How do you perform data validation testing in an agile and collaborative manner?

Data Completeness: Required Fields: Identify fields that must be populated for a record to be considered valid. Conditional Requirements: Define rules for fields that are required only under specific conditions (e.g., if Field A is "Yes," then Field B is required). Allowed Values: Specify valid values or ranges for each field (e.g., numeric, alphanumeric, date formats). Data Consistency: Duplicate Checks: Prevent duplicate records based on unique identifiers or combinations of fields. Cross-Field Validation: Ensure that values across related fields are consistent (e.g., "Start Date" should be before "End Date").

To ensure effective data validation testing, start by aligning criteria with business goals. Link validation criteria to business objectives, user needs, and data quality standards for accurate outcomes. Next, document and communicate these criteria clearly to all stakeholders, fostering shared understanding and agreement on testing goals. Finally, consider the entire data flow from source to destination when defining your criteria. Prepare and document all data sources, formats, transformations, and destinations involved. This holistic approach guarantees comprehensive testing coverage, enhancing data reliability and integrity. Following these steps establishes a robust framework for successful data validation.

In agile teamwork, it's crucial to clearly set rules for checking data (validation criteria). Work together with different teams, including those interested in the project, to decide on specific things to measure, like making sure the data is right, complete, and stays the same. Use simple stories and rules to help everyone understand what needs to be checked. Bring in tools that can automatically do some of the testing, so you get quick feedback. Keep talking with the teams and update the rules as needed. This way, you stay flexible and make sure your data checks match what the project needs.

Para um trabalho mais ágil, é crítico definir quais os pontos e dados a serem validados, assim como seus critério para a valida??o. Integrar demais setores da empresa onde os dados possuem importancia. Envolva os setores informando a importancia e o objetivo da valida??o dos dados, garantindo sinergia. E sempre atualize, conforme a necessidade, as regras, métodos e etc, para valida??o de dados, nem todos os dados s?o lidos de forma igual.

#1 is the most important factor - you can talk about all the tools and method all day, but if you can't define it/document it, there's nothing to input into your fancy tools and methods.

Data validation tools and frameworks are indispensable for ensuring data quality and integrity within modern data pipelines. They streamline the validation process by: Automating tasks: Handling repetitive validation checks, reducing manual effort. Standardizing procedures: Ensuring consistent validation across different data sources and formats. Integrating with data pipelines: Seamlessly incorporating validation within data workflows. Providing insights: Generating reports and metrics to identify data quality issues. Facilitating collaboration: Enabling teams to share validation rules and results.

Data Quality Tools: Open Source: Great Expectations, Apache Griffin, DataCleaner, Talend Open Studio Commercial: Informatica Data Quality, IBM InfoSphere Information Server, SAS Data Quality Test Automation Frameworks: Robot Framework: A generic keyword-driven automation framework for acceptance testing and robotic process automation (RPA). Selenium: For web application testing, including data validation on web forms and interfaces. JUnit/NUnit: For unit testing of data validation logic in code. Data Profiling Tools: Open Source: Pandas Profiling, DataProfiler Commercial: Talend Data Preparation, Trifacta Wrangler

Data validation testing in an agile and collaborative manner can be achieved by creating a collaborative environment of stakeholders, testers, and developers. Stakeholders should be involved in establishing acceptance criteria and test cases. Testers should be involved in creating automated test scripts to validate data, and developers should create code that verifies data accuracy. All parties involved should be able to review and approve the results of the tests and collaborate on how to address any issues that arise in order to ensure the accuracy of the data.

Unit Tests: Test individual components or functions of the software to ensure they handle data correctly. This includes validating input data, data transformations, calculations, and output data. Integration Tests: Test the interaction between different components or systems to ensure data flows correctly across interfaces and boundaries. End-to-End Tests: Test the entire data flow from input to output, simulating real-world scenarios and validating the final results against expected outcomes. Regression Tests: Re-run previously passed tests to ensure that new code changes haven't introduced any regressions or unintended side effects.

Implementing data validation tests at the outset of a project is paramount to ensuring data quality and project success. Early and frequent data validation is crucial for agile development. Identify Issues Early: Discover data quality problems as soon as possible, preventing downstream errors. Improve Data Quality: Proactive validation helps maintain data integrity and consistency. Accelerate Development: By catching data issues early, developers can focus on core functionalities without delays. Enhance Collaboration: Integrate data validation into the entire team's workflow, fostering shared responsibility for data quality. Mitigate Risks: Reduce the risk of application failures due to invalid data.

Requirements Gathering: Engage stakeholders (business users, data owners, product managers) from the beginning to gather their input on data validation criteria, business rules, and expected outcomes. Regular Reviews: Conduct regular reviews of test plans, test cases, and data validation results with stakeholders to ensure alignment and address any concerns or feedback. Feedback Loops: Establish feedback mechanisms (e.g., surveys, meetings, online forums) to encourage stakeholders to provide input on the effectiveness of data validation testing and suggest improvements.

Para uma valida??o efetiva, sempre integre e interaja com demais setores da empresa. Descreva as características do projeto de valida??o de dados, e como será feito. Apresente aos setores envolvidos, o plano para valida??o dos dados e como o resultado irá gerar bons frutos dentro da empresa e como será utilizado, trazendo maiores resultados. Lembrando sempre, de entender onde é como o resultado será utilizado por cada setor, cada equipe e cada colaborador. Aceitando sempre Insights e feedbacks das equipes envolvidas.

Sprint Retrospectives: Incorporate data validation test reviews into your sprint retrospectives. Discuss what worked well, what could be improved, and any challenges encountered during testing. Dedicated Review Sessions: Schedule regular review sessions specifically focused on data validation tests. This allows for a more in-depth analysis and discussion of test results and effectiveness. Involve Stakeholders: Invite stakeholders to participate in the review process to ensure tests align with evolving business needs and requirements.

Data validation is an ongoing process that requires continual attention. To ensure data integrity, it is imperative to regularly review and refine validation tests. Key components of this process include: Periodic Evaluation: Conduct scheduled assessments of test effectiveness and coverage. Adaptive Testing: Modify tests to align with evolving data structures and formats. Root Cause Analysis: Investigate test failures to identify underlying data quality issues. Collaborative Refinement: Involve data stakeholders in test review and improvement. Test Automation: Employ automation tools to enhance efficiency and accuracy. Prioritization: Focus on critical validation points to maximize test impact.

1. Define Acceptance Criteria: Clearly outline what correct data looks like for each task or feature, just like setting the rules for a game. 2. Automated Checks: Use tools and scripts to automatically test data, like having a robot helper check if your toys are in the right places. 3. Frequent Reviews:Regularly show the data to your teammates (like friends in a game) to make sure everyone agrees it's correct. This happens often, not just at the end. 4. Feedback Loops: Create quick ways for people to tell you if they spot a mistake, like a buzzer in a game. This helps fix issues right away. 5. Iterative Adjustments: Keep improving your data rules and checks as you go, making small changes often instead of waiting for a big update.

Um ciclo de PDCA (planejamento, desenvolvimento, checagem e a??o) é fundamental nesse sentido. Toda checagem de dados deve gerar planos de a??o para continuidade desse ciclo, tanto para os dados validados com sucesso (gerar planos de a??o de amplia??o da estratégia) quanto com erros (gerar planos de a??o de melhoria contínua). Esse tipo de controle possibilita aumentar o escopo dos testes e ampliar os cenários de atua??o em produ??o, muitas vezes gerando insights para novos produtos, novos públicos ou novas estratégias de mercado.

The most helpful thing I have ever run into in order to validate the actual data, not the code, but the actual data is to first write your analytical script in Excel VBA. Because VBA is just going to keep crashing until you have a complete understanding of the data. Because VBA keeps crashing it will force you to read the actual data, and in my experience when you start reading the data it becomes very easy to tell that your data is not valid. Once you can do something in VBA then you can transition into a programming language for data validation that's much more stable and has much more capability.

Data validation testing is not a solo activity, but a collaborative one. You need to work closely with your data validation team and stakeholders to ensure that your data validation tests are relevant, consistent, and comprehensive. You should also share your data validation results, feedback, and insights with your team and stakeholders, so that you can learn from each other and improve your data quality. You can use tools like Jira, Slack, or Trello to communicate, coordinate, and track your data validation activities.

Some of these tasks are longer running than a sprint (and we have three week sprints). We tend to built working groups for this, schedule standing meetings (say, monthly), then keep a checklist for story tasking when a specific change needs to be incorporated in a sprint effort. Each group keeps its own agenda in Trello, Kanban board or whatever is most helpful for them. This is true not only for managing our test data sets, our schemas, but also things like UX research and architecture. There's no reason they must be cut up into the procrustean bed of a sprint--nor ignored as part of the work of a completing a sprint.

To perform data validation testing in an agile and collaborative manner, leverage automated testing tools for quick iterations. Collaborate closely with cross-functional teams to define validation criteria and use continuous integration for real-time feedback. Regularly review and adapt validation processes to ensure alignment with evolving project requirements and maintain a collaborative environment.

Interesting approach and I definitely see the merit in it. Thanks for sharing. My experience has been lacking chunks of data and having it come from different sources in different formats. I review each as a piece, finding the common core but keeping the outlaying data in case I find it adds to the story at which point I can request as needed. My challenge is always getting all the data and matching format