The Cloudcast Improving AI Through Data Quality
8 snips
Jul 30, 2025 
Elliot Shmukler, Co-Founder and CEO of Anomalo, discusses the pivotal role of data quality in AI. He emphasizes the challenges of integrating unstructured data with AI models and the necessity for accurate data lifespan monitoring. Elliot reveals how proper data management can prevent model drift and ensure reliable outcomes. The conversation also covers the importance of centralized data repositories in streamlining processes, ultimately impacting organizational efficiency and confidence in AI technologies.
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AI Needs Quality Organizational Data
- Incorporating an organization's specific data into AI models is essential for relevant outputs.
- Data quality must be ensured to avoid models simply parroting bad or sensitive data.
Monitor Data Quality Throughout Pipeline
- Data quality should be monitored through each step of the transformation pipeline.
- Errors often arise from transformations, making monitoring essential beyond just data ingestion and final output.
Detect Data Issues by Anomaly Changes
- Detect data quality problems by identifying unexpected large changes in data characteristics.
- Automate spotting anomalies like missing data, late arrivals, or volume changes to flag likely quality issues.