As enterprise data platforms evolve, the ability to understand how they perform may become as important as the data they process. 

Modern data platforms such as Databricks have transformed how enterprises manage data, analytics and AI. With a unified Lakehouse architecture, organisations can bring data engineering, business intelligence, machine learning and AI workloads together. 

But as these environments become more complex, an important question emerges: 

How well do organisations really understand the health and performance of their data platforms? 

A pipeline may complete successfully, but is the data fresh? Are workloads performing efficiently? Can teams identify unexpected changes, bottlenecks or failures before they affect downstream applications? 

This is where data observability is gaining strategic importance. 

1. The growing data-readiness challenge

The growing importance of reliable data is reflected in recent industry research. 

According to Gartner’s February 2025 research, 63% of organisations either lack or are unsure whether they have the right data management practices to support AI. Gartner also predicted that through 2026, organisations would abandon 60% of AI projects unsupported by AI-ready data. 

McKinsey’s June 2026 research, AI Data Readiness: The Key to Scaling Impact, similarly highlights data readiness as a significant constraint to scaling AI. It reports that only 7% of companies have fully scaled AI across their organisations. 

These findings point to a larger challenge: having a modern data platform does not automatically guarantee that the data powering it is reliable, accessible and ready for enterprise use. 

2. Native monitoring versus broader observability

Databricks already offers several capabilities to help organisations monitor and manage their data environments. 

Through System Tables, Lakeflow Pipeline Monitoring, pipeline event logs and data quality monitoring, teams can gain visibility into workload performance, pipeline execution, data freshness, lineage, resource utilisation and operational costs. 

The advantages of native monitoring: 

• Integrated visibility within the existing Databricks environment. 

• Access to platform-level performance and operational metrics. 

• Reduced dependency on additional monitoring infrastructure. 

• Better alignment with existing data engineering workflows. 

However, as enterprises expand across multiple cloud environments, applications and interconnected data platforms, their monitoring requirements may extend beyond individual platform boundaries. 

Where broader observability can add value: 

• Connecting operational signals across different systems. 

• Identifying dependencies and issues across data pipelines. 

• Understanding anomalies through a more consolidated view. 

• Supporting faster investigation and root-cause analysis. 

Specialised observability solutions can help address these requirements, although they may introduce additional integration, infrastructure and telemetry costs. 

The opportunity is not necessarily to choose one approach over another, but to understand where each creates value. 

3. What are market leaders doing differently?

The industry is gradually moving beyond traditional dashboards and threshold-based alerts towards more connected and intelligent data operations. 

Three developments stand out: 

• From reactive to proactive monitoring – Organisations are increasingly exploring AI-assisted anomaly detection to identify unusual patterns and potential failures before they affect business operations. 

• From fragmented visibility to connected intelligence – Data quality, lineage, infrastructure performance and workload behaviour are increasingly being considered together rather than as isolated monitoring activities. 

• From data governance to operational trust – BCG’s 2026 research, Agentic AI Is Rewriting the Rules of Data Risk Management, highlights data quality, lineage and provenance as core risk priorities as AI agents gain the ability to access and act on enterprise data. 

McKinsey also emphasises embedding data quality checks, security controls, lineage tracking and observability directly into data pipelines to support AI at scale. 

These developments suggest that differentiation is increasingly about how effectively organisations interpret operational signals and translate them into timely decisions. 

4. Finding the right balance

For organisations building on Databricks, the next step may look different depending on the scale and complexity of their data environments. 

Some may find native monitoring capabilities sufficient for their current requirements. Others managing interconnected systems, growing workloads and enterprise AI applications may benefit from additional observability capabilities. 

The important considerations are the level of visibility required, the complexity of the environment, operational costs and the ability to translate monitoring insights into meaningful action. 

As enterprise data platforms become increasingly central to business operations and AI, the ability to understand their performance is becoming more than a technical consideration. 

Perhaps the next competitive advantage will not simply come from how much data an organisation can process, but from how effectively it can understand, manage and optimise the systems powering its business. 

Is data observability becoming another operational requirement, or could it be one of the capabilities that differentiates the next generation of data-driven enterprises?