Veja como você pode garantir a qualidade e a integridade dos dados aproveitando os princípios de governança de dados.

Building on these defined goals, implement data standards and policies. These define how data should be formatted, categorized, and stored. Standardized data is easier to integrate, analyze, and trust. Enforce these standards through data validation rules and data lineage tracking. Validation rules ensure data accuracy at the point of entry, while lineage tracking allows you to trace data origin and identify potential inconsistencies. This comprehensive approach ensures your data reflects reality and supports reliable decision-making.

To ensure data quality and integrity through data governance principles, follow these steps: Define Goals: Establish clear, SMART objectives to guide your data governance strategy, such as improving customer satisfaction or streamlining operations. Data Stewardship: Appoint data stewards responsible for managing data assets, ensuring accuracy, and maintaining data standards. Policies and Standards: Develop and enforce comprehensive data policies and standards to ensure consistency and reliability. Data Quality Metrics: Implement metrics to regularly assess data quality, such as accuracy, completeness, and timeliness. Data Audits: Conduct regular data audits to identify and address quality issues promptly.

Standardize Processes: Data governance goes beyond just assigning roles. To ensure consistency and minimize errors, establish standardized processes for data collection, entry, and maintenance. This could include data quality checks, data validation rules, and clear documentation for data handling procedures. By standardizing these processes, you can streamline data management and prevent inconsistencies that could compromise data integrity.

Enforce Standardization. Data governance goes beyond policies. Define data standards to ensure consistency throughout your data warehouse. This includes data formats, definitions, and naming conventions. Standardized data simplifies analysis, reduces errors, and improves data lineage – the ability to track data origin and transformations. Standardization tools and data dictionaries can further enhance data consistency and understanding within your organization.

Data standards are just one piece of the puzzle. Appoint data stewards who are accountable for maintaining data quality within specific domains. These stewards can define data ownership, implement data dictionaries, and monitor adherence to standards. Regular data audits can identify and address any inconsistencies that may slip through. By establishing a clear data governance framework, you can ensure long-term data quality and empower informed decision-making across the organization.

Implement Data Lineage. Beyond monitoring, understanding the origin and flow of your data (data lineage) strengthens data governance. By tracing data from source to destination, you can pinpoint where errors might occur and track the impact of changes. This transparency allows you to identify potential risks early on and ensures data integrity throughout its lifecycle. Furthermore, data lineage simplifies impact analysis during regulatory audits or compliance checks.

In my experience, with the large volumes of data being acquired/generated in today's world, data quality is more of a process rather than an end state. While there are numerous technical methods to analyze and enhance data quality, it is very important to have the team aligned to the governance policies and best practices. This will ensure to keep the data quality better for longer.

Garantir a qualidade e integridade dos dados requer um olhar atento para os princípios de governança. Começo treinando a equipe sobre a importância disso e as políticas específicas da organização. Cada membro precisa entender como suas ações impactam os dados. Sessões regulares de treinamento são úteis para manter todos atualizados sobre práticas e tecnologias. Incentivar uma cultura de responsabilidade pelos dados é essencial, permitindo que a equipe relate problemas sem receio. Uma equipe bem informada é a melhor defesa contra manuseio incorreto e violações. Usar exemplos concretos nas sessões de treinamento ajuda a ilustrar o impacto real de boas práticas de governança de dados.

Data quality, integrity, and data governance are all different things. Think of data governance more like the people side. Set up a data governance organization where you have representation throughout the company and then you're ready to start governing things. It might be data quality. It could be metadata. Defining data quality or metadata does not come from a single person. In a huge effort, you might have one person leading and guiding others who were identified in the data governance organization, but not one person making all decisions.