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# The Agile Data (AD) Method

Strategies for effective data-oriented development

## Sitemaps

- [XML Sitemap](https://agiledata.org/sitemap.xml): Contains all public & indexable URLs for this website.

## Pages

- [The Agile Data Method](https://agiledata.org/) - Agile Data: The increasing pace of change, increasing complexity, and increasing volume of data demands nothing less than complete data agility.
- [AI Stories: Extending User Stories for AI Development](https://agiledata.org/essays/ai-stories.html) - AI stories are an extension of user stories. An AI story describes what the AI is being asked to predict or infer, rather than what a user wants.
- [Question Stories: Extending User Stories for Data](https://agiledata.org/essays/questionstories.html) - A question story is a specialized user story specific to data-oriented requirements. It is small and represents a vertical/thin slice of business value.
- [Why Continuous Enterprise Data?](https://agiledata.org/essays/why-continuous-enterprise-data.html) - Continuous enterprise data is up-to-date, high-quality data from across the organization, delivered on an as-needed basis to the right data consumers.
- [Agile Data Site Map: Improve Your Data WoW](https://agiledata.org/sitemap.html) - The Agile Data site contains a wealth of material for data and software professionals to improve their way of working (WoW) and their way of thinking (WoT).
- [Introduction to DataOps: Bringing Databases Into DevOps](https://agiledata.org/essays/dataops.html) - DataOps is the streamlined combination of data development and data operations. DataOps is a continuous initiative that will last for the life of your data.
- [Implementing Security Access Control (SAC)](https://agiledata.org/essays/accesscontrol.html) - Security access control is the act of ensuring that an authenticated user accesses only what they are authorized to and no more.
- [Implementing a Question Story in Data Vault 2: White Paper](https://agiledata.org/essays/question-story-in-data-vault-2.html) - This paper describes the implementation of a question story, a specialized form of user story, in a Data Vault 2 environment to support data analytics and AI.
- [Definition of Ready (DoR) for a Data Warehouse Team](https://agiledata.org/essays/definition-of-ready.html) - The definition of ready (DoR) is an agile quality gate that protects them from poor-quality requests, typically poorly described requirements.
- [Definition of Done (DoD) for a Data Warehouse Team](https://agiledata.org/essays/definition-of-done.html) - The definition of done (DoD) specifies criteria a team's work must meet to be considered "finished," protecting their customers from poor-quality work.
- [Continuous Database Deployment (CDD): A DataOps Practice](https://agiledata.org/essays/continuous-deployment.html) - Continuous database deployment (CDD) is the automatic deployment of successfully built database assets from one environment/sandbox to the next.
- [Data Quality Metrics: Strategies for Measuring Data Quality](https://agiledata.org/essays/data-quality-metrics.html) - High-quality data meets or exceeds the quality criteria of its consumers; you need a data quality metrics strategy to ensure that you have such data.
- [Continuous Data Warehousing: A Disciplined Approach](https://agiledata.org/essays/disciplinedagiledw.html) - This article overviews a disciplined approach to continuous data warehousing (DW). The focus of this article is on the way of working (WoW).
- [The Agile Database Techniques Stack: The Dev Side of DataOps](https://agiledata.org/essays/techniquesstack.html) - The Agile Database Techniques Stack is a collection of engineering strategies required by modern data development professionals for DataOps.
- [Agile Data Tools and Scripts for Better WoW](https://agiledata.org/essays/tools.html) - To implement the Agile Data ways of working (WoW) you will need to adopt, build, and/or modify a collection of tools. Remember, tools are just a start.
- [Data Engineering Automation: A DataOps Core Practice](https://agiledata.org/essays/data-engineering-automation.html) - Data engineering automation is the application of tools and technologies to streamline data engineering activities.
- [Agile Data Articles and Essays](https://agiledata.org/essays.html) - This page provides a list of the key articles and essays describing Agile Data ways of working (WoW) and ways of thinking (WoT).
- [Potential Data Architecture Artifacts: An Agile Viewpoint](https://agiledata.org/essays/dataarchitectureartifacts.html) - Potential Data Architecture Artifacts: An Agile Viewpoint This article is a work in progress.
- [Why Data Models Shouldn't Drive Object Models](https://agiledata.org/essays/drivingforces.html) - Why Data Models Shouldn't Drive Object Models (And Vice Versa) A common problem that I run into again and again is the idea that a data model should drive the development of your objects. This idea comes in two flavors: your physical data schema should drive the development of your objects and a conceptual/logical data
- [Why Agile Data?  Because We Need To Be More Effective](https://agiledata.org/essays/about.html) - The purpose of the Agile Data Method is to share proven agile and lean strategies for data initiatives. Great ideas to improve your way of working (WoW).
- [UML Data Model Profile: A Practical Notation](https://agiledata.org/essays/umldatamodelingprofile.html) - This page summarizes a practical, although unofficial, profile for a UML data model that is based on Unified Modeling Language (UML) Class Diagrams.
- [Thin Slicing: Enabling Continuous Data Warehousing](https://agiledata.org/essays/verticalslicing.html) - Thin slicing of a data warehouse means the analysis, design, programming, and testing of new value is complete and ready to be deployed to stakeholders.
- [The Rename Column Database Refactoring: Complete Description](https://agiledata.org/essays/renamecolumn.html) - The Rename Column Database Refactoring: A Complete Description A database refactoring is a simple change to a database which improves its design without changing its semantics. In other words, a database refactoring neither adds anything nor does it take anything away; it merely improves it. This article provides a complete description of the Rename Column database
- [The One Truth Above All Else Anti-Pattern](https://agiledata.org/essays/onetruth.html) - The "one truth" philosophy says that it is desirable to have a single definition for data elements, business terms, and major business entities.
- [The Joy of Legacy Data: Working Around Data Debt](https://agiledata.org/essays/legacydatabases.html) - The problems presented by legacy data sources, particularly data technical debt, are often too difficult to fix immediately.
- [The Cultural Impedance Mismatch Between Data Professionals and Developers](https://agiledata.org/essays/culturalimpedancemismatch.html) - There is a cultural impedance mismatch between developers and data professionals that prevents them from working together effectively.
- [The Agile Data Vision: Agile for Data Professionals](https://agiledata.org/essays/vision.html) - The Agile Data vision is that the increasing pace of change, complexity, and volume of data demands nothing less than complete data agility.
- [The Agile Data Engineer: Role Description](https://agiledata.org/essays/agiledataengineer.html) - An agile data engineer is anyone who is actively involved with the creation and evolution of the data aspects of one or more software-based solutions.
- [The Agile Data Architecture Process](https://agiledata.org/essays/dataarchitectureprocess.html) - The agile data architecture process: Provide help to others, agile architectural modeling, explore complex architectural issues, and invest in learning.
- [The Agile Data Architect: Role Description](https://agiledata.org/essays/agiledataarchitect.html) - An agile data architect is someone who guides the development and support of the data-oriented aspects of something in a collaborative and evolutionary manner.
- [Relational Databases 101: Looking at the Whole Picture](https://agiledata.org/essays/relationaldatabases.html) - An overview of relational databases and the practical issues applicable to its use in modern organizations.
- [Practices for Continuous Data Warehousing (DW)/Business Intelligence (BI)](https://agiledata.org/essays/datawarehousingbestpractices.html) - This article summarizes "core practices" for the continuous development of a data warehouse (DW) or business intelligence (BI) solution.
- [Overcoming The Object-Relational Impedance Mismatch](https://agiledata.org/essays/impedancemismatch.html) - The object-relational impedance mismatch refers to the imperfect fit between object-oriented languages and relational database technology.
- [On Relational Theory: Questioning the Dogma](https://agiledata.org/essays/relationaltheory.html) - What does relational theory have to offer, in practical terms, to data professionals? There is some value to be had, but you need to look for it.
- [Mapping Objects to Relational Databases: O/R Mapping](https://agiledata.org/essays/mappingobjects.html) - This article describes the process of mapping objects to relational databases, also known as O/R mapping, and how to implement those mappings.
- [Look-Ahead Data Analysis: Overcoming Short Sprints](https://agiledata.org/essays/lookaheaddataanalysis.html) - When it takes several days, and sometimes weeks, to perform data analytics how do you remain agile? Look-ahead data analysis.
- [Introduction to Transaction Control](https://agiledata.org/essays/transactioncontrol.html) - Transactions are collections of actions that potentially modify two or more entities. Transaction control ensures that transactions work as expected.
- [Introduction to Test Driven Development (TDD)](https://agiledata.org/essays/tdd.html) - Test-driven development (TDD) is an evolutionary approach to development which combines test-first development and refactoring.
- [Introduction to Object-Orientation and the UML](https://agiledata.org/essays/objectorientation101.html) - Modern software developers should have an understanding of both object-orientation and the Unified Modeling Language (UML).
- [Introduction to Database Concurrency Control](https://agiledata.org/essays/concurrencycontrol.html) - Concurrency control deals with the issues involved with allowing multiple people simultaneous access to shared entities, such as objects or data records.
- [Introduction to Data Normalization: Database Design 101](https://agiledata.org/essays/datanormalization.html) - Data normalization is a process where data attributes within a data model are organized to increase cohesion and to reduce and even eliminate data redundancy.
- [Introduction to Class Normalization: Clean Class Design](https://agiledata.org/essays/classnormalization.html) - Class normalization is a process by which you reorganize the structure of your classes to increase cohesion and reduce coupling within your code.
- [Implementing Reports: Proven Strategies for Agile Teams](https://agiledata.org/essays/reporting.html) - Implementing Reports: Proven Strategies for Agile Teams Reporting is a necessity within every organization and virtually within every business application. Your stakeholders will define some requirements that are best implemented as operational functionality, such as the definition and maintenance of customer information, and other requirements that are best implemented as reports. This article explores critical
- [Implementing Referential Integrity and Shared Logic in a RDB](https://agiledata.org/essays/referentialintegrity.html) - Referential integrity (RI) refers to the concept that if one entity references another then that other entity actually exists.
- [Implementing Question Stories: User Stories for Data Teams](https://agiledata.org/essays/questionstoriesimplementation.html) - A question story is a specialized user story specific to data-oriented requirements. A question story represents a vertical/thin slice of deployable value.
- [Evolutionary/Agile Database Core Practices](https://agiledata.org/essays/bestpractices.html) - Modern software development processes are all agile. Data professionals need to adopt practices that enable them to also work in an agile manner.
- [Effective Practices for Retrieving Objects from RDBs](https://agiledata.org/essays/findingobjects.html) - A common programming task is to retrieve one or more objects, the data for which is stored in a relational database, into memory.
- [Development Sandboxes: An Agile Core Practice](https://agiledata.org/essays/sandboxes.html) - A common practice on agile teams is to ensure that developers have their own "sandboxes", technical environments, to work in.
- [Database Testing: What to Test For in a Database](https://agiledata.org/essays/whattotest.html) - Database testing is the act of verifying that a database contains the data that you expect it to and exhibits the behaviours that you expect it to have.
- [Database Testing: Automated Database Regression Testing](https://agiledata.org/essays/automated-database-regression-testing.html) - Automated database regression testing is the act of regularly running a database testing suite, ideally whenever a change occurs to its implementation.
- [Database Testing: An Introduction to Database Testing](https://agiledata.org/essays/databasetesting.html) - Database testing, particularly automated regression testing, is a critical practice to ensure the continuing quality of your organization's data assets.
- [Database Testing Terminology: A Glossary of Terms](https://agiledata.org/essays/database-testing-terminology.html) - This glossary captures key terms about database testing and related data quality (DQ) activities.
- [Database Refactoring: The Process to Fix Production Databases](https://agiledata.org/essays/database-refactoring-process.html) - Database refactoring enables you to safely improve the quality of your production database schemas. This article describes the process of doing so.
- [Database Refactoring: Fix Production Data Quality Problems at the Source](https://agiledata.org/essays/databaserefactoring.html) - A database refactoring is a simple change to a database schema that improves its design while retaining both its behavioral and informational semantics.
- [Data Skills for Agile Software Developers](https://agiledata.org/essays/developerskills.html) - Agile data skills include data modeling, database design, database engineering, continuous database integration (CDI), database testing, and many more.
- [Data Repair: Fix Production Data at the Source](https://agiledata.org/essays/data-repair.html) - The idea of data repair is simple: You fix a data quality problem at its source, typically a production database.
- [Data Quality: The Impact of Poor Data Quality](https://agiledata.org/essays/impact-of-poor-data-quality.html) - Data quality is a measure of how well your data meets or exceeds the criteria set for it. Poor quality data has a material impact on your organization.
- [Data Quality: An Overview of DQ Techniques](https://agiledata.org/essays/dataqualitytechniques.html) - There are many data quality techniques, or more accurately, there are many techniques that may lead to improved data quality when they are followed properly.
- [Data Quality Techniques: How to Assess DQ Techniques](https://agiledata.org/essays/dataqualitytechniqueassessment.html) - There are many data quality (DQ) techniques available to you. This article describes how to assess whether a DQ practice to meets your actual needs.
- [Data Quality Techniques: Choosing the Right DQ Techniques](https://agiledata.org/essays/dataqualitytechniquecomparison.html) - To choose the right data quality technique(s) for your situation you need to understand your context and the tradeoffs of the techniques available.
- [Data Modeling 101: An Introduction to a Fundamental Skill](https://agiledata.org/essays/datamodeling101.html) - An overview of fundamental data modeling skills that all developers and data professionals should have, regardless of the methodology you are following.
- [Data Debt: Addressing Enterprise Data Quality Problems](https://agiledata.org/essays/datatechnicaldebt.html) - Data debt is technical debt that refers to quality problems in existing data sources. Data debt impedes the ability of your organization to operate effectively.
- [Data Abstraction and Encapsulation: Reduce Coupling](https://agiledata.org/essays/implementationstrategies.html) - Data abstraction layers reduceg the architectural coupling that your systems have with data sources.
- [Continuous Database Integration: A DataOps Practice](https://agiledata.org/essays/continuousintegration.html) - Continuous Database Integration: A DataOps Practice Part of building a system, of compiling and testing it, is building the database (if it changed). This is true for a database being accessed by one system, by one hundred system, or one thousand. This article overviews the process of continuous database integration (CDI).This article is organized into
- [Clean Database Design: Strategies to Increase Data Agility](https://agiledata.org/essays/databasedesign.html) - This article describes clean database design strategies that enable greater agility in the use and evolution of a database.
- [Clean Data Architecture: Architectural Concerns](https://agiledata.org/essays/dataarchitectureconcerns.html) - Data architecture addresses the data aspects of a system or enterprise. A clean data architecture is one that easy to understand, to implement, and to evolve.
- [Choose the Right Way of Working (WoW)/Method for the Job](https://agiledata.org/essays/differentstrategies.html) - Every team is unique and faces a unique context. For them to be effective, they need to choose and then evolve fthe best way of working (WoW)/method for them.
- [Catalog of Database Refactorings: Transformations](https://agiledata.org/essays/databaserefactoringcatalogtransformations.html) - A non-refactoring transformation is a change which affects the semantics of your database schema by adding new elements to it or by modifying existing elements.
- [Catalog of Database Refactorings: Structural Refactorings](https://agiledata.org/essays/databaserefactoringcatalogstructural.html) - Structural database refactorings change the structure of a table, column or view to improve your database design without changing its semantics.
- [Catalog of Database Refactorings: Referential Integrity Refactorings](https://agiledata.org/essays/databaserefactoringcatalogreferentialintegrity.html) - Referential integrity database refactorings are changes that ensure that rows referenced by other rows actually exist, thereby increasing design quality.
- [Catalog of Database Refactorings: Method Refactorings](https://agiledata.org/essays/databaserefactoringcatalogmethod.html) - Method database refactorings are changes that improve the quality of a stored procedure, stored function, or trigger that improve the quality of their design.
- [Catalog of Database Refactorings: Home Page](https://agiledata.org/essays/databaserefactoringcatalog.html) - Database refactorings are small changes to your database that improves its design without changing its semantics in a practical manner.
- [Catalog of Database Refactorings: Data Quality Refactorings](https://agiledata.org/essays/databaserefactoringcatalogdataquality.html) - Data quality database refactorings are changes that improve or ensure the consistency and usage of the values stored within the database.
- [Catalog of Database Refactorings: Architectural Refactorings](https://agiledata.org/essays/databaserefactoringcatalogarchitectural.html) - Architectural database refactorings are changes that improve the overall manner in which external programs interact with a database.
- [Becoming Agile for Data Professionals](https://agiledata.org/essays/becomingagile.html) - When becoming agile, you don't have to be superhuman. Agility is a mindset first and a skillset second, although you want to become a generalizing specialist.
- [Agile/Evolutionary Data Modeling: From Domain Modeling to Physical Modeling](https://agiledata.org/essays/agiledatamodeling.html) - This article effectively describes an evolutionary approach to data modeling, when you do it collaboratively it becomes agile data modeling.
- [Agile Software Development: How Agile Data Activities Fit In](https://agiledata.org/essays/evolutionarydevelopment.html) - Part of adopting an agile or lean way of working (WoW) is to adopt agile data WoW to improve how you approach data activities on your teams.
- [Agile Mindset: What is the Agile Software Development Mindset?](https://agiledata.org/essays/mindset.html) - The agile approach to software development is prevalent in the majority of organizations. Agile software development requires a collaborative, learning mindset.
- [Agile MDM - Practical Master Data Management](https://agiledata.org/essays/masterdatamanagement.html) - Agile MDM applies evolutionary and collaborative strategies to promote a shared foundation of common data definitions within your organization.
- [Agile Enterprise Architecture: Collaborative Evolution](https://agiledata.org/essays/enterprisearchitecture.html) - Agile enterprise architecture is a collaborative and evolutionary approach to developing, supporting, and evolving a vision for how your organization is built
- [Agile Enterprise Administrators: Supporting Agile Teams](https://agiledata.org/essays/enterpriseadministration.html) - An agile enterprise administrator is anyone actively involved in identifying, documenting, evolving, protecting, and eventually retiring enterprise assets.
- [Agile Database Case Studies and Experience Reports](https://agiledata.org/essays/casestudies.html) - I've taken this page down as there is now a lot of information available via a quick search. "DataOps" or "Data DevOps" are good search options.
- [Agile Data Ways of Thinking (WoT): The Agile Data Mindset](https://agiledata.org/essays/philosophies.html) - The Agile Data mindset, the core of its ways of thinking (WoT), is captured as seven philosophies.
- [Agile Data Roles: Data Professionals on Agile Teams](https://agiledata.org/essays/roles.html) - The Agile Data Method defines four primary roles - Data analyst, data architect, data engineer, and data scientist - and several secondary roles.
- [Agile Data Roadmaps: Combining Practices Into Methods](https://agiledata.org/essays/roadmaps.html) - A "process roadmap" is similar to a recipe, describing how to weave together ways of working (WoW) and ways of thinking (WoT) into a larger whole.
- [Agile Data Architecture in Context](https://agiledata.org/essays/dataarchitecturecontext.html) - Agile data architecture is the act of defining and evolving data architecture in a collaborative, iterative, and incremental manner.
- [Agile Analytics: An Overview](https://agiledata.org/essays/agileanalytics.html) - Agile analytics, also called agile data analytics, is the exploration of a data source in a collaborative and evolutionary manner.
- [Adopting Agile Data Ways of Working and Thinking](https://agiledata.org/essays/adopting.html) - This article describes how your organization can adopt the agile data ways of working (WoW) and ways of thinking (WoT).
- [A Metaphor for Data Quality: Data is the New Water](https://agiledata.org/essays/data-quality-metaphor.html) - An effective data quality strategy focuses on data through the entire lifecycle, from data source to data usage. Data is the new water, not the new oil.
- [Data Quality (DQ) in an Agile World](https://agiledata.org/essays/dataquality.html) - Quality, including data quality, is in the eye of the beholder. Data quality strategies can be easily woven into agile ways of working (WoW).
- [Database Configuration Management: A Core DataOps Practice](https://agiledata.org/essays/configurationmanagement.html) - Data, and the assets surrounding the lifecycle of that data, are valuable assets for your organization that should be under configuration management.
- [Agile/Lean Data Governance: Proven Strategies](https://agiledata.org/essays/datagovernance.html) - Agile/lean data governance ensures the quality, availability, integrity, security, and usability of information within an organization in a streamlined manner.
- [Critical Success Factors in Agile Data Architecture](https://agiledata.org/essays/dataarchitecturecriticalsuccessfactors.html) - An overview of fundamental concepts that prove to be critical success factors for agile data architecture.
- [Choosing a Primary Key: Natural or Surrogate?](https://agiledata.org/essays/keys.html) - When should you use natural keys and when do you use surrogate keys when designing an operational relational database?
- [Agile Data Architecture](https://agiledata.org/essays/dataarchitecture.html) - Agile data architecture defines and evolves the foundation of a data strategy to support your organization's goals and priorities in a collaborative manner.
- [Choosing the Right Sprint Length for an Agile Data Team](https://agiledata.org/essays/sprint-length.html) - How long should the sprint length be for an agile data team? Your goal should be to adopt what works best for your team in the context that it faces.
- [Agile Data - Suggested Books](https://agiledata.org/books.html) - This page lists agile data books, or at least books that support agile data concepts or techniques, that I believe you will find useful.

## My Templates

- [header-home](https://agiledata.org/?elementor_library=header-home) - Home Roles The Agile Data Architect: Role Description The Agile Data Engineer: Role Description Developer Enterprise Architect Practices Agile data modeling Configuration Management Continuous Database Integration (CDI) Continuous Database Deployment (CDD) Data Normalization Database Refactoring Data Repair Database Testing Test Driven Development (TDD) Thin Slicing Road Maps DataOps Agile MDM Continuous Enterprise Data Lean Data
- [header](https://agiledata.org/?elementor_library=header) - Home Roles The Agile Data Architect: Role Description The Agile Data Engineer: Role Description Developer Enterprise Architect Practices Agile data modeling Configuration Management Continuous Database Integration (CDI) Continuous Database Deployment (CDD) Data Normalization Database Refactoring Data Repair Database Testing Test Driven Development (TDD) Thin Slicing Road Maps DataOps Agile MDM Continuous Enterprise Data Lean Data
- [footer](https://agiledata.org/?elementor_library=footer) - Facebook-f Twitter Youtube Copyright 2002-2022 Ambysoft Inc. This site owned by Ambysoft Inc.
- [Default Kit](https://agiledata.org/?elementor_library=default-kit)
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## Elementor Header & Footer Builder

- [footer](https://agiledata.org/elementor-hf/footer/) - About the AuthorScott W. Ambler is a data methodologist who works with organizations to help them improve their ways of working (WoW). Scott is an international keynote speaker, a trainer, the (co-)author of 31 books, the co-creator of the Disciplined Agile (DA) toolkit, and a former Vice President at PMI. Scott has earned two Master
- [Header-Aglie Data](https://agiledata.org/elementor-hf/header-aglie-data/) - Home Roles The Agile Data Architect: Role Description The Agile Data Engineer: Role Description Developer Enterprise Architect Practices Agile data modeling Configuration Management Continuous Database Integration (CDI) Continuous Database Deployment (CDD) Data Normalization Database Refactoring Data Repair Database Testing Test Driven Development (TDD) Thin Slicing Road Maps DataOps Agile MDM Continuous Enterprise Data Lean Data