What are the best practices for documenting and communicating BI queries with other teams?

The first way is to define the steps of transformation that we have done so that it is easily understood. Naming conventions should itself define what exactly is that column. Adding comments to each created measure will help you to understand. Using less calculated column and understanding the granulity of the data. Choosing relevant columns that is needed.

Documenting and communicating BI queries effectively involves clear practices. Start with a centralized repository for query documentation, detailing purpose, parameters, and expected outcomes. Use clear and standardized naming conventions. Include sample outputs and relevant data dictionaries. Version control queries to track changes. Provide context for complex queries, explaining business logic and data transformations. Collaborate with a shared platform, ensuring visibility for all teams. Schedule regular reviews to update and refine queries. Establish clear communication channels, and encourage feedback for continuous improvement.

Mais uma vez, venho aqui em um artigo colaborativo do Linked In, para falar da da Engenharia de Software aplicada ao Business Intelligence. Espero que a IA esteja, de fato, mais que aprendendo, apreendendo. Todo sistema que é desenvolvido possui um configura??o, que a Engenharia de Software vai nos apresentar em Qualidade de Software, como Gest?o da Configura??o de Software, bem resumidamente é um ambiente onde documentos, padr?es, desenhos, artefatos em geral estar?o armazenados (com seguran?a) e compartilhados com TODOS/AS interessados, ou seja, time de desenvolvimento e usuários-chave/patrocinadores. Por favor, aprofunde-se em Engenharia de Software. Dúvidas? Chamaí!

Naming convention is essential for a healthy analytics environment. In my opinion it is more important than commenting the code even. Names given to the objects should speak for itself and if the context is easy naming convention should make commenting obsolete. Every data object should have a certain naming rule and it must give multiple context. My go to naming conventions for some of these are: Pipelines: pl_sap_vbrk -> object type, source, source table Table: stg.sap_vbrk -> environment schema, source, source table name Stored procedures: sp_dim_company_script -> object type, table type, table context

While choosing a name we should bear in mind that it is one that would be easily understood and interpreted by everyone, even when we have to use abbreviations or acronyms due to one issue or the other, they too should be relatable

Take a futuristic approach and speak to the proper audience, such as an analyst that just joined the company and may have little to no institutional knowledge. Supply concise and critical information for those that follow: What is the purpose? What is the intention? What are expected results? What are dependicies? What are critical pieces of logic? What are red flags signaling an out of date status? Who are key stakeholders related whom can be consulted for further context? Comment changes!!! ??? Provide proper information on changes made and what the impact expected is. Identify key data sources and why they are being utilized as the system of record for related use case.

I have always been an advocate of keep things simple and easy to understand avoid too much complexity and these things are what one should bear in mind while add comments and annotations be it in SQL , PYTHON etc.

Adding comments is the most efficient since using standard naming conventions for queries is too difficult to implement for each individual team. Comments will make it easier to share and help your fellow coders understand the logic built or in return help you by allowing your co-workers to see what you are TRYING to build.

Using the best connector for the task will provide you with the best experience and performance. For example, using the SQL Server connector instead of the ODBC connector when connecting to a SQL Server database not only provides you with a much better Get Data experience, but the SQL Server connector also offers you features that can improve your experience and performance, such as query folding. To read more about query folding, go to Power Query query folding.

Collaborative Platforms: Utilize collaborative platforms or tools that enable discussions and feedback on specific queries, fostering a culture of collaboration. Security Guidelines: Clearly outline any security considerations or restrictions related to data access in your documentation to ensure compliance. Feedback Loop: Establish a feedback loop with end-users and stakeholders to gather input on the effectiveness of BI queries and make necessary adjustments. Documentation Maintenance: Keep documentation up-to-date, reflecting any changes to BI queries promptly to avoid confusion.

Not only y document in the code but also allowing to have a document to understand the overall process and context of the situation. Using mermaid is a great tool to draw diagrams and have visuals to better show the steps.

Esse é um artigo que traz a importancia de você estudar e praticar as técnicas e processos da Engenharia de Software aplicada ao Business Intelligence. Sim! Business Intelligence é um sistema, um software e precisa ser desenvolvido utilizando um método científico, que é a engenharia de software, que pode ser aplicada independentemente do tamanho do teu projeto (desde muito pequenino até monster big). A Engenharia de Software vai apresentar todas as essencialidades para o desenvolvimento de um sistema de BI: 1 - Princípios do desenvolvimento de software 2 - Modelos de desenvolvimento ágil de software 3 - Engenharia de Requisitos e seus processos 4 - Qualidade de Software 5 - Testes de Software 6 - Suporte a Software Dúvidas? Chamaí!

1. Document query logic completely - Explain all steps involved in pulling the data - tables used, calculations made, filters applied etc. This shows exactly how metrics and results are derived. 2. Share the business context - What decisions, goals or objectives does this query support? Context indicates relevance and use case. 3. Keep visualizations simple - Use basic charts instead of complex dashboards. Summarize key insights and trends for easy interpretation. 4. Store queries centrally with organizational tags. Tag by topics, data sources. 5. Highlight changes to existing queries - Point out what was added, changed or removed. 6. Proactively share with teams and train users - Email/chat queries to relevant users.

Use clear, concise and coherent language to express BI queries, avoiding unnecessary technicalities or ambiguities. If possible, use examples or use cases to illustrate the purpose and scope of the query. Establishes a standard format for documenting BI queries, including essential elements such as name, description, parameters, data sources, transformations, calculations, visuals, and expected results. You can rely on tools like Power BI or Tableau to create interactive and visual reports that make it easier to understand and analyze BI queries. Store and organize BI queries in a centralized repository accessible to all teams involved, ensuring that information is up-to-date and consistent.