The document outlines the principles of disciplined agile data management as proposed by Scott Ambler, emphasizing the necessity for enterprise-class organizations to adopt an agile mindset for data processes. Key themes include collaboration, automation, and a shifting focus from traditional documentation to light-weight governance and quality strategies. The framework encourages organizations to invest in staff and automation infrastructure while prioritizing the evolution of data management practices.

1. Disciplined
Agile
Data
Management

2. Scott Ambler
• Helps enterprise-­class organizations around the
world to improve their processes and organization
structures
• Thought leader of:
– Agile Modeling (AM) method
– Agile Data (AD) method
• Co-­creator of the Disciplined Agile (DA) framework
• Author of 20+ books
• scott [at] scottambler.com
© Disciplined Agile Consortium 2
@scottwambler

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4. © Disciplined Agile Consortium 4
Agenda
• The Disciplined Agile Framework
• The Agile Data Mindset
• Disciplined Agile Data Management
• Parting Thoughts

5. Disciplined
Agile Enterprise
Disciplined
Agile IT
Disciplined
DevOps
DAD
The Disciplined Agile Framework
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http://www.disciplinedagiledelivery.com/the-­agile-­tractor-­engine-­analogy/
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6. The Disciplined Agile Framework
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7. An Agile Mindset
for Data Management
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8. Evolution over
Definition
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9. Holistic Organization Over Data Management
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10. Collaboration over Documentation
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11. Sufficiency over Perfection
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What we want
Where
Disciplined
Agilists
initially aim
Where
traditionalists
typically aim

12. Cross Functional Over Specialized Staff
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Specialized Staff
Cross-­Functional Staff

13. Automated Over Manual Processes
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Data Automation opportunities:
– Automated regression testing
– Data schema analysis
– Continuous database integration
– Continuous database deployment
– Operational database monitoring with
real-­time dashboards

14. Disciplined Agile Data Management
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15. The Data
Management
Process Blade
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16. Comparing Data Quality Strategies
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Long-­term payback,
difficult to keep updated
Transform logic little more than
a band-­aid over source problems
Ensures that future changes don’t
“break” previous implementations
Detailed executable specification
and validation
Reduces feedback cycle, thereby
minimizing cost of fixing problems
Safely address production problems

17. Evolve Data Quality Assets
• Keep this as light-­weight and collaborative as possible
• Test data management can be challenging:
– Privacy issues surrounding production data
– Some test data will need to be engineered
– Configuration management of test data
– Continuous database integration requires automated restoration of test
data
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18. Refactoring Legacy Data Sources
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Database refactoring
immediately addresses legacy
quality problems BUT requires
long-­term administration to
remove implementation
scaffolding

19. Data Security
• Security addresses a range of issues:
– Data security
– Facilities security
– Staff vetting
– Staff protection
– And more
• Implication: Data management should collaborate closely with
Security professionals
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20. Specifying Data Structures
• Modeling is effective at thinking through and communicating high-­
level ideas
– Fundamental lean concept is that models of complex things should be
very simple in nature, not detailed
• Tests, when automated and better yet written before the
implementation that they validate, are very effective as detailed
specifications
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21. Agile Data Governance
• Effective governance focuses on motivation and enablement, not on
policing the creation of artifacts
• Data governance is one of many aspects of IT Governance
• Agile strategies:
– Collaborative support of teams and stakeholders
– Light-­weight guidance
– Automated monitoring(tests, schema analysis)
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22. Agile Data Quality Strategies
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24. Shifting Your Data Management Mindset
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25. How to Adopt Agile Data Management Strategies
1. Expect (a lot) better
2. Surface your challenges
3. Invest in your staff
4. Hire agile coaches with deep experience in both agile and data
management
5. Invest in automation infrastructure
– Question your existing “data quality tools”
– Data is a critical component of your CI/CD pipeline
– DB testing and refactoring tools are proven in practice
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26. scott [at] scottambler.com
Twitter: @scottwambler
DisciplinedAgileConsortium.org
DisciplinedAgileDelivery.com
Thank You!
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27. Would You Like This Presented
to Your Organization?
Contact us at ScottAmbler.com
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28. Scott Ambler + Associates is the thought leader behind the Disciplined
Agile Delivery (DAD) framework and its application. We are a boutique
IT management consulting firm that advises organizations to be more
effective applying disciplined agile and lean processes within the
context of your business.
Our website is ScottAmbler.com
We can help
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29. Additional
Slides
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30. Values for Agile Data Management
• Evolution over definition
• Holistic organization over data management
• Sufficiency over perfection
• Collaboration over documentation
• Cross-­functional staff over specialized staff
• Automation over manual processes
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