Automating Data Quality in Dev Environments

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Lauren Maffeo

1:04:14

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01 - Introduction

01 - Why data quality is crucial.mp4

00:48

02 - 1. Write Your Roadmap for Data QA

01 - Manage your data as a product.mp4

02:24

02 - Choose a high-priority project.mp4

01:37

03 - Do a data audit.mp4

02:47

04 - Create a current-state process map.mp4

02:16

05 - Define data quality.mp4

02:32

06 - Write a roadmap for data product delivery.mp4

02:01

03 - 2. Practice Governance-Driven Development

01 - Write data requirements for your roadmap.mp4

01:51

02 - Confirm your datas source system(s).mp4

02:17

03 - Establish the right data system integrations.mp4

02:34

04 - Define your source datas minimum acceptance criteria (MAC).mp4

01:52

05 - Set up data lineage tracking.mp4

03:02

06 - Define levels of access per user.mp4

02:36

07 - Draft a to-be process map.mp4

02:33

08 - Define areas of data transformation.mp4

02:35

09 - Choose some super users to validate your product.mp4

01:44

10 - Give your data team room to fail.mp4

02:56

04 - 3. Monitor Data in Production

01 - Make a plan to govern data throughout the full lifecycle.mp4

02:38

02 - Practice data mesh design principles.mp4

03:29

03 - Automate federated data QA standards.mp4

02:12

04 - Execute data security standards.mp4

02:25

05 - Make a traceability matrix.mp4

02:45

06 - Scale and automate your data QA standards.mp4

03:01

07 - Use feature stores to prevent data drift.mp4

02:49

08 - Ship new data as deployable units.mp4

02:10

09 - Track ongoing regulation changes.mp4

03:10

05 - Conclusion

01 - Continuing your learning journey in the data quality.mp4

01:10

Description

Data quality is the backbone of successful AI, yet most leaders lack quality standards they can automate in production. This course teaches you how to create quality standards for the data in your domains, then automate those standards in production environments.

Most organizations produce and ingest more data than they can effectively manage, with insufficient standards to measure quality. As leaders face increasing pressure to leverage AI, companies that don't adopt and implement better standards will fall behind. Instructor Lauren Maffeo explains how to define data quality standards per domain, who should set these quality standards, which tools you should use to scale and automate these standards, and how to ensure that any new data is measured against these standards. Gain an understanding of the people, processes, and tools needed to know what data quality looks like and integrate those standards into your data architecture.

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Data Science

Lauren Maffeo

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Linkedin Learning

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LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.