OAUG 05-2009-MDM-1683-A Fiteni CPA, CMA


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Oracle Application User Group sponsored Collaborate 2009 Presentation 'Building a Practical Strategy for Managing Data Quality' by Alex Fiteni CPA, CMA

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OAUG 05-2009-MDM-1683-A Fiteni CPA, CMA

  1. 1. Master Data Management Strategies – Data Quality Building a Practical Strategy for Managing Data Quality Alex Fiteni CMA, Fiteni International LLC http://www.fiteni.com http://blog.fiteni.com Presentation # 1683
  2. 2. Alex Fiteni CMA • Alex Fiteni CMA is a professional accountant whose career include comptrollership, business process improvement and business software development. • Alex is currently provides professional services in Master Data Management, ERP implementations, Project Management and Transaction Based Taxes. • Recent projects include: – R12 MDM Strategy, Data Quality & Data Conversion – Oracle E-Business Tax implementation for Canada – R12 Oracle E-Business Suite Solution Architecture & Project Plan Implementation #2
  3. 3. Topic Overview • When global MDM strategies are implemented, Data Quality is often a low priority until conversion is at hand. Here is a practical approach to making data quality a central theme of your migration strategy. 1. Identify the data quality issues facing enterprise during migration to a central master data hub 2. Define the critical success factors of a well crafted data quality strategy during migration 3. Provide insights into building the business case to ensure data quality is a priority 4. Recap Lessons Learned #3
  4. 4. What is Master Data Management? • “is application infrastructure (not a data warehouse, enterprise application, data integration or middleware), designed to manage master data and provide it to applications via business services. “ (1)‫‏‬ • Customers (and prospects)‫‏‬ • Products (new, current, obsolete) • Suppliers (prospective and current) • Future, Present and Past Employees, Contractors, Retirees • Research • Tangible Assets & IPR #4
  5. 5. Data Quality Is A Key Success Factor for Migration Applications Integrations Inventory Technology Standards Develop Integration Strategy Data Model Standards Data Quality Standards Integration Tools Survey Choose Tool Set Choose Reference Models Phase In Approach for Integration Inventory Install Integration Automation Tools As organization is able to support them Integration Platform Choose Dev/Support Model Integration Accountabilities Map to Reference Models Build & Depl oy Support & Maintenance Hire, Mentor, Train Staff across Enterprise Deploy Data Quality Standards Policies (Globally) While Managing Data Quality (Locally)þ #5
  6. 6. 1. Data Quality Issues & Migration • 1. 2. 3. 4. 5. 6. “Lack of cross-organizational communication and consultation has its consequences A lack of cross-organizational data governance structures, policymaking, risk calculation or data asset appreciation, causing a disconnect between business goals and IT programs. Governance policies are not linked to structured requirements gathering, forecasting and reporting. Risks are not addressed from a lifecycle perspective with common data repositories, policies, standards and calculation processes. Metadata and business glossaries are not used as to track data quality, bridge semantic differences and demonstrate the business value of data. Few technologies exist today to assess data values, calculate risk and support the human process of governing data usage in an enterprise. Controls, compliance and architecture are deployed before long-term consequences are modeled.(1)” #6
  7. 7. Master Data Quality - Problem • Lack of a clear mandate to change the current situation – No clear business accountability – Ownership vs stewardship – is it an IT issue only? • Lack of understanding of the issue on a global basis – Lack of a process to address the issues locally or globally • Merging the master data repositories adds a new level of complexity #7
  8. 8. Consistency Issues in DQM Practices • Agreeing to disagree – Supplier Name and Address standards different from Customer standards • Suppliers, employees and customers often have multiple contact roles, so ensuring cross-repository standards reduces the error correction costs • Product Names in local language – Global Product Listing managed locally and in each local language, when 98% of products were the same in every country • Global companies must set global language based standards, then act locally to enforce them • Conversion will clean it Up – Data conversion is not a panacea for data cleansing activities. • Leverage human expertise via local data cleansing activities, ad make them accountable #8
  9. 9. 2. Critical success factors for a Data Quality Strategy • Take a Global, Strategic Approach to Master Data Management and to Data Quality – Best Practices – Governance Roles and Responsibilities – Key Elements of a MDM Quality Program #9
  10. 10. 10 best Practices in MDM (4) 1 Ensure the active involvement by senior executives, appoint a Data Czar 2 The Business must own the stewardship of its own data throughout the MDM life cycle, not IT, and not just during the project 3 Any Change Management program must address the Nay Sayers 4 Tie financial and time investments to the end result, not just to the project outcomes 5 Develop programs that are easy to understand, implement and deploy with measurable results 6 Make Data Quality a full time job 7 A corporate Data Model is not just a pretty face … it shows where the bodies are buried 8 What really costs is customization … keep to the basics 9 Plan for at least one upgrade during the implementation 10 Test …Test …Test again # 10
  11. 11. What is Data Governance? • “Data governance is the orchestration of people, process and technology to enable an organization to leverage information as an enterprise asset. Data governance manages, safeguards, improves and protects organizational information. Effective data governance can enhance the quality, availability and integrity of your data by enabling crossorganizational collaboration and structured policymaking. “ (1)‫‏‬ # 11
  12. 12. Why is Data Governance Important? • Regulatory Compliance • Corporate Compliance • Data Quality – – – – – Data Cleansing Duplicates/replicate data merge Quality Checks Initial Load Coverage to include original, production, test, and archived data • Data Provenance and Change Management # 12
  13. 13. Data Management Roles Role > Group Data Quality Management Database Management CrossApplication Integration Information & Application Data Access Management Primary Process Owner RA C C RA RA Indirect Stakeholders CI I I CI CI Technology Service Group CI RA RA C I Fiduciary, Compliance Management ** CI I I I I Legend: R=Responsible; A=Accountable; C=Consult; I=Inform ** - Required for any repositories that have or provide a financial component # 13
  14. 14. Involvement in the DQM Process • The following groups must be involved: – The Business groups owning master data – The Compliance groups – Key users of the master data – Information Technology, including project team • Global Data Quality organization – A Senior Manager for Quality, Compliance, or similar – A Business Process Lead familiar with the data repository – An appointed Global Data Quality Lead for the master data repository – Local Data Quality teams must include key end users from key departments – Project support provides a data management Lead for best practices # 14
  15. 15. How do I build an MDM program? • • • • • Key Elements Critical Success Factors Process driven MDM Build MDM into daily operations Continuous Improvement programs and MDM # 15
  16. 16. A MDM Quality program • Define the MDM Quality Strategy – Estimate, formulate, and get approval, funding from senior management – Define Global Master Repositories and Standards for each • Establish and Build Global/Local Data Quality teams – Agree on approach and guidelines – Engage local teams in Data Quality Initiatives – Establish a Lean DQM cross-disciplinary team in each Locale • Define Master Data Quality projects and guidelines • Review project progress and results • Post results to Global Master Data Quality dashboard • Get IT support – Leverage the current legacy systems’ capabilities to enforce compliance – Consider Alerts, triggers where available to monitor post-clean up compliance – Dashboards and reports # 16
  17. 17. Data Quality Standards • Working Principles – People, Resources, Funding, Governance • Standards by Repository – Comprehensive, focused, automatable, simple to deploy • Establish a MDM Glossary # 17
  18. 18. Data Quality & Consistency Rules • Data Consistency Rules – – – – – – – – • Object Identifiers – external and internal Naming conventions for abbreviations, letter cases, suffixes, prefixes, etc. Special terms(glossary) Language differences Search criteria Date/time stamping across time zones Manual replication rules Data Cleansing resources – data content repositories, software, real-time DQM Duplicated data within a repository – Synonyms, short form names – Numbering • Replicated data across Repositories – Identifying global master reference base – Defining replication rules – Building synchronization protocols # 18
  19. 19. Data Quality Principles – Consistency • Naming and Numbering Conventions for Primary Identifiers, Proper Names and Searchable Descriptions • Classification and Code assignments are current and internally consistent – Accuracy • New or Obsolete Resources are approved by a manager • The Resource descriptive and control data are reviewed by a colleague • Run data quality check programs periodically – Timeliness • New Resources are added • Changes are approved quickly • Old Resources are made obsolete or disabled # 19
  20. 20. Data Quality Dashboard Data Quality by Type of Issue Dup/Rep/ Archive 7% Merge 20% Entry, Val'n 13% Coding 7% Obsolete 53% # 20
  21. 21. 3. Building the business case for Data Duality • Focus on the value of information as a key strategic investment • Develop a model with the 4 dimensions of Data Quality Programs – Consistency - Standards reduce errors – Timeliness - Time to Market Value, – Expertise – Knowledge, Multi-Lingual – the way to Global/Local Synergy – Risk – Compliance, Loss and Opportunity # 21
  22. 22. Cost Benefit Profiles • Reduced costs: – Errors cost time to correct, but also lost opportunity due to mis-matching, duplication, etc. • Increased revenues, market opportunities: – Increased integration of customer, products improves insights into buying habits though improved data mining • Reduced Inventory, time to market: – Increased integration of buying habits with supply chain data reduces waste, inventory, better timeto-market reaction times # 22
  23. 23. The Data Quality Impact Wave Investing Early reduces effort at time critical Go Live date Investing Late forces programmatic or manual intervention 2500 2500 2000 2000 Errors Effort Program Mods Days Available Tsunami 1500 1500 1000 1000 500 500 0 0 Design Build Test Go Live Design Build Test Go Live # 23
  24. 24. Lessons Learned 1. Make Data Quality a formal Key Success Factor of the overall project 2. Senior management must own and invest in the data stewardship role 3. Establish DQM Leadership and teams, leverage Six Sigma and related BPI soft technologies to improve data quality processes 4. Build Data Quality Standards across organizational boundaries 5. This is NOT a technology problem, so do not ‘automate a mess’ 6. Leverage Data Quality Management technology to clean and standardize key data repositories # 24
  25. 25. Next Steps – MDM Sessions with Customer focus Title Presenter Date & Time #1683 – Building a Practical Strategy for managing Data Quality Alexander Fiteni May 6, 2009 Fiteni International, L.L.C 11:00 AM – 12:00 PM #2762 - Rapid ROI with Oracle Master Data Management for Oracle E-Business Suite customers Pascal Laik May 6, 2009 Oracle 01:30 PM – 02:30 PM #2251 - Master Data Management for ERP Suites Bill Swanton May 6. 2009 AMR Research 03:15 PM – 04:15 PM #2911 – Re-Introducing Oracle Customer to an Organization, Customer Data Management Tanya Andghuladze May 6, 2009 Forsythe Technology 04:30 PM – 05:30 P)M #1499 – The lunatic, the lover & the poet Beyond Imagining Data Management How to Make Something of Nothing Brent Zionic May 7, 2009 Sun Microsystems 08:30 AM – 09:30 PM #1660 – Top 10 Mistakes Companies make in forming Enterprise Data Governance William McKnight May 7, 2009 Lucidity Consulting Group 09:45 AM – 10:45 PM #2378 – Customer Intelligence: Proactive Approaches to Cleanup and Maintaining Customer Master Data Rita Popp May 7, 2009 Jibe Consulting, Inc. 11:00 AM – 12:00 PM # 25
  26. 26. CDM SIG – To Become a Member Do one of • You can also join CDM SIG from OAUG site at http://www.oaug.com • Send a blank email to cdmsig-subscribe@yahoogroups.com • Go to CDMSIG Yahoo group at http://groups.yahoo.com/group/cdmsig and click on ‘Join this Group’: • Or send an email to mmanda@rhaptech.com expressing your interest in becoming CDMSIG member. You will receive membership application in reply. Upon sending the completed form to mmanda@rhaptech.com, your membership will be enabled. # 26
  27. 27. Q&A • • • • • • Alex Fiteni CMA alex@fiteni.com http://www.fiteni.com http://blog.fiteni.com Fiteni International LLC WHQ: – Suite 500, 3960 Howard Hughes Pkwy – Las Vegas, NV,USA 89169 • • • • Office: 702-990-3869 eFax: 603-590-2598 US Cell: 650-799-5949 CA Cell: 604-902-2782 # 27