What are the best practices for transforming data to meet different user roles and levels?

Channel your inner Sherlock Holmes and deduce the needs of your data users???♂??? Just like Holmes unravels mysteries, you must uncover who your users are, what they seek, and how they prefer to engage with data ?? Use your magnifying glass: interviews, surveys, or even a game of deduction—to gather clues about their data desires ?? Once you've cracked the case, you'll be transforming data with the precision of Sherlock, delivering insights tailored to each user's needs ?? Elementary, my dear BI-Watson! ???♂?

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- perform an analytical query on the user roles - run a workshop with the business to confirm which roles are pertinent still and which are obsolete and if any new roles need to be added to this list; - assign clear descriptions and rights to all the confirmed user roles - apply security i.e these rights to the roles - run a UAT testing to confirm if all has been implemented correctly;

Transforming data to meet the needs of different user roles and levels requires a thoughtful approach to ensure that the information provided is relevant, actionable, and tailored to the specific requirements of each audience, it's important to understand user needs, customizing data format according to their needs and providing relevant context. Lastly providing space for iteration and improvement.

Keep in mind that 99% of these users do not have data literacy. Try to avoid terms like "data", "KPIs", and "metrics" altogether. Keep it high-level and interview them about their business questions and what kind of business answers they need to make their business decisions on the daily.

Keep this step simple and start with pen and paper. A data model from scratch can be a daunting task. Break it down into separate departments or stakeholders first with whom you conducted interviews. There will be overlap and inefficiencies across them which you will optimize once you combine them in your final drafts. And don't forget to get started on your data dictionary at this point! It'll be a mere copy-and-paste doing both tasks together.

Devemos converter os dados a serem usados para um formato padr?o compatível com as ferramentas usadas pelos parceiros de negócio. Incluímos também, a qualidade desses dados, limpando, removendo duplitcatas e corrigindo erros que vem da fonte de dados e atrapalham na agilidade do processo.

Designing an e-ffective information model aligne-d to the business nee-ds is crucial for maximizing the analytical value extracte-d from your data transformation efforts. I've see-n BI projects struggle from the be-ginning because of flawed or unplanne-d data modeling. The key is taking a structure-d, standard-based approach like dimensional mode-ling with a focus on the specific questions and use-s you aim to enable. Work closely with subje-ct matter experts and stake-holders to deeply unde-rstand the entities, facts, attribute-s, hierarchies and relationships important to the- domain. From there, iterative-ly develop conceptual, logical and physical mode-ls, evolving from high-level abstract ide-as.

Designing a data model involves aligning structure with analytical needs and business logic ?? Three key models: conceptual, logical, and physical serve as blueprints ?? Standardization and scalability are ensured through these models ?? Data storytelling is facilitated, resonating with stakeholders ?? Informed decision-making drives business development ?? Dimensional modeling, especially star schemas, simplifies data relationships ?? Insights are easier to extract for analysts and understand for business users ??

A well-designed data model is crucial for effective BI as it serves as the blueprint for data organization and access. It's important to ensure that the model is flexible enough to accommodate future business changes and scalable to handle growing data volumes. Employing best practices such as normalization for reducing redundancy, and denormalization for improving query performance, can significantly enhance the efficiency of BI processes. Additionally, incorporating metadata and documentation within the data model aids users in understanding data sources and transformations.

This step seems premature at this point in time. Once you have your data model and can funnel data through it, you should look at the raw data first. To "ensure that your data is clean, consistent, and ready for analysis and reporting" requires analyzing your data quality, accuracy, and some very likely clean-up in your data sources before it even hits your data model in your database. Don't implement complex transformations for data issues that can be resolved at the source.

Devemos converter os dados para os formatos necessários, criando também novas variáveis derivadas, e consolide os dados conforme necessário para a integridade do mesmo. Análise Exploratória: Examine os dados para encontrar padr?es, tendências e anomalias que possam fornecer insights preliminares.

Impleme-nting strong data transformation steps is crucial for providing trustworthy and useful insights to data users. This is whe-re all the preparation put into re-quirements gathering and mode-ling pays off. I recommend taking a methodical, pipe-line-focused approach to structure your data cle-aning flows. Start with raw extraction and standardization from source systems using tools like- ETL platforms. Apply clear cleaning, validation and enrichme-nt rules to establish a solid combined data laye-r. Then allow for more advanced transformations and groupings aligne-d to your dimensional models. Continually examine- and monitor data quality, establishing automated testing and issue- solving processes.

- Data transformations are crucial in the Business Intelligence lifecycle. ?? - Tailor transformations to meet different user roles' needs. ???????? - Analysts need granular data, while executives require aggregated information. ?? - Balance normalization and denormalization in the data model. ?? - The way data is transformed affects the narrative and insights. ???? - Data storytelling influences business strategies and outcomes. ????

Be very clear with your stakeholders that this is not a solo task. You can profile and audit all you want. If you deal with data from systems your stakeholders are expert frontend users in you will require their input and feedback on what you see come through the backend.

Checking data re-sults is the final crucial step before- sharing trusted insights with decision makers. Eve-n with robust early data joining and changing processes, thorough che-cking is needed to make- sure info is correct, steady, and suitable- for its purpose. I suggest checking from diffe-rent sides including technical data quality rule-s, business logic tests, and user acce-ptance standards. On the technical side-, implement careful profiling and Data Quality Se-rvices rules checking for comple-teness, following the rule-s, duplication, basic constraints and more. Automate as much of this as possible. Busine-ss logic checking requires close- teamwork with subject matter e-xperts looking over results against the-ir area knowledge.

Defini??o de Critérios: regras claras para o que constitui dados válidos. Implementa??o de Controles de Valida??o: Isso pode incluir verifica??es de tipo de dados, intervalos de valores, e consistência lógica. Testes Automatizados: Desenvolva testes automatizados que possam ser executados regularmente Revis?o Manual: Em alguns casos, uma revis?o manual pode ser necessária para validar dados que n?o podem ser verificados automaticamente ou para resolver ambiguidades. Documenta??o: Mantenha uma documenta??o detalhada dos critérios de valida??o para referência futura e auditorias. Feedback e Melhoria Contínua: sempre olhe o retorno do seu modelo, em caso de dados incoerentes, ajuste o modelo e pense sempre em melhoria contínua.

Indexa??o Eficiente: Crie índices nas colunas em consultas, para acelerar as opera??es de busca e recupera??o de dados. Particionamento de Dados: Divida grandes conjuntos de dados em parti??es menores. Normaliza??o e Desnormaliza??o: normaliza??o (para evitar redundancia) ou desnormaliza??o (para melhorar o desempenho de leitura). Otimiza??o de Consultas: Analise e ajuste suas consultas SQL para garantir que elas sejam executadas da maneira mais eficiente possível. Limpeza de Dados: Remova dados desnecessários ou duplicados. Caching de Dados: Use técnicas de caching para armazenar dados frequentemente acessados em memória. Uso de Ferramentas de Desempenho: Utilize ferramentas de monitoramento e diagnóstico

Performance optimization needs to be a standard process that you can replicate any time you work on a new data model or database implementation. Document it.

Optimizing data outputs is crucial for meeting the varying needs of BI users, from analysts to executives. Techniques like data indexing and partitioning are vital for query performance, while caching can significantly speed up data retrieval for frequently accessed information. Compression and archiving not only save storage costs but also improve system efficiency. It's essential to balance these strategies to maintain a responsive BI environment that adapts to the evolving demands of users and minimizes latency in decision-making processes.

You'll want to create a set of tests, expressed in code, that can be run whenever a change is proposed to your transformation regime, which will prevent you from implementing a breaking change accidentally. You'll also run these tests on a regular basis to monitor for regressions stemming from changes in upstream applications. You'll also want to implement some kind of anomaly detection system to detect slow degradations of services compared to the dramatic failures caught by tests. Finally, implement a method of looking at your data state in the past, using something like Snowflake time travel. This will help you quickly diagnose issues not caught by your testing and observability solutions that get reported by impacted users.

Make sure that users only have access to the data that is relevant to their roles and responsibilities by considering implementing Role-Based Access Control (RBAC) to control access to different types of data based on user roles and permissions.