webinar: initiating a customer mdm/data governance program

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

© 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

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.


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


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


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


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


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


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


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

webinar: initiating a customer mdm/data governance program

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