This document discusses using erwin Modeling to execute a data discovery and analysis pilot for an MDM and data governance initiative. It provides an overview of MDM and describes a case study of an initial failed MDM attempt. The benefits of a model-driven approach using erwin Modeling are outlined, including discovering and documenting the as-is data landscape, enabling stakeholder collaboration, and specifying the to-be MDM architecture and governance foundation. Key activities of the proposed pilot with erwin Modeling are reverse engineering data sources, analyzing and harmonizing differences, centralizing models, and deriving an MDM specification blueprint. The benefits of accelerating MDM analysis cycles and establishing reusable processes for governance are summarized.

1. Accelerate and Assure your Customer MDM &
Data Governance Initiative
Executing a Data Discovery and Analysis Pilot with erwin Modeling
Danny Sandwell, Product Marketing, erwin Modeling
November 1, 2016

2. Agenda
MDM
Overview
Case Study
Overview
Data
Discovery Pilot
Using erwin
Modeling
Summary of
Benefits

3. MDM Overview
© 2016 erwin. All rights reserved. 3
▶ Master data management (MDM) is a comprehensive method of
enabling an enterprise to link all of its critical data to one file, called a
master file, that provides a common point of reference. When
properly done, MDM streamlines data sharing among personnel and
departments.
▶ There are several ways in which master data may be collated and
distributed to other systems. This includes:
– Data consolidation – The process of capturing master data from multiple
sources and integrating into a single hub (operational data store) for
replication to other destination systems.
– Data federation – The process of providing a single virtual view of master data
from one or more sources to one or more destination systems.
– Data propagation – The process of copying master data from one system to
another, typically through point-to-point interfaces in legacy systems.
Wikipedia

4. MDM : A Key Data Governance Domain
Metadata Management
Data Quality/Data Profiling
Information Life-Cycle Management
MDM/Reference Data
Data Security/Privacy

5. Keys to MDM Success
Enterprise Consumer Engagement
• Prior to designing and building a repository understand the user expectations
and requirements for that shared information.
Data Governance
• Putting the right amount of policy definition and management in place is
critical to ensure consistency of use for shared information and data
visibility.
Metadata Collaboration
• Prior to exposing a shared view of data to the community of data consumers,
one must provide a common view of the business terms, definitions and uses
that are pervasive across the application landscape.
© 2016 erwin. All rights reserved. 5

6. Case Study
▶ Traditional “brick and mortar” business with growing online routes to
market
– Multiple lines of business – Products and Services
– Large and varied customer base – Consumer and Corporate
– Stagnating revenues - Poor return on cross sell/up sell and new customer
acquisition programs
– Declining customer satisfaction and retention metrics
▶ Data management infrastructure spanning mainframe to the cloud
▶ Early MDM aspirations resulted in a failed MDM initiative
▶ No data modeling practice in place
© 2016 erwin. All rights reserved. 6

7. Initial MDM Failure - Contributing Factors
7
Lacking
business
sponsorship and
accountability
Focusing on
MDM technology
vs satisfaction of
business needs
Failure to
coordinate MDM
activities
initiated in silos
Inadequate
project/program
scoping and
resourcing
Implementing
MDM in a
vacuum, Not
underpinned of
aligned to an
enterprise data
management or
data governance
strategy

8. Starting Over: “Customer” MDM Goals From the Business
Achieve
360° view
and
connection
with
customer
• Increase customer
satisfaction and retention
• Capitalize on customer
relationships to drive
growth
• Accelerate new customer
acquisition
Deliver an
MDM
platform that
will:
• Ensure high quality and
consistent data that is
trusted by the business
• Promote a data centric
approach through
governance and
organizational data fluency
• Enable an agile and
effective analytics platform
• Drive operational excellence
through effective and
efficient integration
© 2016 erwin. All rights reserved. 8

9. Why a Model Driven Approach?
A low risk “apples to apples” facility enabling you to….
Visualize - Break down complexity
Abstract - Capture different perspectives
Standardize - Drive consistency thru reuse
Relate - Integrate data elements
Extend - Customize to need/purpose
Compare - Analyze gaps and deltas
Govern - Iterate and control change
Collaborate - Facilitate stakeholders

10. Integrated erwin Data Models Break Down
The Data Management Silos
Taxonomy Models
– Business Terminology
Conceptual Models
– Business Alignment
Logical Models
– Business Rules
Physical Models
– Data Deployment
Configuration Models
– Data Integration

11. Data Discovery Pilot with erwin Modeling
Document the
”As-is” Data
Landscape
Enable
Stakeholder
Awareness and
Collaborative
Analysis
Specify the
MDM “To-Be”
Architecture
Establish Data
Governance at
Program
Initiation
© 2016 erwin. All rights reserved. 11

12. Discover, Document, Visualize and Analyze
Document
the As-Is
Data
Landscape
Capture, standardize and categorize data source
metadata and structures
Document current points of integration and
transformations
Rationalize and harmonize differences
Enable
Stakeholder
Awareness
and
Collaborative
Analysis
Publish integrated business and technical
architectures/models/metadata
Facilitate self service discovery for technical and non-
technical roles
Create a managed feedback loop between
organizational silos
© 2016 erwin. All rights reserved. 12

13. Define, Specify, Integrate and Govern
Specify the
To-Be
Architecture
Define master record requirements
Define semantic and dataflow integration and
harmonization requirements
Enable planning: MDM scope, impact of change,
implementation phases
Establish
Data
Governance
Foundation
At Program
Initiation
Capture and integration business vocabulary and
terminology
Enable lineage and impact analysis
Specify and align ownership, accountability, policies,
and procedures
© 2016 erwin. All rights reserved. 13

14. Discover, Standardize and Document Relevant Data
Structures
Data Source
Reverse Engineer
and Visualize
• Naming Standards
• Datatype Standards
• Standard Domains
• User Defined Properties
• Standard Model Display
Themes Annotations
• Bulk Import and Editing of
Definitions
• Model Auto-Layout
• Active Standards Templates

15. Analyze and Harmonize Differences
Complete Compare
• Identify inconsistencies
• Analyze differences
• Synchronize metadata and
structures
• Mark and document
differences
• Report results of compare
and sync processes

16. Centralize Models for Data Governance and Metadata
Configuration
Publication, Governance
and Analysis
• Glossary Derivation and
Authoring
• Semantic Mapping
• Dataflow Mapping
• Configuration Models
• Lineage and Impact Analysis
• Model Visualization
• Metadata Drill-Down
• Metadata Reporting
• Metadata Tags
• Metadata Authoring

17. Enable Collaborative Stakeholder Exploration
and Contribution

18. Derive your Proposed MDM Design and Architecture
Derive MDM
Specification
• Requirements/Scope for
Build/Buy Analysis
• Blueprint to accelerate design
and integration of proposed
MDM
• Conceptual – Business
Alignment
• Logical – Business Rules
• Physical – Deployment
• Configuration – Points of
Integration
Model Derivation
Customer
Model 3
Customer
Model 2
Customer
Model 1
Customer
MDM
Model(s)

19. Benefits of the Data Discovery Pilot with erwin Modeling
Accelerate and enhance MDM data analysis cycles
Enable effective interactions between stakeholders
Optimize the specification of MDM requirements
Institute accountability for proposed MDM elements and
processes
Establish a repeatable process and reusable facility for
underpinning downstream MDM initiatives

20. erwin Inc. acquires Corso - Agile EA
Expanding the Architecture and Governance Foundation
© 2016 erwin. All rights reserved. 20
Expanding our Data
Management Platform
with erwinCloudCore™,
a Powerful, Cloud-
Based Data Modeling &
Enterprise Architecture
Bundle
Corso is disruptive technology in
a mature Enterprise Architecture
“EA” market that helps companies
align business strategy and IT
capabilities so they can plan and
react to change with agility.
Corso offers an integrated
enterprise architecture, portfolio
and innovation management
SAAS platform that gives you the
ability to manage business
transformation activities at any
scale.

21. Q and A