In this session, Gorkem Sevinc, CEO of Qualytics, will provide practical strategies to ensure your data is ready for AI and machine learning. He will cover three essential steps: establishing data quality metrics, structuring data for predictability, and ensuring data consistency. You will learn the 8 key metrics necessary for adequate measurement of your data quality, the importance of predictable data shapes, and real-world examples of the impact of unpredictable data. Additionally, he’ll share why data consistency is crucial for AI and explore steps to maintain it, illustrated by challenges caused by inconsistent data. The presentation will conclude with a Q&A session, offering a chance to gain expert advice. This is a hands-on opportunity to enhance your data strategy and prepare your organization for the future of AI and ML.

What You'll Learn:

• Establishing data quality metrics
• Structuring data for predictability
• Ensuring data consistency
• The 8 key metrics for measuring data quality
• Real-world examples of the impact of unpredictable data

Enhance your data strategy with expert advice in the concluding Q&A session.

Speaker: Gorkem Sevinc

Gorkem Sevinc is the Co-Founder and CEO of Qualytics, the Augmented Data Quality Platform, and an Adjunct Assistant Professor at Johns Hopkins University School of Medicine. Gorkem has vast experience in building, scaling, and leading teams and architecting enterprise-level software and data infrastructures, with specific experience in Artificial Intelligence, Machine Learning, Data Analytics and Visualization, Large-Scale Data Operations, and Scalable Architectures. He was previously the Co-Founder and Chief Architect of Facet, a financial services company focused on providing full financial management to mass affluent households; Co-Founder and CTO of Scene Health, a mobile health platform for medication adherence; VP of Software Engineering for miDiagnostics, a medical device company focused on blood diagnostics through nanofluidics; and Managing Director of the Johns Hopkins Medicine Technology Innovation Center. His education includes Johns Hopkins University (MSE, Surgical Robotics, Computer Science) and the University of Kansas (BSc, Computer Science - Fulbright Scholar).

Speaker: Renee Colwell

Renee Colwell is a Senior Data Analyst / Data Specialist with skills in SQL development, Business Intelligence, and data warehouse environments with knowledge and experience in data governance, data quality, data lineage, and data curation. Renee is the go-to specialist for data quality issues, root cause analysis, troubleshooting, data sourcing and provisioning, data profiling and quality metrics, data migration, and cleansing and has experience in complicated line-of-business silos, workflows, and how they affect the data landscape.