Disciplined Agile

Views Within the Disciplined Agile® Tool Kit

Architectural views are themes that cut across all layers of the Disciplined Agile® (DA™) tool kit. There are four views, as you see in Figure 1.

DA Architecture Views

Figure 1. The four architectural views of the DA tool kit (click to enlarge) 

In order of importance, the architectural views of the DA tool kit are:

1. Mindset. The DA mindset captures the DA “ways of thinking,” and is described via principles, promises, guidelines, and philosophies. All disciplined agilists believe in the principles, so we promise to adopt these behaviors and follow these guidelines when doing so. Furthermore, each process blade extends the DA mindset with philosophies that are specific to that process blade, as you see in Figure 2 for data management. 

Data Management Mindset

Figure 2. The Disciplined Agile (DA) mindset for data management (click to enlarge)

2. People. The people view addresses the issue of “Who are you and how are you organized?”  This view is captured via roles and responsibilities, team structures, and organization structures.

3. Flow. The flow view addresses the issue of “How do you work together?”  Flows, also called workflows or process flows, are captured as life cycles or workflow diagrams.  Figure 3 depicts the Exploratory life cycle and Figure 4 the internal process flow for the data management process blade.

LifeCycleExploratory

Figure 3. DAD’s Exploratory (Lean Startup) life cycle (click to enlarge)

Internal

Figure 4. Internal workflow for Data Management (click to enlarge)

4. Practices. This view addresses the issue of “What do you do?”  There are thousands of potential strategies and practices, techniques, that you may choose to adopt, and people should strive to choose the most appropriate techniques given the context of the situation that they face. To help guide you through these decisions, the DA tool kit provides goal diagrams that enable you to easily navigate your choices.

Copyright Project Management Institute All Rights Reserved Data Management v5.6 Consolidate Data DataVault2Inmon data warehouseKimball data warehouseData lake (managed)Data lake/swampLegacy data sources Provide Intelligence Self-service business intelligence (SSBI)Automated dashboardsBusiness intelligence (BI)Tailorable reportsStatic reports Ensure Data Security AccountabilityData encryptionLeast accessPhysical data securityPrivacy access control Analyse Data Artificial intelligence (AI)Big data analyticsExploratory data analysisMachine learningMarket analyticsPredictive analyticsStructured data analyticsVisualize data Evolve Data Assets Master dataMeta dataReference dataTest data Specify Data Assets Executable specificationsPhysical data model (PDM)Enterprise conceptual modelEnterprise data model (EDM)Logical data model (LDM) Improve Data Quality Database refactoringContinuous database integrationTest-driven database development (TDDD)Database regression testingMeta data management (MDM)Data cleaningExtract Transform Load (ETL) Refactor Legacy Data Sources Refactor data sourceAnnounce refactoringMonitor deprecated schemaRemove deprecated schema Govern Data Data stewardshipDevelop data metricsDevelop data guidanceTrack progress

Figure 5. The process goal diagram for data management (click to enlarge).