In this conversation, CEO Andrew Ahn discusses the intricacies of AI and data classification, emphasising the importance of data quality, curation, and the challenges posed by dark and gray data.

He highlights the risks of neglecting dark data and the benefits of automating data classification processes.

The discussion also covers real-world applications and the significance of domain knowledge in ensuring accurate data classification.

Takeaways

- The first step in creating an AI model is obtaining the right data.

- Data labelling, classification, and curation are distinct but interconnected processes.

- Curation is essential for organising data relevant to specific questions.

- Dark data represents unknown unknowns that can pose risks to businesses.

- Automating data classification can significantly reduce manual workload.

- 80% of a data worker's time is spent on data curation tasks.

- Bad data leads to poor decision-making and outcomes.

- Domain knowledge enhances the accuracy of data classification models.

- Companies need to be proactive in managing their dark data.

- The foundation of AI and analytics is high-quality, well-classified data.

Chapters

00:00 Introduction to AI and Data Classification

02:32 Understanding Data Labelling, Classification, and Curation

05:36 The Importance of Data Quality and Curation

08:09 Exploring Dark and Gray Data

11:07 The Risks of Ignoring Dark Data

13:54 Benefits of Automated Data Classification

16:18 Real-World Applications of Data Classification

19:20 The Role of Domain Knowledge in Data Classification

21:54 Conclusion and Future of Data Classification

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