Is Your Data AI Ready? Critical Questions You Must Ask Data Engineers

The rise of generative AI (GenAI) as a solution to automate manual-intensive and repetitive tasks is revolutionizing how we handle data quality challenges. Despite augmented features and evolving technologies, GenAI also introduces new challenges to organizations who attempt to deploy them for data quality improvement. Before the actual implementation, data and analytics leaders should understand potential challenges and risks and know how to react to them. Data engineers are expected to assure data is risk-free by analyzing the extent and forms of data bias. Join this free D&A webinar where Gartner Distinguished VP Analyst Mark Beyer explains the interactive relationship between traditional and augmented data management and AI data processes. You’ll understand how AI processes metadata from synthetic data, labeling, enrichment and contextual processes to actively leverage observability.

Compare the utilization of data between artificial intelligence and human intelligence
Identify the factors that contribute to the challenges of reusing data
Assess model quality and ensure data privacy and security