Master data management (MDM) comprises the processes, governance, policies, standards and tools that define and manage critical data. MDM is used to conduct strategic initiatives such as customer 360, product excellence and operational efficiency. The quality of enterprise Information depends on the master data, so getting it right should be a high priority. This webinar will highlight key factors needed for success in each of the three stages of the MDM journey: Planning Implementation Steady state We review each stage in detail and provide insight into planning and collaborative activities. In this slideshare you will learn: Best practices, tips and techniques for a successful MDM program Top considerations for business case building, architecture and going live How to support the overall program after launching your MDM program

1. Unlocking Success in the 3 Stages of Master Data
Management
July 15, 2014

2. Perficient is a leading information technology consulting firm serving clients throughout
North America.
We help clients implement business-driven technology solutions that integrate business
processes, improve worker productivity, increase customer loyalty and create a more agile
enterprise to better respond to new business opportunities.
About Perficient

3. • Founded in 1997
• Public, NASDAQ: PRFT
• 2013 revenue $373 million
• Major market locations throughout North America
• Atlanta, Boston, Charlotte, Chicago, Cincinnati, Columbus,
Dallas, Denver, Detroit, Fairfax, Houston, Indianapolis,
Los Angeles, Minneapolis, New Orleans, New York City,
Northern California, Philadelphia, Southern California,
St. Louis, Toronto and Washington, D.C.
• Global delivery centers in China, Europe and India
• >2,100 colleagues
• Dedicated solution practices
• ~85% repeat business rate
• Alliance partnerships with major technology vendors
• Multiple vendor/industry technology and growth awards
Perficient Profile

4. BUSINESS SOLUTIONS
Business Intelligence
Business Process Management
Customer Experience and CRM
Enterprise Performance Management
Enterprise Resource Planning
Experience Design (XD)
Management Consulting
TECHNOLOGY SOLUTIONS
Business Integration/SOA
Cloud Services
Commerce
Content Management
Custom Application Development
Education
Information Management
Mobile Platforms
Platform Integration
Portal & Social
Our Solutions Expertise

5. Shankar RamaNathan
Sr. Solutions Architect | Enterprise Information Solutions CWP
Shankar RamaNathan is a sr. solutions architect with Perficient. He has more than 20 years
of experience in successfully developing and implementing IT and information governance
strategies, as well as establishing BI and data governance committees and conducting
information governance workshops.
Speaker

6. Introduction
48%
45%
29%
24%
0% 10% 20% 30% 40% 50% 60%
In general we spend more time reconciling data
than analyzing it
There is no one clearly accountable for the
quality of information
We cannot be sure whose spreadhseet has the
correct data
Business rules for allocation of production and
marketing costs differ between locations
Top Data Issues
Source: TDWI

7. Introduction
48%
45%
29%
24%
0% 10% 20% 30% 40% 50% 60%
In general we spend more time reconciling data
than analyzing it
There is no one clearly accountable for the
quality of information
We cannot be sure whose spreadhseet has the
correct data
Business rules for allocation of production and
marketing costs differ between locations
Top Data Issues
40%
47%
33%
23%
60%
54%
47%
5%
0%
10%
20%
30%
40%
50%
60%
70%
Inaccurate decisions from
poor data
Lack of authoritative
system
Finding information is
complicated / lengthy
Business partners deman
better data exchange
MDM Drivers
Best in class All other
Source: Aberdeen

8. Introduction
48%
45%
29%
24%
0% 10% 20% 30% 40% 50% 60%
In general we spend more time reconciling data
than analyzing it
There is no one clearly accountable for the
quality of information
We cannot be sure whose spreadhseet has the
correct data
Business rules for allocation of production and
marketing costs differ between locations
Top Data Issues
40%
47%
33%
23%
60%
54%
47%
5%
0%
10%
20%
30%
40%
50%
60%
70%
Inaccurate decisions from
poor data
Lack of authoritative
system
Finding information is
complicated / lengthy
Business partners deman
better data exchange
MDM Drivers
Best in class All other
Success Rate of MDM – Source TDWI
Source: Aberdeen
39%
28%
16%
8%
7%
2%
1%
0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
Successful
Neither successful nor unsucessful
We don't have MDM technology
Very successful
Unsuccessful
Don't Know
Very unsuccessful
MDM success rate

9. Introduction
48%
45%
29%
24%
0% 10% 20% 30% 40% 50% 60%
In general we spend more time reconciling data
than analyzing it
There is no one clearly accountable for the
quality of information
We cannot be sure whose spreadhseet has the
correct data
Business rules for allocation of production and
marketing costs differ between locations
Top Data Issues
40%
47%
33%
23%
60%
54%
47%
5%
0%
10%
20%
30%
40%
50%
60%
70%
Inaccurate decisions from
poor data
Lack of authoritative
system
Finding information is
complicated / lengthy
Business partners deman
better data exchange
MDM Drivers
Best in class All other
Success Rate of MDM – Source TDWI
Source: Aberdeen
39%
28%
16%
8%
7%
2%
1%
0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
Successful
Neither successful nor unsucessful
We don't have MDM technology
Very successful
Unsuccessful
Don't Know
Very unsuccessful
MDM success rate

10. Agenda
Planning Stage MDM trigger points
Building the business case
Prep work
Implementation
Stage
Data governance
Key decisions
Development
Steady State
(Operations)
SLA’s
Performance metrics
ITIL process
Conclusion Measuring the success
Q & A
Planning
Implementation
Steady State

11. Triggers
• Multiple versions
• Enterprise view not
possible
Business case
• Why do we need
MDM?
• What are the
consequences of not
having an MDM?
Prep-work
• Opportunities
• Sponsorship
• Governance
• Tools selection
• Team building
Planning Stage
Triggers Business Case Prep-work
 Multiple CRM
 Unified messaging
 Product definition
 Hierarchy
 Data quality issues
 Enterprise view
 New ERP implementation
 Supplier discounts
 Customer inventory
 Vendor contact
 Customer life time value
 Data quality improvements
 Executive buy-in
 Co-managing data
 New platforms
 New capabilities

12. Check List
• Lay the foundation for co-
managing data
• Identify SME’s
• Collect as many pain points as
you can
• Assess the impact of not
having a MDM solution
Planning Stage - Checklist

13. Implementation Stage
Governance
• Performance metrics
• Business
involvement
Key Decisions
• Scope
• Process changes
• Performance
considerations
• Technology aspects
Development
• Opportunities
• Team building
• Architecture
Governance Key Decisions Development
 Organization
 Representation
 Agenda
 Communication
 Defining the scope
 Engaging the right stakeholders for process
changes
 Identifying and measuring - performance metrics
 Platform considerations
 Areas of improvement
 Key SME’s
 Overall architecture
 MDM
 Metadata
 DQ
 Enrichment
 SOA (Publication, Synchronization)
 Workflow

14. Transaction Data
Integration
ETL DQ
Change Data
Big Data Integration
Load Mapreduce
Aggregation
Master Data
Management
Enrich
Hierarchy
Transaction Systems
Data Governance
SAP CRM EBS
Business Rules/ Metadata
Business Glossary Compliance
Application CAD Web
External Data
Big Data
Architecture Security Information Quality
Other
EDW
Finance &
Accounting
Operational
Marketing
BPM / Workflow
Industry Specific
Subject Areas
Predictive
Prescriptive
Descriptive
Operational
Information Access Information Availability
Visualization
Analytics
Information Life Cycle
Lineage
DQ
Consolidate
Match & Merge
Reference Data
Auditing
Publishing
Downstream Applications / Sync
Publication
SOA/ETL
EDW Reference Architecture

15. Data Management Tools Landscape
Applications
(ERP,CRM etc.)
Data Profiling
DQ Tools (Address
Enhancement)
ETL
SOA
Workflow
Metadata
Management
Master Data
Management
Data Virtualization
Data Movement
(Replication)
Data Privacy
Identity Resolution
Data Warehouse
(Industry Models)
DW Appliance
In Memory
Database
Cloud Application
Cloud ETL /
Integration
Data Modeling
Cloud Platform
Services
Cloud Data
Enrichment
Data Lifecycle
Management
Big Data
(Structured &
Unstructured)
Data Visualization
Cloud Analytics
Analytics Platform
(Descriptive,
Predictive,
Prescriptive)
Content
Management
Security Tools

16. Check List
• DG – Organization
• DG – Roles & responsibilities
• DG – Representation
• DG – Operating procedures
• Architecture –
• Tools list
• Platform requirements
• Performance metrics (DQ)
• Performance metrics (SLA)
Implementation Stage - Checklist

17. Steady State
Measurement
•DQ metrics
•SLA’s
•Access
Support
•Do we have the metrics
captured and reported?
•Are we meeting the
SLA’s?
•Do we have process in
place for ITIL activities?
Continuous
Improvement
•SLA improvements
•Additional domains
•Capability enhancements
Measurement Support Continuous Improvement
 Data quality metrics
 Performance metrics
 Auditing / reporting
 ITIL –
 Incident management
 Problem management
 Release management
 Change management
 Metrics reporting
 Center of excellence
 Capability
 Capability improvements
 Governance effectiveness
 New Platforms / capabilities

18. Check List
• Metrics measurement & reporting
• ITIL – service support
Steady State - Checklist
ITILITIL
Incident
Management
Problem
Management
Change
Management
Release
Management
Configuration
Management
Service Level
Management
Financial
Management
Capacity
Management
IT Continuity
Management
Availability
Management

19.  Measure against alignment, specific outcomes and effectiveness from business
perspective to achieve business satisfaction
 Measure repeatability and completeness for continuous improvement of processes
Measuring Success

20. As a reminder, please submit your
questions in the chat box
We will get to as many as possible

21. Daily unique content
about Information
Governance, content
management, user
experience, portals
and other enterprise
information technology
solutions across a
variety of industries.
Perficient.com/SocialMedia
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Twitter.com/Perficient

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