2025 Data Quality Benchmark Survey

In the 2025 Data Quality Benchmark Survey we’ve asked hundreds of data leaders and engineers about their top challenges and planned investments around data quality.

AI/ML use cases are now the most critical use cases, but data teams are struggling with how to best build reliable data pipelines, citing “insufficient knowledge of how to test well” as their top data quality challenge.

This high-stakes environment is driving nearly 40% of companies to increase investments in data quality and observability tools, which today mainly consists of built-in tooling from their data transformation tool.‍

Download the 2025 Data Quality Benchmark here

Key findings

• Insufficient knowledge on how to test well is the biggest data quality challenge
• AI/ML ranks as the most important use case for data, followed by internal BI reporting
• Nearly 20% of respondents report a single data incident cost over $10,000
• The vast majority of participants rely on built-in tests from their data transformation tool
• Nearly 40% of teams plan to increase their data quality and observability investments next year
• Only 10% of respondents use AI often in their data quality workflows