You can watch the replay for this Geek Sync webcast in the IDERA Resource Center: http://ow.ly/nuyN50A5dJi 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.

1. The Importance of Data
Model Change Management
March 8, 2017
Joy Ruff
Product Marketing Manager
Joyce.Ruff@idera.com

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
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
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
Waterfall vs Agile
Data Modeling

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

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
Benefits of data modeling
 Design
• Manage redundancy
• Integrate and rationalize
• Increase quality
 Use & Maintain
• Increase discoverability
• Improve comprehension
• Data dictionaries
• Business glossaries

10. 10
The Need for Common Understanding

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
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
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
Managing changes during agile sprints

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
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
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
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
Thanks!
Any questions?
You can find me at:
joyce.ruff@idera.com
@jfruff