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This is the practice of creating, updating and consistently enforcing the processes, rules and standards that prevent errors, data loss, data corruption, mishandling of sensitive or regulated data, and data breaches. Effective data security protocols and tools contribute to strong data integrity.
This enhances the depth and usefulness of the data. DataGovernance Frameworks Establishing a robust datagovernance framework ensures that data quality is maintained consistently across the organization. It defines roles, responsibilities, and processes for data management.
We already know that a data quality framework is basically a set of processes for validating, cleaning, transforming, and monitoring data. DataGovernanceDatagovernance is the foundation of any data quality framework. If any of these is missing, the client data is considered incomplete.
It sits between the data lake and cloud object storage, allowing you to version and control changes to data lakes at scale. LakeFS facilitates data reproducibility, collaboration, and datagovernance within the data lake environment. Share features across the organization.
It asks much larger questions, which flesh out an organization’s relationship with data: Why do we have data? Why keep data at all? Answering these questions can improve operational efficiencies and inform a number of data intelligence use cases, which include datagovernance, self-service analytics, and more.
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