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Geek Sync I The Importance of Data Model Change Management


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In today’s development environments, it is of critical importance to ensure that data models and databases are aligned to the user stories and tasks being created. Data architects must proactively collaborate with DBAs and designers, and take the initiative to track data model changes and correlate them against development and database updates.

Join IDERA and Joy Ruff in this webinar to learn about these trends and considerations for implementing model change management in your enterprise.

About Joy Ruff: Joy is the product marketing manager for ER/Studio, IDERA’s flagship data modeling and architecture platform, plus several database management and security products. With nearly 25 years of experience in high-tech hardware and software, Joy enjoys communicating product value to customers.

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Geek Sync I The Importance of Data Model Change Management

  1. 1. The Importance of Data Model Change Management March 8, 2017 Joy Ruff Product Marketing Manager
  2. 2. 2 Agenda  Enterprise data trends  Development methodologies  Communicating through data models  Considerations for change management  Sprint-based modeling activities  Summary  Q&A
  3. 3. 3 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.
  4. 4. 4 Evolving Database Ecosystems Volume, Velocity, Variety Keeping pace with the rapid growth of data, change and compliance Agile Development Cycles Maximizing IT Infrastructure ComplianceLimited Resources Data Professionals Need the Right Tools
  5. 5. 5 Waterfall vs Agile Data Modeling
  6. 6. 6 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?
  7. 7. 7
  8. 8. 8 Why we need data models: Much more than a picture  Full Specification • Logical • Physical  Descriptive metadata • Names • Definitions (data dictionary) • Notes  Implementation characteristics • Data types • Keys • Indexes • Views  Business rules • Relationships (referential constraints) • Value Restrictions (constraints)  Security classifications + rules  Governance metadata • Master Data Management classes • Data quality classifications • Retention policies
  9. 9. 9 Benefits of data modeling  Design • Manage redundancy • Integrate and rationalize • Increase quality  Use & Maintain • Increase discoverability • Improve comprehension • Data dictionaries • Business glossaries
  10. 10. 10 The Need for Common Understanding
  11. 11. 11 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
  12. 12. 12 Data model change management considerations  Needs to work with any workflow style – not just sprint-based  Fine-grained check-in and check-out capability  Method to associate model changes to requirements and list them in a change management control center  Audit trail of changes made – what was done and why, to demonstrate compliance for data governance  Ability to compare models to databases and other models, and identify changes that need to be merged into the source or target  Capability to create branches from a model baseline and merge them back in or roll back to restore a previous release  Ability to generate the necessary DDL code to implement the desired changes into the database
  13. 13. 13 Agile data modeling considerations  Primary focus is enablement of the team • Can not be perceived as an obstacle/gatekeeper  Iterative work style • Managing changes during sprints • Implementing database changes with DDL  Collaboration is paramount • Cross-project focus • Enterprise data perspective  Traceability – what changed and why • Data lineage can show change impacts • Audit reporting for data governance
  14. 14. 14 Managing changes during agile sprints
  15. 15. 15 Start of sprint preparation  Participate fully in sprint planning  Ensure there is a “Named Release” as of completion of previous sprint • Always have a baseline for compare/merge!  Submodels • Structure by relevant topic/subject area • At story level if necessary to facilitate communication • Roll up to parent level submodels
  16. 16. 16 In-sprint activities  Modeler fully engaged in daily stand-up meetings  Model change workflow • Model each change, associating with appropriate task/user story • Generate incremental DDL script(s) and post • Use a robust script naming convention, particularly if utilizing automated build systems  Different work approaches • Some designs will be originated “pushed” by data modeler • Others may be “pulled” from developer “sandbox” • Analyze, amend and “push” back out • Compare/merge and redesign as appropriate • Ensure developer uses the officially sanctioned script • Use submodels for audience specific perspective • Use data model design patterns  Maintain the discipline!
  17. 17. 17 End of sprint wrap-up  Create “Named Release” at end of Sprint • Serves as baseline for start of next sprint • Serves as baseline for comparison at ANY later point  Create delta DDL script by using compare/merge • Based on Named Release from end of the previous sprint  Create full database DDL script • Can be used to easily create “sandbox” databases quickly  Ensure the model(s) have been published  Participate fully in sprint planning and retrospectives • Lessons learned • Celebrate the successes
  18. 18. 18 Summary  Important factors for effective data model change management • Working style • Agile (sprint-based) or Waterfall • Collaborative rather than gatekeeper • Consistency in communication • Data dictionaries • Business glossaries • Traceability • Track why changes are made, not just what • Correlate with development changes • Implement model version control • Provides audit trail for compliance / governance
  19. 19. 19 Thanks! Any questions? You can find me at: @jfruff