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.
Geek Sync I The Importance of Data Model Change Management
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.
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?
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
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