Key Takeaways: Data in organizations is typically managed by two distinct groups: data producers and data consumers. Data governance is essential in the age of data democratization, especially when...
Key Takeaways: Prioritize metadata maturity as the foundation for scalable, impactful data governance. Recognize that artificial intelligence is a data governance accelerator – and a process that...
Key Takeaways: Data integrity is essential for AI success and reliability – helping you prevent harmful biases and inaccuracies in AI models. Robust data governance for AI ensures data privacy,...
How compliant is your organization with the GDPR (General Data Protection Regulation) requirements that keep personal data only as long as needed for the purpose it was collected? How easily could...
Key Takeaways Data quality ensures your data is accurate, complete, reliable, and up to date – powering AI conclusions that reduce costs and increase revenue and compliance. Data observability...
Key Takeaways Data fabric and data mesh are modern data management architectures that allow organizations to more easily understand, create, and manage data for more timely, accurate, consistent, and...
Each of the following data mining techniques cater to a different business problem and provides a different insight. Knowing the type of business problem that you’re trying to solve will determine...
Key Takeaways: Data governance has transitioned from a regulatory necessity to a strategic asset that enhances innovation. Robust data governance supports AI applications, ensuring data is AI-ready,...
Jean-Paul Otte recently joined Precisely as Head of Data Strategy Services for Europe. His specialty? Data! Jean-Paul sat down for an interview where we discussed his background as a former CDO,...
