More business leaders and technologists are focusing on improving the data quality behind AI projects to promote more inclusive datasets to take bias out of AI …
Data engineers spend 40% of their workweek dealing with incidents relating to poor data quality, which may cost an organization 20% of its
Manual efforts are no longer enough to ensure data quality. That's why data quality enhancement technology is an imperative.
From logistics to fraud prevention and across industries, data enrichment is being used, providing new insights and streamlining processes.
As organizations continue to add more data collection and analytics to their business, proper data management and governance is critical for future
Bogging down ML engineers with poor quality data that requires extensive manual processes impacts product quality and new feature speed to market.
Blockchain can help ensure the success of IoT and AI by ensuring the trustworthiness and accuracy of the data being used by systems.
Cloud service uses Hadoop and machine learning to find and fix issues in real time.
Corporate educator TDWI has published an eBook on five engineering requirements for machine learning
The MapR DataOps Governance Framework enables organizations to achieve a high level of data quality and integrity, and help meet mandated