Unlocking Success in the 3 Stages of Master Data Management
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Transcript of Unlocking Success in the 3 Stages of Master Data Management
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 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
• 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
BUSINESS SOLUTIONSBusiness IntelligenceBusiness Process ManagementCustomer Experience and CRMEnterprise Performance ManagementEnterprise Resource PlanningExperience Design (XD)Management Consulting
TECHNOLOGY SOLUTIONSBusiness Integration/SOACloud ServicesCommerceContent ManagementCustom Application DevelopmentEducationInformation ManagementMobile PlatformsPlatform IntegrationPortal & Social
Our Solutions Expertise
Shankar RamaNathanSr. 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
Introduction
48%
45%
29%
24%
0% 10% 20% 30% 40% 50% 60%
In general we spend more time reconciling datathan analyzing it
There is no one clearly accountable for thequality of information
We cannot be sure whose spreadhseet has thecorrect data
Business rules for allocation of production andmarketing costs differ between locations
Top Data Issues
Source: TDWI
Introduction
48%
45%
29%
24%
0% 10% 20% 30% 40% 50% 60%
In general we spend more time reconciling datathan analyzing it
There is no one clearly accountable for thequality of information
We cannot be sure whose spreadhseet has thecorrect data
Business rules for allocation of production andmarketing 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 frompoor data
Lack of authoritativesystem
Finding information iscomplicated / lengthy
Business partners demanbetter data exchange
MDM Drivers
Best in class All other
Source: Aberdeen
Introduction
48%
45%
29%
24%
0% 10% 20% 30% 40% 50% 60%
In general we spend more time reconciling datathan analyzing it
There is no one clearly accountable for thequality of information
We cannot be sure whose spreadhseet has thecorrect data
Business rules for allocation of production andmarketing 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 frompoor data
Lack of authoritativesystem
Finding information iscomplicated / lengthy
Business partners demanbetter 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
Introduction
48%
45%
29%
24%
0% 10% 20% 30% 40% 50% 60%
In general we spend more time reconciling datathan analyzing it
There is no one clearly accountable for thequality of information
We cannot be sure whose spreadhseet has thecorrect data
Business rules for allocation of production andmarketing 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 frompoor data
Lack of authoritativesystem
Finding information iscomplicated / lengthy
Business partners demanbetter 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
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
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
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
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
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 WebExternal 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
SO
A/ E
TL
EDW Reference Architecture
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
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
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
Check List
• Metrics measurement & reporting• ITIL – service support
Steady State - Checklist
ITILITIL
Incident Management
ProblemManagement
Change Management
Release Management
ConfigurationManagement
Service Level Management
FinancialManagement
Capacity Management
IT Continuity Management
AvailabilityManagement
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
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