High-Quality Unstructured Data Requires Human-In-The-Loop Automation

The article discusses the importance of managing unstructured data quality in enterprises, especially as they prepare to implement generative AI solutions like Microsoft Copilot. It highlights that while automation is essential due to the volume and complexity of data, involving content owners in the process often yields better outcomes. The key to achieving high-quality data lies in a hybrid approach known as human-in-the-loop automation, which combines human insight with automated processes to ensure data reliability, security, and scalability.

Human-in-the-loop automation integrates human judgment into automated workflows, allowing users to review and validate outputs at specific points. This approach helps maintain data accuracy and relevance, supporting successful data-driven initiatives. The article outlines considerations for implementing this system, such as defining objectives and identifying data owners for feedback. It provides examples of workflows, including data classification, retention, and employee offboarding, to illustrate how enterprises can effectively manage data quality by balancing human intelligence with automation.

Key takeaways:

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Combining human insight with technology through human-in-the-loop data quality automation leads to better data management outcomes.
Human-in-the-loop automation integrates human judgment into automated processes, ensuring accuracy and relevance at scale.
Implementing human-in-the-loop automation involves defining clear objectives and identifying the right data owners to provide feedback.
High-quality data is crucial for digital transformation and requires a strategic hybrid approach that balances human intelligence with automation.

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High-Quality Unstructured Data Requires Human-In-The-Loop Automation

Key takeaways:

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