Geek Sync I Agile Data Management vs. Agile Data Modeling
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The document discusses agile data modeling, emphasizing that agile is a mindset rather than a technique, with a focus on faster time to market versus thorough analysis. It outlines the principles of agile frameworks like Scrum and Kanban, highlighting the significance of collaboration, adaptation, and continuous improvement in data management and modeling practices. The summary concludes with the importance of matching frameworks to specific problem domains while addressing misconceptions surrounding agile methodologies.
Geek Sync I Agile Data Management vs. Agile Data Modeling
- 1. Agile Data Modeling July 13, 2016
- 2. Agenda Agile Data Modeling The Controversy Misconceptions Applying Discipline to Agile 3 1 2 4 1 Methodology vs. Mindset-
- 3. About me… • United States Air Force – Late 1980’s – Data Analyst • DoD Contractor – Early 1990’s – Birth of the Relational Model • Data Modeling (and tool) Instructor • DW/BI Practice Director • Founding/Managing Partner – Agilarc LLC
- 4. About Agilarc… • Professional Services Consulting Firm specializing in • Enterprise Architecture • Data Management • Cloud Services • Analytics and Business Intelligence • Agile Services • Building agility into your best practice driven architecture is at the heart of what we do. • Managing Partners with over 50+ years of combined Data Management experience
- 5. The Controversy 2 • Conflicting Goals – Faster time to market • Versus… – Thorough Analysis prior to coding – Pros and Cons of each • Code refactoring expensive • Analysis Paralysis
- 6. Misconceptions… • Agile is a technique (not) – Agile is a mindset – Supported by multiple frameworks/techniques • Why we model data – Business Rules vs. Business Questions – Different reasons based on perspective • Agile framework aligned with DM rationale – Here’s were things go off the rails
- 7. Why we model data… • Application Development – Shrink wrapped software • Multiple business rules – Supporting internal business processes • Organizational business rules • Data Warehousing • Analytics • Master Data Management
- 8. Agile Defined… Agile|adjective * • marked by ready ability to move with quick, easy grace <an agile dancer> • having a quick, resourceful, and adaptable character <an agile mind> Antonyms awkward, clumsy, gawky, graceless, klutzy, lumbering, ungainly, ungraceful *Merriam-Webster © 2016
- 9. Agile’s Foundational Values • People come first, before product and before process – Personal commitment must be made between everyone on the team – Without the team, there is no product • Adaptation – Change…of mind, understanding, circumstance, is inevitable, and essential – Be ready, willing, and able to respond to changes worth responding to – Changes may apply to people, process, or product • Early and frequent value delivery – The feedback loop is more important than specs on paper – Focus relentlessly on doing valuable work and ignoring all else – Customers seeing early and frequent ROI are more engaged and enthusiastic • Customer collaboration – Delighting customers requires frequent collaboration – Spirit of partnership, not vendor-buyer or winner-loser
- 10. The Agile Manifesto… • Applying the Mind-Set to Software Development
- 11. Mindset vs. Implementation Frameworks • Agile is a mind-set • Frameworks are the manifestation of the mind- set • Understanding the mind-set first facilitates success with the frameworks
- 12. Agile Frameworks - Scrum • Core concepts – Empiricism – Iteration – Emergence – Self-organization – Prioritization – Time-boxing – Delaying detail
- 13. Defined Process vs. Empirical Process Defined process - Command and control - Plan what you expect to happen - Enforce the plan, sometimes regardless of changing conditions - Use change control, because change is expensive Empirical process - Learn as we proceed - Expect and embrace change - Inspect & Adapt: iterative improvements - Estimates are usually wrong. Don’t commit to them
- 14. Iteration Multiplies Feedback Project Timeline 1 2 3 Release 1 4 5 6
- 15. Emergence Defeats Complexity Cone of Uncertainty
- 16. Agile Frameworks - Kanban • kanban defined (small k vs. big K) – A Japanese word that means “signal card” – A manufacturing process that tells an upstream process to produce more – Upstream cannot do work until they get the signal that the downstream is done (limiting WIP) – Facilitates JIT automation and kaizen (continuous improvement) – Semi-prescriptive scheduling system of artifacts, concepts, and processes for managing the supply chain and providing inventory control, or managing tasks and change…requires that some process is already in place – Formalized by Microsoft in the early 2000s as a product development method
- 17. Simple Example MoMA Admission System • Capacity is limited (bandwidth) • Line is continuous (backlog) • Cards are created to represent upstream capacity • Every patron is provided a card needed to enter • When the patron is finished, the card is dropped off, freeing up another spot for a new patron (signal mechanism) • When there are no free cards, no more entry is permitted (limiting WIP)
- 18. What’s a “Pull” System? • Find a systematic way to achieve sustainable pace of work • Find an approach to introducing process changes that would meet little resistance and facilitate cultural evolution • Avoid employee burnout, increase morale, encourage empowerment and process refinement (Culture Change!) • Upstream “pulls” from downstream only when bandwidth is available, rather than pushing from source with no regard for upstream availability • Limits WIP, balances demand on the team • Only workers at the bottleneck remain fully loaded; everyone else experiences slack Why?
- 19. Kanban Board Development Backlog Plan Develop Test 2 3 5 User Story Defect Task Inprogress Done Done Done Inprogress Inprogress Done Rules: Plan: hours assigned, test case agreed Develop: Coding done, increment in staging ready for review with staging link published Test: Test case completed and verified by Product Owner
- 20. The Kanban Board Visualize the Workflow Backlog Plan Develop Test Done Rules: Plan: hours assigned, test case agreed Develop: Coding done, increment in staging ready for review with staging link published Test: Test case completed and verified by Product Owner 5 3 5 User Story Defect Task Bottleneck Under-utilized Idle Slack Lead Time
- 21. WIP Limits Backlog Plan Develop Test Done Rules: Plan: hours assigned, test case agreed Develop: Coding done, increment in staging ready for review with staging link published Test: Test case completed and verified by Product Owner 3 9 1 User Story Defect Task
- 22. Core Concepts & Principles • Start now, with what you have • Target incremental change • Current processes, people, titles, roles • Every team member is a leader • Limit WIP • Visualize the workflow • Release frequently • Identify work types • Improve continuously • Process visibility • Develop sustainable pace • Create slack Stop starting…start finishing
- 23. Scrum vs. Kanban Which is “Better?” Kanban - Continuous development - “Support” based - Ongoing timeframe - Lead-time-based - Specialized teams are okay - WIP limited directly - Shared backlog by multiple teams - Less prescriptive Scrum - Iterative development - “Product” based - Discrete timeframe - Velocity-based - XF teams - WIP limited by time-box - Backlog owned by one team - More prescriptive Bottom line: The team is more important than the process. Like-minded thinkers that believe in the Agile mind-set can make either work in any situation.
- 24. Data Modeling and Data Management • Data modeling equals Emergence – Example: resolving a many-to-many relationship – Doctors see many Patients, Patients see many Doctors • Adaptation in the world of Business Rules – Driving consensus in a data model – The importance of meta data and taxonomy • Early and frequent value delivery – Value vs. features
- 25. Data Management • Data modeling as a component of Data Management • Data Management “engagement” types – Master Data Management – Data Warehousing – Enterprise Data Model Development – Meta Data Management – Data Governance and Stewardship • Which Framework works best – Is the team fully dedicated? – Is there a discrete timeframe? – Dedicated Product Owner? – Will we be fixing defects? – Is work in progress an issue? – Bottleneck visibility…
- 26. Framework Mapping Domain Agile Framework Shrink Wrapped Software Development Scrum Master Data Management Kanban Data Warehousing Kanban Internal Application Development Scrum/Kanban Data Governance Kanban Analytics and Reporting Kanban Enterprise Data Modeling Scrum
- 27. Summary… • Misconceptions lead to Controversy – Agile a much misunderstood concept • Agile is a mindset • Data modeling with a purpose • Data modeling is a component of Data Management • Frameworks should match problem domain
- 28. Questions… • Joseph Maggi [email protected] 412.335.3027 Service Offerings: • BIDW Architecture Review/Strategy • Custom Development • Data Modeling/Governance • Daily / Multi-week Scrum Workshops • Scrum Coaching • Process Auditing
- 29. IDERA Download any of our tools free for 14 days at IDERA.com Join the conversation at community.idera.com