With the unstoppable rise of Artificial Intelligence, organisations are faced with an urgent question: how can they ensure that AI models are designed, deployed and used in an ethical, traceable and compliant manner? The answer lies not only in MLOps tools, but also in having a robust framework for governance that integrates people, processes and technology.
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Compliance with data protection regulations, such as the European Union's General Data Protection Regulation (GDPR), is a key challenge for modern organisations. These regulations require, among other things, keeping records of data processing activities up to date, ensuring traceability, conducting impact assessments and demonstrating compliance. The complexity of this task increases considerably if adequate tools are not available.
Controlled democratisation of data is a goal pursued by many organisations, but achieving it depends on many factors.
DAMA establishes a compilation of best practices for data management, highlighting two concepts: Data Governance and Data Management. However, these two concepts must be differentiated because they involve a separation of functions between supervision and execution.
Metadata is data that describes various facets of data assets, providing the context necessary to use and interpret this data and improving our understanding of it throughout its entire lifecycle.
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