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
Agenda
MDM Overview
Case Study Overview
Data Discovery Pilot
Using erwin Modeling
Summary of Benefits
MDM Overview
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▶ 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
MDM : A Key Data Governance Domain
Metadata Management
Data Quality/Data Profiling
Information Life-Cycle Management
MDM/Reference Data
Data Security/Privacy
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.
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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
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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
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
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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
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
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
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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
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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
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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
Analyze and Harmonize DifferencesComplete Compare
• Identify inconsistencies• Analyze differences• Synchronize metadata and
structures• Mark and document
differences• Report results of compare
and sync processes
Centralize Models for Data Governance and Metadata Configuration
Publication, Governanceand 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
Enable Collaborative Stakeholder Exploration and Contribution
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)
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
erwin Inc. acquires Corso - Agile EAExpanding the Architecture and Governance Foundation
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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.
Q and A