Minor. The Major
classifications tend to be business oriented and communicated in business terms.
They are expressed independent of technology and implementation. The Minor
classifications are based on existing technologies and implementation details.
Major Elements
Major elements are defined according to the following business-oriented
classifications:
 Measure
 Metric
 Dimension.
These are defined as follows:
Measure – The elements of information that managers use to monitor their
business. Measures are what business managers use to find new trends,
look for innovation opportunities, or quantify the success or failure of the
organization.
Example:
 Closure Rate – The % of Claims with a net incurred amount that
are closed.
Metric – A target or benchmark associated with a measure. Different
business units may use different metrics.
Examples:
Dimension – Dimensions are elements used to describe and add meaning
to measures. For example, dimensions can qualify measurements by
product, market, time, period, etc. When the dimensions are combined
with measures end users are empowered to answer specific business
questions.
Examples:
 by Region
 by Month
 by Product
Major Element Summary
Combining Measures, Metrics, and Dimensions will yield the basis for
management information that supports business analysis.
Example:
 The Number of Claims with a net incurred amount that closed
within the 90% threshold by region.
Minor Elements
The Data Management initiatives ensured
development of programs that would
integrate across the “6 pillars” of Data
Management, Data Governance being at
the center.
Data Management was integrated into the
delivery lifecycle. This ensured that the
business needs for timeliness, accuracy,
and quality were captured in the
requirements phase, implemented in the
development phase, and then verified and
approved in the testing phase.
Additionally, operational controls
established periodic Quality Audits
to pro-actively identify data quality
problems and their root causes
before the business could be
negatively impacted. The activity
lowered the incidents of reporting
delays due to data correction and
reloading.
The metadata repository provided
a common lexicon bridging the
business and technical
interpretations of business
intelligence. The result was
improved information consistency,
reduced redundancy, and
enhanced reusability.

The solution for maturing a Data Management Practice cannot use a “one size fits all”
approach. The current maturity level must be compared against the unique needs of the
organization to develop a roadmap of initiatives. The key is to understand the prioritized
needs of the business as a foundation for developing a strategy to mature the Data
Management Practice.
In Closing
The Knowledge Transfer Phase
ensured that all impacted parties
understood their roles and tasks
within the new process.
 Knowledge Transfer and Communication Plan
The Communication Plan explained
to all constituents what the
improved Data Management
Program would deliver and what it
meant to them. It delivered
periodic updates in the form of
performance metrics that monitor
progress over time.

Data Management Strategy

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Data Management Strategy