Session Type: Talk + Q&A (30 mins)

Alignment with the Conference Theme
Data-driven organizations thrive only when their data is accurate, complete, and reliable. This session aligns with the theme by helping companies eliminate costly data errors that impact business performance.

Problem Statement & Relevance
Bad data leads to incorrect analytics, compliance risks, and lost revenue. Organizations spend millions fixing data issues manually when proactive quality measures could prevent these failures.

Solutions & Insights
This session provides a practical framework for data quality, including:

- Automated data validation techniques (Great Expectations, dbt tests)
- Common data quality pitfalls and how to avoid them
- Case studies of companies that turned around poor data practices

Key Takeaways
- How to quantify the cost of bad data and secure executive buy-in
- A data quality framework for ongoing maintenance
- How to implement automated testing and observability for reliable data