When it comes to curating data standards, should organizations adopt existing frameworks, or develop their own?
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 why the real key to success is committing to data governance before you start collecting any data at all.
Jon breaks down:
- Why Snowplow emphasizes implementing a data governance strategy upfront
- How defining standards early prevents costly rework and sunk costs later
- Why teams often skip this step because it slows down initial prototyping
- How collecting data without governance leads to inconsistent models and unreliable AI outputs
- Why investing early in event standards, schemas, and definitions is essential for long-term AI success
This insight is critical for teams building machine learning pipelines, AI applications, data platforms, and high-quality event data ecosystems.
🔗 Watch the full webinar here:
https://snowplow.io/events/the-hidden-costs-of-poor-data-quality-in-ai
#datagovernance #datastandards #datamodeling #eventschemas
data standards, data governance, data modeling, event schemas, AI data quality, Snowplow, ML readiness, enterprise data strategy, Data Science Connect.