Está ampliando su infraestructura de datos. ¿Cómo puede garantizar que los estándares de calidad de los datos se mantengan al día?

In the first place, it is important to establish clear data quality standards such as accuracy, completeness, consistency, timeliness, and validity. Ensure these standards are documented and communicated across the organization. One thing I believe that can help is to have controls built in to ensure implementation of these standards are success criteria for architecture design and system implementation sjgn-offs.

Expanding data infrastructure is a big step, and maintaining high data quality standards is crucial for ensuring that your data remains reliable and useful. We can walk through some strategies here. 1. Establish Data Governance: Develop a comprehensive data governance framework that outlines data quality standards, policies for data management, data stewardship, and data quality metrics, roles, and responsibilities. 2. Implement Data Quality Metrics: Define and monitor key data quality metrics and regularly review these metrics to identify and address any issues promptly. 3. Automate Data Quality Processes 4. Train and Educate 5. Foster a Data Quality Culture 6. Leverage Data Quality Frameworks and Best Practices

As the company expanded, so did its data infrastructure. With new platforms and massive data inflows, errors and inconsistencies began to creep into reports. Sarah, the data architect, knew the growth was crucial but saw the risk to data quality. * She implemented automated validation rules, set up real-time monitoring, and trained teams on best practices. * Each new data source was vetted for accuracy before integration. * Over time, despite the scale, data quality improved, not declined. Sarah's proactive approach ensured that as the infrastructure grew, the standards held firm, maintaining trust in the data.

Hoje a melhor estratégia para manter a qualidade de dados está no emprego de ferramentas de qualidade de dados integradas com inteligência artificial, ou então desenvolver uma API que recebe metadados e informações de dados e esta faz uma integração com outra API ou ferramenta de IA para receber estas informações e com isso processar a maior quantidade de dados e metadados possível. Isso vai auxiliar muito na expansão da governança de dados por inúmeras áreas dentro da empresa.