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1. 04/16/2017
MDM Tool Comparison

2. Copyright © 2009 Deloitte Development LLC. All rights reserved.2
What are MDM Solutions?
MDM solutions are software products that:
■ Support the global identification, linking and synchronization of master data across heterogeneous data
sources through semantic reconciliation of master data.
■ Create and manage a central, persisted system of record or index of record for master data.
■ Enable delivery of a single view of one or more subject areas to all stakeholders, in support of various
business initiatives.
■ Support ongoing master data stewardship and governance requirements through workflow based
monitoring and corrective-action techniques.
■ Are "agnostic" with regard to the business application landscape in which they reside; that is, they do not
assume or depend on the presence of any particular business application(s) to function.

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Why to Implement MDM?
Business Strategy
• Improve customer retention
• Grow through cross-sell & up-sell
• Decrease enterprise expense ratio
• Improve regulatory and audit responsiveness
Operational goals
• Back-Office Simplification
• Improved Customer Relationship Management
• Product rationalization
• Service delivery transformation
• Business intelligence & analytics improvements
Enabling MDM
capabilities
• Data accuracy improvements through harmonization & duplicate
reduction
• Transparency and availability of data across business lines &
channels
• Improved Security to create & change master data with history &
audit trail
• Streamlined experience enabled by self-service capabilities and
improved enterprise delivery

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Gartner Magic Quadrant

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MDM
Functional
Capabilitie
s
Workflow/BPM
Loading, synchronization, business services and integration
Data modeling
Information quality and semantics
Performance, scalability, high availability and security
Hierarchy management
Information stewardship support
Information governance support
Multiple implementation style support
MDM Functional Capabilities

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MDM Implementation Approach: Start with Quick Wins
In an MDM implementation, one of the guiding principles is to think big strategically but start projects in smaller phases.
Start mastering sources that contribute to most profitable book of business.
Objective
Key
Activities
Vision and Planning Enablement Adoption Transition
Validate capabilities
and refine scope and
approach
Develop enterprise
model to support core
customer data
Expand solution by
integrating with new
customer business
units
Expand solution by
integrating additional
sources
Define “Customer”
and the scope of
SVOC
Define data
governance strategy
for customer data
Develop business
case and roadmap
for delivering
Customer Data
Integration (CDI)
technology and
SVOC capabilities
Design customer
(Party) data model
Develop data
standardization,
matching, and
householding rules
Integrate data
enhancements (e.g.
lifestyle, stage,
demographics)
SVOC education
strategy/work plan
Match/consolidate
customer data
across different
sources to provide
single view of the
customer
Expand core services
for limited real-time
capabilities
Support for real-time
synchronization with
operational systems
Tight integration with
operational systems
Manage capability
transfer process
Deliver training to
stakeholders
Augmentation
Expand solution by
integrating across
business lines
Preference/privacy
management across
channels
Integration of
customer records
across all line units
into the EDW
Expand core services
for limited real-time
capabilities
Develop training
material

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Comparison of MDM Tools - IBM Initiate MDM?
MDM Capability IBM Initiate Informatica Siperian Oracle DRM
Data Hubs
Packaged data model for Party and Product domains
Customizable data model to meet client requirements
Configurable decision points for linking/matching
Matching Capability
Automatic linking/un-linking
Manual linking/un-linking/override match results
Integration Capability
Batch Integration
Real-time Integration (SoA services API support)
Real-time publishing
Batch publishing
Application support for Data Governance and Data Stewards

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Parameters Description
Scalability
Vertically Scalable  
Horizontally Scalable  
Linear Scalable (Max CPU Usage)  
Parallelism
Automated Partitioning

(Dynamic partitioning)

DB Partitioning  
Workflow/Object Partitioning  
Transformation Partitioning  
Degree of Parallelism Supported
Limited
(But depends on # of
core, RAM, CPU’s)
5
ERP/CRM
Support
SAP 

(SAP product,
hence preferred)
JD Edwards  
PeopleSoft  
Oracle EBS  
SAS  
Comparison of MDM Tools – Informatica vs BODS

9. Copyright © 2009 Deloitte Development LLC. All rights reserved.9
Parameters Description
ERP/CRM Support
Salesforce  
Mainframe  
Message Queue / Web services

(Ultra Messaging
Queuing Edition)

Memory and Hardware
(HA System)
Space Requirement (3 - Tier) 2 x 20GB 2 x 23 GB
OS Supported (Only Linux and Windows) Both Both
RAM (3 - Tier) 2 x 8 GB 2 x 8 GB
No. of Processors (Frequency) 4 CPU 2 x 4 (2 GHz)
Cache management

(detailed level)

Client/Server Arch
Active – Active Arch (Cluster Aware)  
Active – Passive Arch  
Client Server Architecture  
Comparison of MDM Tools – Informatica vs BODS

10. Copyright © 2009 Deloitte Development LLC. All rights reserved.10
Comparison of MDM Tools – Informatica vs BODS
Parameters Description
Capabilities
Ease of connectivity

(connectors available for larger system)

Volume Handling

(better performance when data volume high
and ETL logic complex)

Delta Mechanism

(Data Source Based CDC components
available)

Real time load capability  
Load failures

(Supports Recovery Mechanism)

Easy maintenance job scheduling
mechanism

(Provides in-built scheduling component)

Slowly changing dimension handling  
Complex Transformation  

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Comparison of MDM Tools – Informatica vs BODS
Parameters Description
Capabilities
Clustering and job distribution

(better at detailed level)

Debugging  
Data Governance and Data quality

(Supported using Informatica Data Director
and Informatica Data Quality Tools)

(Standard Information Stewart is
available)

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Guiding principles - Lessons learned
Best Practice Benefits/ Impacts
Data profiling before and after MDM
implementation
Compare data quality metrics and share with data
stewards in order to measure process effectiveness
through data quality improvement
Business stakeholder engagement
in defining mastering rules
Share attribute validation report with business users,
so that they can make an informed decision while
defining source survivorship and matching rules
Build consensus across all
consuming applications around
‘golden’ record
Set expectation with all consuming application owners that
the golden record may(most likely) look different from that
of the source systems
Define impact of historical data on
mastering rules
Define an archival strategy for historical data, so that the
mastering process considers the most recent version of
source data while generating the golden record
Future dated transactions should
not influence mastering
Customers that don’t actively pursue business with
the client at any given time, shouldn’t be mastered
until they are effective
Maintain reference data, hierarchies
etc. in an application that can
maintained by the business.
Maintain data enumeration rules, hierarchies(e.g.
product, sales hierarchy etc.) and standards(e.g.
address standardization, gender derivation etc.)
within a tool, MDM tools can do this, but it should be
owned by Enterprise Data Governance organization,
not the MDM project team.
Persist golden view within database Make provision to persist golden view generated by
MDM, outside of the application in order to increase
consumability

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Reference
For additional information, please contact:
Subbu Panigrahi
Consulting - Commercial
supanigrahi@deloitte.com
Rajeev Krishnan
Consulting - Commercial
rajkrishnan@deloitte.ca
David Helmuth
Consulting - Commercial
dhelmuth@deloitte.com
Tami Frankenfield
Consulting - Commercial
tfrankenfield@deloitte.com
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