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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