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Supercharge Your Data Quality Testing with AI/ML

Supercharge Your Data Quality Testing with AI/ML

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About The Video

Check out our latest LambdaTest Experience (XP) Series episode featuring 𝐘𝐨𝐠𝐞𝐧𝐝𝐫𝐚 𝐏𝐨𝐫𝐰𝐚π₯, Test Automation Lead, Binmile. In this session, Yogendra introduces the importance of data quality testing and its impact on data-driven decision-making. He also highlights the limitations of traditional data quality testing methods and the challenges with increasing volume of data. Check out the session now!

Video Chapters

00:00 Welcome

03:57 Why is Data Quality and Detailed Testing Important?

08:24 Challenges and Solutions in Data Quality Testing

11:51 What Potential does AIML have for Data Quality Testing?

21:55 Key Considerations for Adopting AI in Data Quality Testing

27:36 Advice for Exploring AI and ML for Data Quality Testing

29:45 Conclusion

Key Topics Covered

Introduction to the Series

Overview of the LambdaTest series and introduction of guest speaker Yugendra, a test automation expert.

Importance of Data Quality and ETL Testing

Discussion on why data quality is critical in data-driven enterprises and its influence on business decision-making.

Approach to Data Quality Testing

Insight into the fundamental approach to data quality testing, including understanding business processes and data domains.

Challenges in Ensuring Data Quality

Key challenges such as handling diverse data sources, formats, and managing large-scale data.

Potential of AI and ML in Data Quality Testing

Exploration of how AI and machine learning can automate tedious tasks and improve the accuracy of data testing.

AI and ML Tools for ETL Testing

Overview of tools like Keboola and QuerySurge for automating ETL testing processes.

Key Areas for AI/ML Impact in Data Quality

Promising areas such as automated data profiling and self-healing pipelines where AI can enhance data quality processes.

Practical Examples of AI in Data Testing

Real-life use cases of AI implementation in tasks like anomaly detection and improving data quality checks.

Considerations for AI Adoption

Important factors for organizations to consider when adopting AI for data testing, such as model selection and computational requirements.

Future Impact of AI on Data Quality Testing

Predictions on how AI will transform data quality testing into a more proactive and efficient process.

Advice for Implementing AI in Data Quality Testing

Tips for organizations looking to adopt AI and LLM models to enhance their data quality testing efforts.

Related Blogs & Hubs

In-Depth with Playwright: A Modern Testing Framework

Building Quality Software: AI-based testing approach with Jira and QMetry

Rethinking the Role of QA Profile

Transforming Test Automation: The Power of Generative AI in Reducing Maintenance and Enhancing Speed

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