Summary
Discover how to tackle poor data quality and unlock the potential of generative AI with the right data architecture.
Poor data quality can lead to customer distrust, regulatory issues, poor decision-making, lost revenue, and failed projects. However, a robust data architecture can enhance data quality, and provide a framework for effective data management across various applications, including business intelligence and data science.
Join this webinar, featuring Jeff Wai and Mike Grasselt from IBM Data Governance, which will highlight four critical data quality capabilities necessary for a modern data architecture in today's evolving data landscape.
In this webinar you’ll get:
- Capabilities for improving data quality
- Real-world data quality use cases
- Live demos of data quality SLAs and monitoring
Key speakers
Jeff Wai - Product Manager, IBM Data Quality
Jeff is a product manager on the IBM Data Governance team, specializing in data quality. With previous experience building technology solutions for data architecture, governance and business process management use-cases at global financial firms, he aims to bring the voice of customers in building market leading data quality products at IBM.
Mike Grasselt - Information Platform and Solutions Developer, IBM
Mike is a software architect in the IBM Data Governance team with expertise in data quality workflows and a background in Mainframe system automation. With a history of collaborating with universities, Mike has contributed to advancing data-related education and research.
Ryan Yackel - GTM Product Manager, IBM Databand
Ryan is the IBM Product Strategy Leader. He’s passionate about solving data quality and reliability problems through data observability. Before IBM, Ryan led GTM programs at high-growth startups in software test automation, DevOps, and IAM cybersecurity.
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