Data Observability: Guidance for Data Leaders

Data observability lives within an uncomfortable paradox. It is widely recognized as critical to a modern data stack and to delivering upon the potential of AI. Yet it is something that many data leaders are still talking about as an emerging priority. This fact reveals something telling: Many organizations are struggling to get the reliable data their agents require. They don’t know if they are getting reliable data to their AI agents or not. They might be, or they might not be – but without data observability, they are just hoping and guessing. Instead, you want to know what is happening, good or bad. This immaturity becomes harder to tolerate as AI moves from information to action.

In an AI-driven universe, data flows are growing in complexity, and the transformation logic needed to move data reliably from source to consumption needs to be more sophisticated than the past. Given this, the more intricate the pipeline, the more opportunities exist for errors to creep in, for data to leak, and for the wrong information to reach the wrong place at the wrong time. Rahul Auradkar, president and GM of Salesforce Data Foundations, believes this should be easy enough that even end users can ensure quality checks have been made. At the same time, he is seeing an increasing problem with system and data fragmentation that observability can help with.

At its core, data observability is the discipline for ensuring that delivery happens. It’s showing proof of whether it’s happening or not. In other words, data is fit for purpose when and where it’s consumed. This requires that errors and bottlenecks are surfaced before they become business problems, and that the infrastructure operates at its potential. Think of it as a continuous window into how data moves: not just confirming that pipelines are running, but validating that what flows through them is actually trustworthy.

The Pressure Is Building from Multiple Directions

The need for data observability is being pushed for multiple reasons. One vector is organizations are deepening their commitment to data governance – encompassing quality, security, privacy, lifecycle management, and cost – and with this comes greater scrutiny of data assets and how they are produced and handled. At the same time, AI is shifting from experimental to essential. As AI use cases become mission-critical, the pipelines that feed them become equally so. A model is only as reliable as the data it consumes. Stakeholders who have tolerated occasional data quality issues are asking harder questions including what, exactly, is flowing into their most consequential systems. The result is a growing imperative for control – not just oversight in theory, but real-time transparency into how data moves across BI, analytics, and AI environments that these new pressures demand.

Data Observability Is the Entry Point for Governance – Not an Add-On to It

Realizing data observability is the entry point for data governance is the insight that changes the conversation. Governance has to start somewhere, and the logical starting point is a clear, factual understanding of your data assets: what data exists, how they are accessed, where they flow, and how they are consumed across the enterprise. Data observability, done well, provides exactly that foundation.

Surfacing metrics about data usage and pipeline behavior gives governance teams the evidence base they need to move from policy to practice. It allows them to operationalize rules around security, privacy, quality, lifecycle, and cost in ways that are grounded in how data actually behaves, not how it’s assumed to behave. Providers that build toward this vision leverage AI to automate monitoring and augment the collection of data-related metrics. By doing so, they position their products at the center of something much larger than pipeline health. The organizations that benefit most are those already feeling the friction: persistent data loss, unreliable pipelines, and quality issues surfacing too late in critical workflows. For them, observability isn’t a nice-to-have. It’s the first step toward infrastructure that can actually be trusted.

The Numbers Behind the Gap: What the Data Says About Data Observability

The story of data observability in 2026 is, at its core, a story about a gap between how important organizations know this capability is, and how many have actually done something about it. Dresner Advisory Services’ 2026 research makes this tension visible. Only 22% of surveyed organizations identify data observability as critical, though another 32% rate it as very important. That’s a meaningful base of awareness. Yet only slightly more than one-third of organizations report actual adoption. Globally, strong majorities across every region acknowledge its importance and still, most haven’t acted. The gap between conviction and execution is wide, and it is one of the more telling findings in this year’s research.

The Organizations That Have Moved Aren’t Waiting for Data to Harm the Organization

Among those that have acted, a pattern is clear: Data observability adoption concentrates where performance pressure is highest. Seventy-one percent of organizations achieving high ROI from their BI investments rate this capability as critical. Forty-four percent of organizations at an advanced stage of AI deployment have already adopted it. These aren’t organizations that stumbled into observability – they are organizations for whom data reliability has become a competitive requirement or where regulators require it, and they’ve built accordingly.

Quality and Trust Drive the Agenda

When organizations deploying data observability are asked what they’re trying to accomplish, improving data quality and trust leads decisively, cited by 73% of respondents. This is the foundational motivation: Before data can be used with confidence, it has to be trustworthy. Close behind is a cluster of reliability objectives that speak to the operational demands of running data pipelines at scale. This means having the ability to detect and resolve pipeline failures, meeting data delivery SLAs, monitoring data freshness, and troubleshooting root causes of pipeline issues. Each of these was cited by 50% to 60% of respondents, representing significant and consistent demand. Compliance and cost reduction trail further behind, cited by 32% and 27% respectively, suggesting that for most organizations the primary case for observability is still reliability, quality, and trust, not risk mitigation.

The benefits organizations actually experience track closely with what they set out to achieve. Eighty-two percent cite improvements in surfacing data quality issues as a realized benefit; 71% point to the ability to surface data latency issues. This number reflects the growing emphasis on ensuring that critical data flows arrive not just accurately, but on time. In 2026, 61% of respondents identify recognizing schema changes and data volume anomalies as relevant, completing a picture of broad-based demand for anomaly detection across the pipeline.

Security Is Emerging as a Front-Line Concern

One of the more significant findings in this year’s data involves the rapid rise in demand for security-related capabilities: 52% of organizations indicate that security management features are important in a data observability solution and 50% identify quality and quality assurance management features as equally so. This likely reflects the emergence of data security posture management as a rapidly evolving priority, bringing with it new expectations for what observability tooling must cover.

Beyond security, organizations prioritize capabilities for monitoring data-related anomalies above all else. Integration with identity and access management, and data lineage and impact analysis, rank next in importance at 37% and 36% respectively. Notably, nearly half of respondents indicate that audit trails and lineage tracking are important – the highest rate among all relevant capabilities – underscoring how closely observability and accountability are becoming intertwined.

Vendor Landscape Reflects a Market Still Forming

On sourcing strategy, the market is nearly split. 53% of organizations prefer best-of-breed vendors for data observability, while 46% favor vendors that partner and tightly integrate with their strategic data and analytics infrastructure provider. That near-even divide suggests the market hasn’t yet consolidated around a dominant model and that vendors on both sides of the equation have real opportunity, provided they can demonstrate depth where it matters. The data, taken together, describes a capability that organizations understand they need, are beginning to evaluate seriously, and are only starting to build into the fabric of how they manage data at scale. The gap between importance and adoption won’t close on its own. But the organizations closing it fastest are, by most measures, the ones pulling ahead.

Parting Words

It is increasingly clear that data observability is evolving beyond a mission of improving data quality. While data quality remains foundational, organizations now recognize that ensuring trusted data requires visibility and measurement across the entire data lifecycle – from creation and movement to consumption and governance. As a result, data observability is expanding into adjacent domains, including metrics management, data governance, and support for data security initiatives. The need to monitor, measure, and validate data at every stage naturally broadens its scope and strategic importance. This expanding charter reflects the growing recognition that data reliability is an infrastructure-level concern, not merely a data management issue. The market’s evolution is likely to continue, and it will be interesting to see over the next several years just how quickly the data observability mandate becomes – and how deeply it becomes embedded in the core data infrastructure stack.