In the quest to reach the full potential of artificial intelligence (AI) and machine learning (ML), there’s no substitute for readily accessible, high-quality data. If the data volume is insufficient, it’s impossible to build robust ML algorithms. If the data quality is poor, the generated outcomes will be useless.
Data silos, lack of standardization, and uncertainty over compliance with privacy regulations can limit accessibility and compromise data quality, but modern data management c...
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