5 Steps To Master Data Management
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5 Steps To Master Data Management

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Embarcadero Technologies & Ron Lewis, Senior Security Analyst with CDO Technologies hosted a live one hour webinar on the "Five Steps to Mastering Master Data Management. Learn how a solid metadata ...

Embarcadero Technologies & Ron Lewis, Senior Security Analyst with CDO Technologies hosted a live one hour webinar on the "Five Steps to Mastering Master Data Management. Learn how a solid metadata repository can support data governance and increase the effectiveness of master data use.

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5 Steps To Master Data Management Presentation Transcript

  • 1. Five Steps to Mastering Master Data Management Ron Lewis November 19, 2009
  • 2. Presentation Overview • Introduction • What is Master Data Management? g • 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 g • Data Governance and Discovery • Metadata Repository Implementation g • Metadata Management • Presentation Focus: The Discovery and Analysis Phases 19/11/2009 2
  • 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 11/15/2009 3
  • 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 11/15/2009 4
  • 5. Discovery Phase • Involve your infrastructure and/or security personnel • Iteration I: Capture existing data and schemas p g • Find your database servers, respective owners and access • Reverse engineering your physical data models • Build a master data dictionary and catalog y g • 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 19/11/2009 5
  • 6. Infrastructure / Security Tooling 19/11/2009 6
  • 7. Use ER Studio to Reverse Engineer 19/11/2009 7
  • 8. Reverse Engineer Physical Schemas 19/11/2009 8
  • 9. Example Reverse Engineered Model 19/11/2009 9
  • 10. Start Building Master Data Catalog 19/11/2009 10
  • 11. Exporting Catalog for Sharing 19/11/2009 11
  • 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 g g g p g g g • 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 19/11/2009 12
  • 13. Profiling Data Use with DBOptimizer 19/11/2009 13
  • 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 g y • Solution- • Start with what’s most important • Wh ’ i What’s important should b obvious h ld be b i 11/15/2009 14
  • 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) 19/11/2009 15
  • 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) 19/11/2009 16
  • 17. Data Lineage and Transform Rules 19/11/2009 17
  • 18. Setting the Foundation for Governance 19/11/2009 18
  • 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 g g 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) 19/11/2009 19
  • 20. Performing Analysis With Compare Utility 19/11/2009 20
  • 21. Exporting to Excel for Input into Database 19/11/2009 21
  • 22. Candidates for Consolidation 19/11/2009 22
  • 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- y g • 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 19/11/2009 23
  • 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 19/11/2009 24
  • 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- y g • 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 19/11/2009 25
  • 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 19/11/2009 26
  • 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 19/11/2009 27