The document outlines five steps for mastering data management: discovery, analysis, design, implementation, and establishing data governance. It emphasizes the importance of identifying data sources, authoritative data, and integrating data for better decision-making. Additionally, it discusses the challenges these steps may encounter and provides suggested tools and techniques to facilitate the process.

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

2. 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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3. 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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4. 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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5. 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 (
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)
• Data Modeling and Profiling tools (ER/Studio Data Architect/DBOptimizer)
• Application Profiling tools (NitroSecurity APM)
• Repository to manage the metadata byproducts
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6. Infrastructure / Security Tooling
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7. Use ER Studio to Reverse Engineer
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8. Reverse Engineer Physical Schemas
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9. Example Reverse Engineered Model
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10. Start Building Master Data Catalog
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11. Exporting Catalog for Sharing
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12. 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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13. Profiling Data Use with DBOptimizer
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14. 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
• Primary Challenges-
• 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
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15. 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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16. Analysis Phase – Evaluating ETL
• Biggest Challenges We’re Solving:
• 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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17. Data Lineage and Transform Rules
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18. Setting the Foundation for Governance
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19. Analysis Phase – Identifying Synonyms
• Biggest Challenges We’re Solving:
• 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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20. Performing Analysis With Compare Utility
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21. Exporting to Excel for Input into Database
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22. Candidates for Consolidation
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23. 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
• Primary Challenges-
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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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24. 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
• Primary Challenges-
• 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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25. 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)
• Primary Challenges-
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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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26. 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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27. 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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