Why is data quality such a critical factor in whether AI and machine learning succeed or fail?
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 real-world consequences of poor data and the impossible choices it forces organizations to make.
Jon highlights:
- The two bad options teams face when data quality is low:
(1) wasting time manually cleaning messy data
(2) blindly trusting flawed outputs from AI/ML pipelines
- How biases and hidden errors can slip into models unnoticed
- Why poor data quality leads to incorrect predictions, wasted engineering cycles, and bad business decisions
- The importance of having data that is clean enough, consistent enough, and well-structured for your AI workflow
- How high-quality event data accelerates reliable insights and reduces risk
This clip is essential for anyone involved in machine learning, AI engineering, data science, data governance, MLOps, analytics, and event data management.
Interested in hearing more? Watch the full webinar here: https://snowplow.io/events/the-hidden-costs-of-poor-data-quality-in-ai
#AIdataquality #datacleaning #datagovernance #eventdata