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Meet the Expert : Jennifer Yang on Applying Machine Learning Techniques for Data Quality Management /
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 mana...
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| Formato: | Electrónico Video |
| Idioma: | Inglés |
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O'Reilly Media, Inc.,
2021.
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| Edición: | 1st edition. |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
| Sumario: | 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 a large amount of data. This presentation will discuss a use case that demonstrates how machine learning techniques can be used in data quality management on a big data platform in the financial industry. Join us for this edition of Meet the Expert with Jennifer Yang to learn how machine learning techniques can be used in the data quality management processes in the financial industry. Jennifer will describe: Results of applying various machine learning techniques in the four most commonly defined data validation categories will be presented. Approaches to operationalize the machine learning data quality management solution. Lessons learned will be discussed to help the audience take on their own data quality management journey using machine learning techniques. 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. |
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| Descripción Física: | 1 online resource (1 video file, approximately 57 min.) |