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Unlocking Success in the 3 Stages of Master Data
Management
July 15, 2014
Perficient is a leading information technology consulting firm serving clients throughout
North America.
We help clients i...
• Founded in 1997
• Public, NASDAQ: PRFT
• 2013 revenue $373 million
• Major market locations throughout North America
• A...
BUSINESS SOLUTIONS
Business Intelligence
Business Process Management
Customer Experience and CRM
Enterprise Performance Ma...
Shankar RamaNathan
Sr. Solutions Architect | Enterprise Information Solutions CWP
Shankar RamaNathan is a sr. solutions ar...
Introduction
48%
45%
29%
24%
0% 10% 20% 30% 40% 50% 60%
In general we spend more time reconciling data
than analyzing it
T...
Introduction
48%
45%
29%
24%
0% 10% 20% 30% 40% 50% 60%
In general we spend more time reconciling data
than analyzing it
T...
Introduction
48%
45%
29%
24%
0% 10% 20% 30% 40% 50% 60%
In general we spend more time reconciling data
than analyzing it
T...
Introduction
48%
45%
29%
24%
0% 10% 20% 30% 40% 50% 60%
In general we spend more time reconciling data
than analyzing it
T...
Agenda
Planning Stage MDM trigger points
Building the business case
Prep work
Implementation
Stage
Data governance
Key dec...
Triggers
• Multiple versions
• Enterprise view not
possible
Business case
• Why do we need
MDM?
• What are the
consequence...
Check List
• Lay the foundation for co-
managing data
• Identify SME’s
• Collect as many pain points as
you can
• Assess t...
Implementation Stage
Governance
• Performance metrics
• Business
involvement
Key Decisions
• Scope
• Process changes
• Per...
Transaction Data
Integration
ETL DQ
Change Data
Big Data Integration
Load Mapreduce
Aggregation
Master Data
Management
Enr...
Data Management Tools Landscape
Applications 
(ERP,CRM etc.)
Data Profiling
DQ Tools (Address 
Enhancement)
ETL
SOA
Workfl...
Check List
• DG – Organization
• DG – Roles & responsibilities
• DG – Representation
• DG – Operating procedures
• Archite...
Steady State
Measurement
•DQ metrics
•SLA’s
•Access
Support
•Do we have the metrics
captured and reported?
•Are we meeting...
Check List
• Metrics measurement & reporting
• ITIL – service support
Steady State - Checklist
ITILITIL
Incident
Managemen...
 Measure against alignment, specific outcomes and effectiveness from business 
perspective to achieve business satisfacti...
As a reminder, please submit your
questions in the chat box
We will get to as many as possible
Daily unique content
about Information
Governance, content
management, user
experience, portals
and other enterprise
infor...
Thank you for your participation today.
Please fill out the survey at the close of this session.
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Unlocking Success in the 3 Stages of Master Data Management

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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

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Transcript of "Unlocking Success in the 3 Stages of Master Data Management"

  1. 1. Unlocking Success in the 3 Stages of Master Data Management July 15, 2014
  2. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 20. As a reminder, please submit your questions in the chat box We will get to as many as possible
  21. 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 Facebook.com/Perficient Twitter.com/Perficient
  22. 22. Thank you for your participation today. Please fill out the survey at the close of this session.
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