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A Modern Approach to DI & MDM

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Was Big Data worth it? We were promised a data revolution when Big Data and Hadoop exploded onto the scene – but those technologies brought with them ungoverned, underexploited, complex environments that didn’t solve the analytical problems they were supposed to. All is not lost, however. This webcast explores three important things we’ve learned from Big Data that can be applied to every kind of data environment: modern approaches to data that exploit the flexibility and power of Big Data without losing the governance and management our businesses need.

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A Modern Approach to DI & MDM

  1. 1. PHIL BOWERMASTER
  2. 2. The WORLD TRANSFORMED
  3. 3. FAST FORWARD onthe WORLD TRANSFORMED
  4. 4. BIG DATA
  5. 5. “BIG DATA”
  6. 6. A MODERN APPROACH to DATAINTEGRATION & MASTERDATAMANAGEMENT
  7. 7. AMODERNAPPROACH TODIANDMDM • MappingData • BusinessvsTechies • DatainContext
  8. 8. JAKE FREIVALD
  9. 9. A Modern Approach to Data Integration and MDM Jake Freivald, Information Builders
  10. 10. Problems with Normal Data Integration Processes Data modeling. Too much time spent coping with slight changes in our business data Business/IT alignment. Data architects, DBAs, and others can’t communicate with businesspeople Processes. Too much detail lost by handing off responsibility for business data to different people
  11. 11. Problem: Data Modeling Too much time spent coping with slight changes in our business data Johann Sebastian Bach Given Middle Family = J.S. Bach
  12. 12. ChenYi Problem: Data Modeling Johann Sebastian Bach Given Middle Family = J.S. Bach = Chen Yi Too much time spent coping with slight changes in our business data
  13. 13. ChenYi Problem: Data Modeling Johann Sebastian Bach Ludwig van Beethoven Given Middle FamilyHon. = J.S. Bach = Chen Yi = L. van Beethoven Too much time spent coping with slight changes in our business data
  14. 14. ChenYi Problem: Data Modeling Johann Sebastian Bach Dmitri Dmitriyevich Shostakovich Ludwig van Beethoven Given Middle FamilyPatronymicHon. = J.S. Bach = Chen Yi = L. van Beethoven = D. Shostakovich Too much time spent coping with slight changes in our business data
  15. 15. ChenYi Problem: Data Modeling Johann Sebastian Bach Dmitri Shostakovich Ludwig van Beethoven Mohamed Mougi Muhammad Qasabgi Given Middle FamilyHon. Dmitriyevich el al Patronymic Art. = J.S. Bach = Chen Yi = L. van Beethoven = D. Shostakovich = M. el-Mougi = M. al-Qasabgi Now I can alphabetize and abbreviate correctly. Too much time spent coping with slight changes in our business data
  16. 16. Mougi = M. el-Mougi = M. al-Qasabgi Problem: Data Modeling Johann Sebastian Bach Given Middle FamilyHon. Dmitri ShostakovichDmitriyevich Mohamed el Muhammad Qasabgial Patronymic Art. Ludwig van Beethoven ChenYi = J.S. Bach = Chen Yi = L. van Beethoven = D. Shostakovich Repeated changes in operational systems’ row-and-column structures Too much time spent coping with slight changes in our business data
  17. 17. Problem: Data Modeling Ripple effects of changes in one system lead to changes in others Mougi Johann Sebastian Bach Given Middle FamilyHon Dmitri ShostakovichDmitriyevich Mohamed el Muhammad Qasabgial Patronymic Art Ludwig van Beethoven ChenYi Operational, designed for transactions
  18. 18. Problem: Data Modeling Ripple effects of changes in one system lead to changes in others Mougi Johann Sebastian Bach Given Middle FamilyHon Dmitri ShostakovichDmitriyevich Mohamed el Muhammad Qasabgial Patronymic Art Ludwig van Beethoven ChenYi Operational, designed for transactions Data warehouse, designed for abstractions Sebastian Middle Dmitriyevich Patronymic el al Art Hon van Mougi Bach Family Shostakovich Qasabgi Beethoven Chen Johann Given Dmitri Mohamed Muhammad Ludwig Yi
  19. 19. Problem: Data Modeling Ripple effects of changes in one system lead to changes in others Mougi Johann Sebastian Bach Given Middle FamilyHon Dmitri ShostakovichDmitriyevich Mohamed el Muhammad Qasabgial Patronymic Art Ludwig van Beethoven ChenYi Operational, designed for transactions Data warehouse, designed for abstractions Sebastian Middle Dmitriyevich Patronymic el al Art Hon van Mougi Bach Family Shostakovich Qasabgi Beethoven Chen Johann Given Dmitri Mohamed Muhammad Ludwig Yi Data mart, designed for analysis Mougi Bach Family Shostak ovich Qasabg i Beetho ven Chen Johann Given Dmitri Moha med Muha mmad Ludwig Yi Mougi Bach Shostak ovich Qasabg i Beetho ven Chen Johann Dmitri Moha med Muha mmad Ludwig Yi Mougi Bach Shostak ovich Qasabg i Beetho ven Chen Johann Dmitri Moha med Muha mmad Ludwig Yi Mougi Johann Sebastian Bach Given Middle FamilyHn Dmitri ShostakovichDmitriyevich Mohamed el Muhammad Qasabgial Patronymic Art Ludwig vn Beethoven ChenYi Sebastian Sebastian Sebastian el el el Dmitriyevich Dmitriyevich Dmitriyevich Dmitriyevich Mougi Johann Sebastian Bach Given Middle FamilyHn Dmitri ShostakovichDmitriyevich Mohamed el Muhammad Qasabgial Patronymic Art Ludwig vn Beethoven ChenYi Mougi Johann Sebastian Bach Given Middle Family Dmitri Shostakovich Mohamed Muhammad Qasabgi Ludwig Beethoven ChenYi Sebastian Mougi Johann Sebastian Bach Given Middle Family Dmitri Shostakovich Mohamed Muhammad Qasabgi Ludwig Beethoven ChenYi Sebastian Sebastian Sebastian Sebastian Sebastian
  20. 20. Problem: Business/IT Alignment Data people often can’t communicate with businesspeople Data architect thinks ▪ Model the data ▪ Govern the data ▪ Watch out for “quick fixes” IT: Gets it That modeling stuff we just talked about Business: Hates it Business thinks ▪ Modeling, metadata are hindrances ▪ Analytical tools best without governance ▪ IT slows them down
  21. 21. Problem: Processes Too much information lost by distributing responsibility for business data Cleansing occurs in transformation step: Different rules being fired Different tools and metadata being used by platform Loss of timestamps, context, before-and-after: No cross-platform auditability No comprehensive rollback, alternate history, what-if Operational application Data warehouse Cloud application Fa mi ly Transformation Cleansing Standardization Transformation Cleansing Standardization
  22. 22. Fa mi ly Fa mi ly How much time do we spend mapping one set of rows and columns to another? What We Learned from Big Data A modern solution: post-relational for data capture, transformation, subject-oriented storage (perhaps), and exchange, rich documents instead of relational models Operational application Data warehouse Analytics
  23. 23. How much time do we spend mapping one set of rows and columns to another? What We Learned from Big Data A modern solution: post-relational for data capture, transformation, subject-oriented storage (perhaps), and exchange, rich documents instead of relational models Operational application Data warehouse Analytics
  24. 24. Operational application Data warehouse Analytics What We Learned from Big Data A modern solution: ELT capture/integrate to capture data as it is, time-stamped apply trustworthy processes to it, subject-oriented and share it in trusted ways How much info do we lose by distributing ETL processes?
  25. 25. Operational application Analytics Data Capture/Transformation Hub Transformation Cleansing Standardization Application to business use cases What We Learned from Big Data How much info do we lose by distributing ETL processes? A modern solution: ELT capture/integrate to capture data as it is, time-stamped apply trustworthy processes to it, subject-oriented and share it in trusted ways
  26. 26. Modern Data Integration: The Omni-Gen Approach History We saw the problem, felt the pain • MDM was the starting point • Unifying data quality with MDM • Aligning business users with mastered subjects • Capturing transactional subjects in MDM store
  27. 27. Modern Data Integration: The Omni-Gen Approach Response We built software to make ourselves successful • Immediate capture in automatically generated data hub • Master data: business-user-oriented, subject-oriented • Rapid, integrated data quality rules • Mastered and transactional subjects • Rapid cycle times to keep the business engaged • Support and automatically apply best practices
  28. 28. Modern Data Integration: The Omni-Gen Approach Extending Value We built models that include customer and supplier Everything you get in Omni-Gen, plus • Pre-built models • Cross-model linking • Pre-built data quality and data governance rules • Pre-built match/merge rules • Immediate 360° core view, unlimited extensions • Supports different consumers with different, but trusted, data
  29. 29. Omni-Gen: More Value in Far Less Time 12-181-3 4-6 Project timeline, in months Traditional Data management tools Build-it-yourself development environment Omni-Gen Software solution with built-in best practices MDM, DQ, integration software with rules, automatically generated data vault, remediation portal, 360° viewer, history, data interfaces, APIs, and feeds Omnifor Persona Software solution with built-in best practices and complete master data models Data vault model, data onramps; MDM, data quality, and integration software; MDM and data quality rules, remediation portal, 360° viewer; Data interfaces, APIs, history, & feeds; Analytical foundation for dashboarding, advanced analytics, more.
  30. 30. Discussion

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