September 2025 Trends Report:
Data Quality and Observability

Findings from 109 data professionals on how teams are monitoring and improving data reliability.

Introduction

Each month, we run a brief survey through our newsletter and social channels to capture what’s top of mind for today’s data teams. The goal is to share actionable benchmarks and peer-driven insights that can help you fine-tune your data stack. These reports aim to reflect the shifting priorities and real-world challenges faced by professionals in engineering, analytics, and operations.

This month, we focused on a cornerstone of every modern data stack: data quality and observability. As pipelines scale and complexity increases, ensuring that data is accurate, timely, and trustworthy has become both a technical and organizational challenge.

To explore how teams are approaching this, we surveyed 109 data professionals across industries to learn how they define ownership, monitor data quality, and manage reliability.

Why This Survey Matters

Reliable data is the backbone of effective analytics, yet many teams still lack visibility into the health of their pipelines. In an era of automation, AI, and real-time decision-making, poor data quality can quickly derail even the most sophisticated systems.

Our findings reveal that while data quality has become a shared responsibility across teams, ownership and process maturity vary widely. Observability tools are gaining traction, but many organizations still rely on manual checks and ad hoc monitoring.

This signals a crucial transition: from reactive troubleshooting to proactive, automated observability, where data reliability is measured, monitored, and managed like application uptime.

Methodology

• Respondents: 109

• Company sizes: From early-stage startups to global enterprises

• Industries: SaaS, finance, retail, healthcare, media, manufacturing

• Roles: Data engineers, analytics engineers, analysts, and data product managers
  
  

Key Takeaways

• 44% say data quality ownership is shared across multiple teams

• 61% of respondents still rely on manual checks or SQL-based validation

• Only 27% report using a dedicated data observability platform

• 39% track SLAs for key pipelines, but only 14% enforce them organization-wide

• The top challenge: limited visibility into pipeline health (31%)
  
  

Results and Insights

Who Owns Data Quality?

Insight

Ownership remains fragmented. Most organizations acknowledge shared accountability, but this can blur responsibility for resolving data issues.

How Teams Monitor Quality

Insight

Manual validation remains the default, especially in mid-sized teams. However, adoption of observability platforms is accelerating, especially among companies managing multiple data sources or real-time pipelines.

What Teams Prioritize in Data Quality

Insight

Teams prioritize the basics, accuracy, and freshness over deeper lineage or compliance aspects. This shows observability is still maturing beyond surface-level monitoring.

SLAs and Reliability Practices

Insight

Few organizations treat data SLAs with the same rigor as application SLAs. Those that do tend to have dedicated reliability or governance teams.

Data Incident Management

Insight

Nearly half of respondents resolve incidents informally. This lack of standardization suggests observability is often reactive rather than systematic.

Top Challenges in Maintaining Reliable Data

Insight

Visibility and accountability continue to be the biggest pain points. Even with modern tools, many teams struggle to trace root causes or define who’s responsible when data issues arise.

Final Thoughts

Data quality is evolving from a technical afterthought to a strategic necessity. The trend is clear: as organizations scale their pipelines, they must also scale trust in their data.

While adoption of observability platforms is growing, most teams are still early in the maturity curve. The next step is standardizing ownership, automating monitoring, and aligning SLAs with business priorities.

Data reliability can no longer be treated as optional. It’s a foundation for confident decision-making, efficient operations, and successful AI adoption.

About Integrate.io

Integrate.io helps data teams simplify pipeline monitoring and improve reliability with built-in visibility, alerts, and transformation testing. By centralizing ingestion, transformation, and orchestration, Integrate.io reduces tool sprawl and makes it easier to deliver high-quality, trustworthy data.

Ready to improve your data reliability? Request a demo