How do organizations actually measure and enforce data quality standards when different teams and departments all use data in their own way?
In this clip from the “The Hidden Costs of Poor Data Quality in AI” panel hosted by Data Science Connect, Jon Malloy, Senior Technical Account Manager at Snowplow, explains the often-overlooked factor behind consistent, high-quality data: dedicated people.
Jon shares why:
- The strongest data-driven organizations invest in centralized data governance roles
- These teams ensure definitions, standards, and expectations stay consistent across the entire data estate
- Data quality breaks down when these roles disappear during layoffs or restructuring
- Even a small governance team (2–3 people) can drive massive value by preventing siloed data, inconsistent metrics, and conflicting interpretations
This insight is crucial for leaders in data engineering, AI/ML, analytics, data governance, MLOps, platform teams, and anyone scaling organizational data maturity.
Interested in listening to the full discussion? Watch the webinar here: https://snowplow.io/events/the-hidden-costs-of-poor-data-quality-in-ai
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