Now that your organization has decided to move forward with Master Data Management (MDM), how do you make sure that you get the most value from your investment? In this webinar, we will cover the critical success factors of MDM that ensure your master data is used across the enterprise to drive business value. We cover: · The key processes involved in mastering data · Data Governance’s role in mastering data · Leveraging data stewards to make your MDM program efficient · How to extend MDM from one domain to multiple domains · Ensuring MDM aligns to business goals and priorities

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The First Step in EIM
Master Data Management
Ensuring Value is Delivered

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Agenda
• Why is MDM important?
• Why is MDM challenging?
• How do we ensure it’s successful?

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[ WHY IS MDM IMPORTANT? ]

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Business and IT Drivers
 Reduce operational costs
 Increase sales force effectiveness
 Improve sales and profits
 Strengthen customer relationships
“A manufacturer can expect to save from $800,000 to $1.2 million for
every $1 billion in sales by achieving data sync.”
“Businesses that use a formal, enterprise-wide strategy for Global Data
Synchronization will realize 30% lower IT costs in integration and data
reconciliation at the departmental level through the rationalization of
traditionally separate and distinct IT projects.”
Analysts Agree… MDM is Important for Addressing Key Business Requirements

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MDM is the Foundation to EIM Vision
MDM provides foundational capabilities to achieve broader information management vision
Process
Automation
Architectural
Improvements
Flexible Data
Architecture
IT
Transformation
and
Adaptability
PAST PRESENT FUTURE
Transaction
Management
Data
Warehousing
Master Data
Management
Integrated Information
Management and Delivery
Process automation and management of transactions with application specific data
within isolated business applications including ERP, CRM, SCM, eCommerce and other
systems over the past decade
Data extraction and normalization for operational as well as management
reporting and functional analytics. Data integrity and lack of standards have
constrained the maturity of data analytics in the past.
MDM and PIM comprises a set of processes, governance, policies, and
tools that consistently define and manage the master data or
foundational data that supports core business process and is
required for accurate data analytics and decision-making
EIM and adaptive architecture to
deliver business capabilities and
flexibility to future changes
Big Data
Management
Integration and management of big data and its
relationship across the enterprise through people,
processes and technology. Find insights in new types of
data, makes an organization more agile, and answer
questions that were previously considered beyond reach

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Challenges of MDM Success
According to a recent TDWI survey, many of the MDM challenges are organizational and
collaborative issues—not technical ones.
Half of users surveyed (56%) realize that MDM can be hamstrung without data governance.

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[ LEVERAGE GOVERNANCE ]

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Data Governance Definition
Data Governance is the organizing
framework for establishing strategy,
objectives and policy for effectively
managing corporate data.
It consists of the organization,
processes, policies, standards and
technologies required to manage and
ensure the availability, usability,
integrity, consistency, auditability and
security of data.
Communication
& Metrics
Data
Strategy
Data Policies,
Processes &
standards
Data
Modeling
&
Standards
A Data Governance Program consists of the inter-workings of
strategy, standards, policies, measurements and communication.

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Governance provides business
context
Master Data Management
MDM Strategy
Technology Infrastructure
MDM Organization
Components
Data Architecture
& Security
Data
Mastering
Data
Quality
Data
Sharing
Measurements
& Monitoring
Metadata
Management
GOVERNANCE
ORGANIZATIONAL ALIGNMENT

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Governance Decisions for MDM
Category Decision
Entity Types • What type of data will be managed in the MDM Hub
• What are the agreed upon definitions of each type
• What is the required cardinality between the entity types
• What constitutes a unique instance of an entity
Key Data Elements • Purpose, definition and usage of each data element
Hierarchies and
Relationships
• Purpose, definition and usage of each hierarchy /
relationship structure
Audit Trails and History • How long do we have to keep track of changes
Data Contributors • What type of data do they supply
• Why is this needed
• At what frequency should they supply it
• What should be taken for Initial load versus ongoing

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Governance Decisions for MDM
(cont.)
Category Decision
Data Quality Targets • How good does the data have to be
• Root cause analysis
Data Consumers • Who needs the data and for what purpose
• What do they need and at what frequency
Survivorship • What should happen when…
Lookups • Which attributes are lookup attributes
• What are the allowable list of values per attribute
• How different are the values across the applications
and how do we deal with inconsistencies
Types of Users and Security • What types of users have to be catered for
• Can they create, update, delete, search
• Can they merge, unmerge
Delete • How should deletes be managed
Privacy and Regulatory • Privacy and regulatory issues
Recommendations Meeting – Master Data Management (MDM) Assessment 071411

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[ MDM POLICIES & PROCESSES ]

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Creating Policies
Charter Principles Policies Processes Procedures

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MDM Policies
• Security, Privacy, Access, Visibility
• Party – Rules supporting:
— Party relationships
— Hierarchies
— Data lifecycle - CRUD
— Data classification
— Data integrity
• Product – Rules supporting:
— Product relationships
— Product definition
— Product components (Items) and their relationship to Product

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Standard MDM Processes
• Exception Handling
• Duplicate Handling
• Consensus Delete
• Company / Customer On-boarding
• Company Merger
• Hierarchy Management
• Match / Merge
• Data Quality

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Issue Management Process
Decision
Meeting
Data
Governance
Working
Group Chair
Data
Governance
Working
Group
Coordinator
Impacted
Business
Lead/Data
Steward
Identifying
Business/IT
Formalize
recommendation
Identify options/
evaluate
implications
Issue identified by
Business/IT
Identify issue
type and severity
Stewards
consults other
Stewards
regarding issue
(weekly stewards
meeting)
Identify options/
evaluate
implications
(impact
assessment)
Issue and supporting
documentation
brought to
Coordinator
Issue and impact
logged in issues
log
Chair reviews
issues log
Issue is
evaluated in
monthly meeting
Update all
documentation
Review issue and
impact
assessment
Update issue and
impact
assessment, if
necessary
Formalize
recommendation
Voting
membership
votes
Coordinator
closes
issue
Publish
communication
Issue and impact
assessment brought to
Business Lead/Data
Steward
Communicate
analysis and
recommendations
back to DQS

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[ ENSURE ALIGNMENT ]

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Alignment Process
• Why is this
important?
• Why should
we care?
Value
• Who cares?
• Why should
they care?
Stakeholders
• How does the
value benefit
the
stakeholders?
Linkage

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Example: Stakeholder Map

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MDM Program
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Sales/Marketing
Improve Segmentation
Understand Risk
Optimize Relationships for
Revenue
IT
Improved Productivity
Proactively support business
Contain Costs
Single View
of Customer
Improved
Data Quality
Common Service
Platform
Example: Articulate Linkage
The Single Repository of Customer
data will improve my understanding
of customers by providing me a
trusted source of timely, accurate
and pertinent data from which to
execute analytics, segmentation and
risk assessment.
The common service platform for
data access and sharing will increase
IT productivity by providing a more
unified integration infrastructure.
This will enable IT to better support
the business in a timely manner
because there will be repeatable
processes and less rework.

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[ MEASUREABLE SUCCESS CRITERIA ]

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Metrics and Measurement
• Metrics and Measurement
 The right metrics help maintain alignment
 Metrics define the requirements for the information you
need to answer the questions
 Measurement is the data reviewed, tracked and reported on
an on-going basis
• Key Performance Indicator (KPI)
 A Key Performance Indicator (KPI) is a quantifiable metric
that the MDM Program has chosen that will give an indication
of MDM program performance.
 A KPI can be used as a driver for improvement and reflects
the critical success factors for the MDM Program
• A metric is not necessarily a KPI

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Example: Metrics and KPI’s
Measurement Target Frequency
Increased confidence in the quality of information
Reduce time spent in data reconciliation activities
Number of requests coming into the DG Group
Data owner assigned for each entity type
Length of time from account opening to availability online
Number of target systems using master data
Reduce time spent on creating a common customer list after
an acquisition
Improved ability to find the right data quickly and easily
Data quality becomes a part of performance objectives
across LOB’s
Presence/Usage of a unique identifier
Survey – yes
50%
Increasing
100%
24 hours
10
20% reduction
from previous
Survey – yes
Increasing
100%
Every 6 months
Monthly
Monthly
Quarterly
Monthly
Quarterly
After every
acquisition
Quarterly
Quarterly
Quarterly

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Impact Determines Success
Issues
• Report Quality
and Accuracy
• Low
Productivity
• Regulatory
Compliance /
Audit
Response
Goals
• Improve data’s
usability
• Improve
efficiency and
productivity
• Reduce
compliance /
audit cost
Metrics/KPI’
s
• Data Quality
• Data
remediation
time
• Effort to
comply
• Use of
identifiers
Impact
• Improve client
relationships
• Address new
markets
• Reduce/avoid
fines
• Improve
analysis &
decision
making

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[ EXTENDING MDM ]

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Extending MDM to the Enterprise
• Create a Roadmap:
 Steps to implement and operationalize a MDM program in a
phased approach given known IT and Business initiatives
 Presentation / high level work plan detailing the phased
implementation steps necessary, resource requirements and
potential costs involved to deliver the intended future state

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Roadmap Overview
6 Months 12 Months 18 Months 24 Months
Data Quality
Client & Prospect
Contact
& Partner Extend
DQ
Product & Account
Goals:
• Establish DG program
• Business Case Approval
• Establish DQ Foundation
• Profiling
• Reporting
• Scorecards
• Define Client, Prospect &
other Entity types and
attributes
Goals:
• Establish the MDM
Foundation
• Matching
• Profiling
• Reporting
• Single Source for Client
& Prospect
• Single Source for Credit
Lines
Goals:
• Single Source for Contact
& Partner
• DQ at point of entry
• Enable reporting and
analysis groups
• Enable Address
synchronization across
operational systems
Goals:
• Measure, Refine &
Monitor
• Single Source for
Product and Account
• 360 degree view of
client
• Improve monitoring of
master data across
operational systems
Operationalize Data
Governance
DG Management and Maintenance
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Keys to Success
Successful MDM
Implementation
Technology
Process
People
Failed MDM
Implementation!
Technology
Process
People

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The First Step in EIM
Contact Info
www.firstsanfranciscopartners.com
Kelle O’Neal
kelle@firstsanfranciscopartners.com
415-425-9661
@1stsanfrancisco