Meet the Expert: Jennifer Yang on Applying Machine Learning Techniques for Data Quality Management
A traditional rule-based data quality management methodology is costly and hard to scale in the big data environment. It requires subject matter experts within business, data, and technology domains. Machine learning techniques for data quality management enable cost-effective and scalable ways to manage data quality for large amounts of data.
Join us for this edition of Meet the Expert with Jennifer Yang to explore the best machine learning techniques to use with data quality management processes in the financial industry. You’ll examine a real-world use case to see the results of applying various machine learning techniques in the four most commonly defined data validation categories, dive into approaches to operationalize your machine learning data quality management solution—and hear the lessons Jennifer learned along the way.
O'Reilly Meet the Expert explores emerging business and technology topics and ideas through a series of one-hour interactive events. You’ll engage in a live conversation with experts, sharing your questions and ideas while hearing their unique perspectives, insights, fears, and predictions.
What you'll learn-and how you can apply it
By the end of this live show, you’ll better understand:
- How to use machine learning techniques to manage data quality
- Other data management areas where machine learning techniques could be valuable
This Discussion is for you because...
- You want to learn about the fundamental shifts that are transforming the business landscape and customer needs.
- You want to explore new methodologies in data quality management to build more scalable and cost-effective solutions.
- You want to explore how to use machine learning techniques within data management.
- Come with your questions for Jennifer Yang
- Have a pen and paper handy to capture notes, insights, and inspiration
About our guest
Jennifer Yang is the head of data management and risk control at Wells Fargo Enterprise Data Technology. Previously, she held various senior leadership roles in data analytics, risk management, and capital management at major financial institutions. Jennifer’s unique experience in business and technology allows her to understand data and technology from both the end user’s and data management’s perspectives. She’s passionate about leveraging the power of new technologies to gain insights from data to develop cost-effective and scalable business solutions. Jennifer holds an undergraduate degree in applied chemistry from Beijing University, a master’s degree in computer science from Stony Brook University, and an MBA from New York University.
The timeframes are only estimates and may vary according to how the class is progressing
Thursday, January 14 2020, at 9:00am PT / 12:00pm ET
- Introduction and presentation (15 minutes)
- Interactive discussion and Q&A (45 minutes)