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Geek Sync I Does Data Modeling Have Business Value?


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Data modeling is an essential component of enterprise architecture that is often overlooked or underestimated in importance. Whether dealing with a single database or a combination of multiple platforms in a complex environment, data modeling provides meaning and value to the business and can serve as the foundation for a data governance or master data management initiative.

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Geek Sync I Does Data Modeling Have Business Value?

  1. 1. Does Data Modeling Have Business Value? October 13, 2016 Joy Ruff Product Marketing Manager
  2. 2. 2 Agenda  The value of enterprise data  Data trends  Communication gaps  Common understanding
  3. 3. 3 Value of Enterprise Data “Business executives and IT managers are increasingly referring to information as one of their organization’s most critical and strategic corporate assets. Certainly there is a sharp rise in enterprises leveraging information as a performance fuel, competitive weaponry and a relationship adhesive.”
  4. 4. 4 Keeping pace with the rapid growth of data, change and compliance Evolving Database Ecosystems Volume, Velocity, Variety Agile Development Cycles Maximizing IT Infrastructure ComplianceLimited Resources Database Professionals Need the Right Tools
  5. 5. 5 Enterprise Data Trends Increasing volumes, velocity, and variety of Enterprise Data 30% - 50% year/year growth Decreasing % of enterprise data which is effectively utilized 5% of all Enterprise data fully utilized Increased risk from data misunderstanding and non-compliance $600bn/annual cost for data clean-up in U.S.
  6. 6. 6 Business Stakeholders’ Data Usage Suspect that business stakeholders INTERPRET DATA INCORRECTLY Yes, frequently 14% Yes, occasionally 67% No, never 9% I don’t know 10% Suspect that business stakeholders make decisions USING THE WRONG DATA? Yes, frequently 11% Yes, occasionally 64% No, never 13% I don’t know 12%
  7. 7. 7 Enterprise Architecture Framework Enterprise Enablement BusinessArchitecture ApplicationArchitecture TechnicalArchitecture Data Architecture
  8. 8. 8 Complex Data Landscape  Proliferation of disparate systems  ERP, mismatched departmental solutions  SAAS (externally controlled and managed), cloud  Obsolete legacy systems  Poor decommissioning strategy  Point-to-point interfaces  Data warehouse, data marts, ETL …
  9. 9. 9 Usability: Design –> Metadata  Discovery • Where do I find Customer information?  Comprehension • What does it mean? Is “11” good or bad?  Compliance • Can I share this part of the Customer record?
  10. 10. 10 Data Model Usage & Understanding 13% 3% 16% 19% 31% 18% 0% 5% 10% 15% 20% 25% 30% 35% We don’t use data models Other Our data team does most data models but developers also build… Our database administrators own data modeling Developers develop their own data models We have a data modeling team that is responsible for data models Completely understand 20% Understand somewhat 60% Don’t understand 17% I don’t know 3% 87% What is your organization’s approach to data modeling? How well does your organization’s technology leadership team understand the value of using data models?
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  12. 12. 12 Creating Value with Data Architecture & Modeling  Build • Manage redundancy • Integrate and rationalize • Increase quality  Use & Maintain • Increase discoverability • Improve comprehension
  13. 13. 13 Types of Models  Conceptual • Technology-neutral, high-level layout of entities and their relationships • Used to establish contextual consensus among modeling domain stakeholders • Usually owned by information workers  Logical • Adds detail to conceptual models in a technology-neutral rendering • More context on the entity relationships, including terms and definitions • Used by data architects and application developers  Physical • Tied to a particular database implementation • Includes implementation-level details such as indexing and federation • Usually created and maintained by systems architects and administrators
  14. 14. 14 Addressing Complexity through Models  Reverse engineering: wide variety of platforms including Big Data  Multi-level sub-models: allow business decomposition  What and where? • Naming standards • Universal mappings  Document and define • Metadata extensions (attachments) • Business glossaries  Data in context: business processes  Data lineage  Repository, collaboration & publishing
  15. 15. 15 The Need for Common Understanding
  16. 16. 16 Apply Meaning with Business Glossaries  Maximize understanding of the core business concepts and terminology of the organization  Minimize misuse of data due to inaccurate understanding of the business concepts and terms  Improve alignment of the business organization with the technology assets (and technology organization)  Maximize the accuracy of the results to searches for business concepts, and associated knowledge
  17. 17. 17 Functional Comparison of Excel & Visio Microsoft Excel and Visio Professional Data Modeling Tool Not intuitive as data modeling tools – not for this function Extremely intuitive and easy to use as a data modeling tool, for new users and very data management professionals No rules to guide the user through a modeling process, and no error handling Provides many integrated tools for rule-based model development, error handling, logical and physical data modeling Provide mostly static pictures of data models – no code-generation capabilities and no reporting Includes interactive capabilities for the of sophisticated, complex data models No support for data dictionary or the ability to re- data models – extremely difficult to develop in a consistent fashion Users can easily share, re-use, re-purpose, and reverse engineer data models, improving data and compliance through standardization and collaboration
  18. 18. 18 Justifying the Value Investment  Benefits • Consistency and standardization • Improved data quality • Reduced operating costs  Results • Greater reuse of assets • More informed decision-making
  19. 19. 19 Summary  Enterprise architecture is vital to the business • Defines alignment with business strategy • Builds on a data architecture foundation • Drives business value from IT solutions and assets • Brings order from chaos  A robust and comprehensive data architecture and modeling program is the foundation for: • Compliance • Data governance • Master data management • Business intelligence and analysis Enterprise Enablement Business Architecture Application Architecture Technical Architecture Data Architecture
  20. 20. 20 Thanks! Any questions? You can find me at: @jfruff
  21. 21. 21 ER/Studio Enterprise Team Edition  Collaborate to capture corporate knowledge and establish consistent business terms  Provide access to models and metadata across the organization  Automate and document model deployments Collaboration, coordination and automation for high-performance teams
  22. 22. 22 Enterprise Collaboration: Share Metadata Across Business & IT