What to do with missing data?
Episode 14 of the Association Tech Insider Podcast explores the pervasive issue of missing data, offering insights into innovative strategies and emphasizing the critical role of data governance and quality in AI and machine learning.
Key Highlights:
- Understanding Data Imputation: Heather Harris from Herkimer Consulting introduced the topic, stressing the impact of missing data on organizational analytics and the necessity for appropriate handling methods.
- Inteleos Journey: Juan Sanchez shared Inteleos' approach towards adopting data imputation strategies, highlighting the role of outcome thinking and goal-setting in improving data accuracy and operational efficiency.
- AI and Machine Learning Implications: The panel discussed the significance of complete and high-quality data in training AI and machine learning models, underscoring the need for advanced data handling techniques beyond mean imputation.
- Future Outlook and Opportunities: The conversation touched on applying data imputation to unstructured data and its potential to enhance AI development.
