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How Meta understands data at scale
- Meta faces challenges in managing and understanding large-scale data ecosystems and has invested in advanced technologies through its Privacy Aware Infrastructure (PAI).
- Meta adopted a 'shift-left' approach with early integration of data schematization and annotations in product development and a universal privacy taxonomy for standardized data privacy management.
- Flexibility and collaboration were key in understanding data across Meta's diverse systems and languages, with ongoing privacy initiatives embedded in product development.
- A decade-long journey saw Meta catalog millions of data assets daily, ensuring privacy considerations at every product development stage.
- Data understanding at Meta involves capturing asset structure and meaning, with privacy driving product innovation through tools like PAI.
- Efforts in data understanding have evolved to address challenges such as inconsistent definitions, missing annotations, and organizational barriers.
- Meta's approach included introducing shared asset schema formats, a unified taxonomy of semantic types, and investing in heuristics and classifiers for accurate data understanding.
- Data understanding initiatives involved steps like schematizing data, predicting metadata at scale, annotating with privacy taxonomy, and inventorying assets using systems like OneCatalog.
- Maintaining data understanding involved shift-left strategies, detecting and fixing annotation gaps, collecting ground truth, and providing canonical consumption APIs for compliance.
- Data understanding has helped Meta streamline developer workflows, improve AI systems, ensure operational efficiency, and drive product innovation through comprehensive data understanding.
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