5 Steps To Master Data Management

Five Steps to Mastering Master Data Management
Ron Lewis
November 19, 2009

Presentation Overview

• Introduction
• What is Master Data Management?
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• The 5 Steps for Master Data Management:
• Discovery – finding all of the data sources, who they are used by and how they are used
• Analysis – identifying authoritative sources, discrepancies, and candidates for consolidation
• Design – designing the metadata repository
• Implementation–implementing a metadata repository
• Establish data governance

• Leveraging Technology to facilitate:
• Business Process and Data Modeling
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• Data Governance and Discovery
• Metadata Repository Implementation
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• Metadata Management

• Presentation Focus: The Discovery and Analysis Phases
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Master Data Management

• Master Data Management
• Master Data is: Principle business data essential for conducting business
• MDM provides an enterprise perspective on the critical Business Processes and the Data necessary to
support them
• Bottom line: Improve decision making

• Core Tasks
• Building the Business Process Models
• Data Governance (Standardizing data - nomenclature, domains, data quality and consumption rules)
• Synchronizing related operational systems using the data
• Integrating/reconciling disparate data silos to provide single enterprise view
• Building and managing an enterprise metadata repository

• Challenge: Must Shift Thinking to the Enterprise Perspective

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Discovery Phase

• Step 1 – Discovery
• Capturing and modeling the essential business processes
• Mapping processes to the data necessary to complete each process successfully
• Identifying data sources and gathering appropriate metadata

• Primary Challenges-
• Cost - It’s Expensive and Disruptive
• Gaining Executive Leadership Support – (“You mean we don’t have this already?”)

• Solution
Solution-
• Start with what’s most important
• What’s important should be obvious

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Discovery Phase

• Involve your infrastructure and/or security personnel
• Iteration I: Capture existing data and schemas
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• Find your database servers, respective owners and access
• Reverse engineering your physical data models
• Build a master data dictionary and catalog
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• Iteration II: Profile existing applications to help with business
• Database Centric: ETL, Stored Procedures, and Triggers
• Application Source Code and User Behavior

• Tools You’ll Need
• Infrastructure/security tools (
y (Nessus)
)
• Data Modeling and Profiling tools (ER/Studio Data Architect/DBOptimizer)
• Application Profiling tools (NitroSecurity APM)
• Repository to manage the metadata byproducts
p y g yp

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Infrastructure / Security Tooling

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Use ER Studio to Reverse Engineer

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Reverse Engineer Physical Schemas

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Example Reverse Engineered Model

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Start Building Master Data Catalog

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Exporting Catalog for Sharing

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Discovery – Profiling Data Use

• Biggest Challenges We’re Solving:
• Reconciling and integrating disparate “Data Silos” into a central location
• Identifying duplicative data elements (or attributes)
• Laying the foundation for identifying which of the data sources contain the actual “source data”

• High Percentage of Business Logic is encapsulated as Programming Logic
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• Stored Procedures and Trigger code stored in the database
• Application Source Code
• Extract Transform and Load Scripts
• We need visibility to this logic, and we need to be able to store it somewhere

• Tools necessary for this:
• DSAuditor and DB Optimizer or Performance Center (to capture live data use)
• Source Code Analyzers (I like Fortify SCA, and Embarcadero JBuilder)
• Profile ETL using Embarcadero’s MetaWizard (usually convert ETL to XML)
• Store metadata in ER/Studio Data Architect’s Data Lineage and Transform Rules Support

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Profiling Data Use with DBOptimizer

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Analysis Phase

• Step 2 – Analysis
• Identifying authoritative sources, discrepancies, and candidates for consolidation
• Evaluating Data Flow and Transform Rules
• Capturing/Defining Synonyms and Assigning Aliases
• Setting the Foundation for Data Governance

• Cost – It’s Time Consuming and is a “Team Effort”
• Getting ancillary information that teams don’t want to share
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• Solution-
• Start with what’s most important
• Wh ’ i
What’s important should b obvious
h ld be b i

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Analysis Phase

• Iteration I: Evaluate ETL for data lineage and transform rules
• Start by reverse engineering the ETL, converting it to XML
• Incorporate it into the repository

• Iteration II: Identify synonymous elements and build alias list
• Evaluate data domains and transform rules for issues such as state and use
• Enlist database and development staff to identify alias and tag the data elements in the master catalog

• Tools You’ll Need
• Data Modeling tools (ER/Studio and MetaWizard)
• Repository to manage the metadata byproducts (ER/Studio)

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Analysis Phase – Evaluating ETL

• Finding which data source is feeding what other data sources
• Collecting Data Lineage metadata
• Making it accessible to the right team members

• Convert the ETL to a form that allows manipulation (
p (such as XML) )
• Importing the metadata into the data modeling tool
• Build, publish and control access to your master data repository
• Start gathering and applying metadata tags
• Tools necessary for this:
• MetaWizard
• ER/Studio Data Architect (or the like)

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Data Lineage and Transform Rules

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Setting the Foundation for Governance

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Analysis Phase – Identifying Synonyms

• Indentifying like data elements and candidates for consolidation
• Building Aliases
• Establishing the foundation for Data Governance

• Evaluate data nomenclature using tool functions such as Merge and
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Compare to identify the obvious overlaps
• Compare descriptors from database staff
• Compare data use and consumption rules derived from tools such as DB
Optimizer
• Tools necessary f this:
for
• ER/Studio Data Architect (or the like)

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Performing Analysis With Compare Utility

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Exporting to Excel for Input into Database

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Candidates for Consolidation

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Step 3 Building the Repository

• Step 3–Building Metadata Repository
• Populating the Repository with the right metadata
• Establishing and Controlling Access to the metadata
• Performing metadata management

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• Defining who needs access to what metadata
• Establishing the rules of use

• Suggestions
Suggestions-
• Implement change control and auditing tool
• What’s important should be obvious
• Understand the value of the metadata on profitability

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Step 4 Implementing the repository

• Step 4 - Implementing the repository
• Mapping the metadata to the requisite business processes
• Leveraging the metadata to determine candidates for business process re-engineering

• Getting the p
g processes down in modeled form
• Obtaining Middle Level Management and Senior Leadership buy in to changes identified by metadata

• Suggestions-
• Leverage a modeling tool that facilitates data to process mapping (integrated metadata)
• Focus on what’s most important to the business—try not to focus on EVERYTHING

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Step 5 Establishing Data Governance

• Step 5 – Establishing Data Governance
• All of the above steps lays the foundation for good data governance
• Get Senior Leadership to stipulate policy enforcing the rules you’ve derived
• Build a Plan and Standardize Iteratively – (don’t try to fix everything all at once)

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• Fundamental Opposition to Change
• Maintaining Momentum

• Suggestions
Suggestions-
• Find a quick kill – tackle the biggest organizational problem you can handle
• Focus on what’s most important to the business—and what drives easily visible ROI

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Summary

• What We Covered:
• Defined Master Data and Master Data Management
• The 5 Steps for Master Data Management:
• Discovery – finding all of the data sources, who they are used by and how they are used
• Analysis – identifying authoritative sources, discrepancies, and candidates for consolidation
• Design – designing the metadata repository
• Implementation–implementing a metadata repository
• Establish data governance
• Demonstrated how to leverage specific technology to facilitate:
• Business Process and Data Modeling
• Data Governance and Discovery
• Metadata Repository Implementation
• Metadata Management

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Questions and Answers

• Tools Discussed:
• Nessus
• ER/Studio Data Architect / Business Architect and ER/Studio Repository
• DBOptimizer
• Change Manager

• Technologies Discussed:
• Building the Data Catalog
• Capturing and Storing Metadata
• Metadata Analysis

• Contact Info:
• Ron Lewis, Ron.Lewis@cdotech.com

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5 Steps To Master Data Management

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