Only 28% of Organizations Trust Their Data: How Data Observability is Transforming Enterprise Reliability

By 2026, two-thirds of enterprises are expected to invest in automated data observability tools, according to insights shared by Matt Aslett.

That stat speaks volumes about how much data reliability matters to enterprises right now. Matt highlights a critical shift: observability tools are quickly becoming the backbone of any solid data strategy, especially as businesses face increasingly complex data pipelines and higher stakes for getting it right.

Automated data observability isn’t just a nice-to-have anymore—it’s essential for any enterprise looking to stay competitive and confident in their data.

Only 28% of Organizations Trust Their Data

Data trust is the foundation of good decision-making. When stakeholders don’t trust the data feeding into their dashboards or models, the ripple effects are immediate: wasted time, missed opportunities, and lost confidence.

Traditional data quality tools aim to address this, but as Matt points out, they fall short in today’s complex environments. They often require manual intervention, leaving teams reacting to issues instead of preventing them. This manual, reactive approach not only drains resources but also makes it harder to prove the value of data initiatives to the business.

That’s where automated data observability comes in.

Two-Thirds of Enterprises Are Investing in Observability

So, why are two-thirds of enterprises expected to adopt automated data observability tools by 2026?

The answer lies in their ability to tackle modern data challenges at scale:

1. Proactive Monitoring: Observability tools don’t wait for problems to surface—they catch anomalies as they happen, reducing downtime and preventing errors from spreading.
2. Comprehensive Visibility: Features like dependency mapping and data lineage provide a complete view of how data flows through systems, making root cause analysis faster and easier.
3. Scalability for AI and GenAI: With AI initiatives relying on increasingly complex data pipelines, observability ensures the underlying data remains trustworthy and reliable.

For example, imagine a global financial services company detecting an anomaly in customer transaction data. With traditional tools, it could take days to trace the issue back to a schema change. Automated observability tools like Bigeye identify the anomaly immediately, map the impacted systems, and can even suggest debug queries to resolve the problem.

Why Enterprises Are Doubling Down

What really drives this shift is the business case. Matt explains how automated observability tools deliver real value by:

• Saving time and resources: Automation frees up data teams from manual troubleshooting.
• Building trust in data: With less than a third of organizations reporting high levels of trust in their data, observability tools address a major gap.
• Supporting AI initiatives: Reliable pipelines are non-negotiable for any business investing in AI.

At Bigeye, we see this play out every day. Enterprises that invest in observability are able to shift their focus to higher-value work and demonstrate clear ROI from their data initiatives.

Inspired by Matt Aslett’s Insights

Matt’s perspective on data observability highlights why it’s no longer optional for enterprises. His emphasis on data reliability as the foundation for AI success makes a lot of sense, especially as more organizations implement those initiatives.

At Bigeye, we’re proud to be leading this shift.

If your enterprise is ready to move beyond traditional data quality tools, now’s the time to take action.

Matt’s insights offer a great starting point: evaluate your current data reliability strategy, embrace automation, and invest in tools that can prepare you for the future.

Check out Matt Aslett’s full analysis here to learn more, or request a demo to start exploring how automated observability could transform your approach to data.