As AI shapes business decisions, making unstructured data AI-ready is a key governance priority. The quality, accessibility and security of unstructured data directly determine the performance of AI applications, particularly for GenAI. To unlock its value for AI initiatives, data and business leaders should evolve their governance strategies to effectively manage, protect and utilize unstructured data, ensuring it is AI-ready while meeting compliance and security requirements.
talk-data.com
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Speaker
Melody Chien
5
talks
Melody Chien is a Senior Research Director with Gartner's Data Management team. Ms. Chien's research includes the best practice for building effective data quality programs, and data and analytics governance strategies. She also covers tool selections and vendor evaluations for data quality solutions, metadata management solutions, and data observability tools. Additionally, her research includes generative AI in data management practices and AI-ready data for AI initiatives.
Ms. Chien has over 24 years of experience in data management and BI analytics. She spent most of these years in California Silicon Valley in U.S. and worked on big-sized data management projects in large organizations, such as SAP and PayPal, where she has built professional expertise in data and analytics management.
Bio from: gartner-data-analytics-apac-2025
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Data quality and data observability tools provide significant capabilities to ensure good data for your BI and AI. Data observability tools give organizations integrated visibility into the health of their data, data pipelines and data landscape. Data quality tools enable business users to manage data at its sources by setting rules and policies. Together, these tools help organizations build a strong foundation in data management for BI and AI initiatives.
Metadata, data quality and data observability tools provide significant capabilities to ensure good data for your BI and AI initiatives. Metadata tools help discover, and inventory your data assets. Data quality tools help business users manage their data at sources by setting rules and policies. Data observability tools give organizations integrated visibility over the health of data, data pipeline and data landscape. Together the tools help organizations lay good foundation in data management for BI and AI initiatives.
Data architects are increasingly tasked with provisioning quality unstructured data to support AI models. However, little has been done to manage unstructured data beyond data security and privacy requirements. This session will look at what it takes to improve the quality of unstructured data and the emerging best practices in this space.
Traditional approaches and thinking around data quality are out of date and not sufficient in the era of AI. Data, analytics and AI leaders will need to reconsider their approach to data quality going beyond the traditional six data quality dimensions. This session will help data leaders learn to think about data quality in a holistic way that support making data AI-ready.