Why Observability Is Replacing Rule‑Driven Data “Cleansing” and Why It Matters

Today, nearly every business decision relies on data. Yet, Gartner reports that poor data quality costs organizations at least $12.9 million per year on average — begetting bad decisions, misguiding marketing and resulting in hours of unplanned troubleshooting. Traditional data quality tools were rule-driven and originally designed for static data warehouses and manual extract, transform, load (ETL) pipelines.

Today’s cloud-native environments are too dynamic, distributed and messy to be maintained by legacy tools alone. This is why observability solutions have become critical to ensuring consistent, trusted and quality data.

This first blog of our two-part series explains why continuous data observability is replacing rule‑driven data “cleansing” and why it matters.

First‑generation data quality tools — Informatica Data Quality, IBM QualityStage, Trillium Quality, Talend Data Quality, SAS DataFlux and others — emerged from an era dominated by on‑premises data warehouses. Their workflow was straightforward: profile data, define business rules (e.g., emails must include @, ZIP codes are five digits), run checks during ETL to standardize or reject records, and quarantine non‑compliant data.

This approach excels at address parsing, name matching, deduplication and enforcing simple rules within siloed databases. Scaling these methods in a modern data ecosystem, however, reveals critical limitations:

• Slow deployment: Legacy tools require extensive configuration and deep ETL integration. Complex implementations take months or years and often need highly skilled consultants.
• Rules can’t keep pace: As data sources explode and business requirements shift rapidly, maintaining a catalog of rules becomes unmanageable. Unexpected data types, new schemas and streaming pipelines resist rule-based control.
• Reactive fixes: Quality checks occur at specific stages (e.g., before loading into a warehouse). Data passing initial rules may still contain subtle errors that surface downstream, turning fixes into firefighting rather than prevention.
• Context erasure: Cleansing overwrites the original value, erasing when, why and how bad data occurred. Without historical context, preventing similar issues becomes impossible.

As data volumes and complexity increased, these shortcomings created tangible problems. Inaccurate customer addresses slip through data quality rules, for example, misdirecting shipments and frustrating customers. Rule‑based data cleansing also struggles with schema drift, duplicate records and pipeline bottlenecks that delay fresh data. When data architects migrated environments to the cloud and adopted distributed data architectures, the rule-based approach broke down — sometimes halting pipelines completely.

Modern data landscapes span multiple clouds, data platforms and streaming pipelines. To maintain trust, teams must identify missing records, duplicates and inconsistent formats before they impact the business. Instead of manually writing thousands of rules, data observability platforms leverage AI to automatically learn what "normal" looks like for each dataset, alerting you when something changes. Per Gartner, fewer than 20% of enterprises implementing distributed data architectures used data observability tools in 2024, but adoption is set to reach about 50% by 2026. Observability is rapidly moving from cutting edge to mainstream.

Platforms such as Monte Carlo, Anomalo, Bigeye and Metaplane take a fundamentally different approach:

1. Non-intrusive connection. These tools connect to cloud warehouses (Snowflake, Databricks, BigQuery, Redshift) via read-only APIs instead of moving data around. Teams monitor datasets without copying or exposing sensitive information.
2. Machine learning baselines. Rather than requiring handwritten rules, the system learns typical volumes, distribution ranges and schema patterns, then detects anomalies — missing rows, unexpected spikes or schema changes — against this baseline.
3. Continuous monitoring. Observability tools watch data flows continuously, not just at fixed ETL checkpoints. Freshness checks ensure data lands on time; volume metrics reveal duplicates or missing records; and schema checks catch unexpected column changes.
4. Automated alerting and lineage. Anomalies trigger alerts via Slack, PagerDuty or tickets. Data lineage and impact analysis show where problems originated and which downstream dashboards or models suffer impact, enabling targeted fixes.

These capabilities shift the paradigm from reactive to proactive. Instead of discovering broken data after it pollutes dashboards or AI models, teams catch anomalies early and trace their origin.

It’s important to emphasize here that modern observability does not replace data quality; it augments it. Data quality tools ensure that accuracy, completeness and consistency standards are met, while observability monitors those standards automatically.

The use of AI has become a real differentiator between data quality and data observability tools. In fact, AI makes data observability possible. While legacy data quality cleansing methods remain largely rule-based and retrospective, observability tools rely heavily on AI for proactive, intelligent monitoring and predictive insights.

In short, observability uses AI to detect and prevent data issues dynamically while legacy data quality tools use rules and validations to confirm data accuracy after the fact. We’ll discuss the role of AI in observability in more depth in part two of this blog series.

Barr Moses, co‑founder of Monte Carlo, originally defined data observability through five pillars. These pillars offer a framework for understanding the health of modern data systems:

Observability platforms use these pillars to determine what "healthy" data looks like and catch deviations before they propagate. Unlike legacy data cleansing, they preserve context and history rather than overwriting it. When an anomaly occurs, the platform records when it happened, who (or which process) made the change and how it flowed through the system. This event-based approach,  which tracks both transaction and validity time, allows teams to audit issues, rewind time for forensic analysis and train AI models using actual data instead of sanitized versions.

Recognizing the differences between data quality and observability clarifies why rule driven cleansing falls short of addressing modern needs. As business intelligence organization Atlan puts it: “While data quality focuses on the inherent attributes of data, data observability provides the real-time vigilance needed to maintain these attributes.”

Several critical challenges arise when relying solely on traditional cleansing:

• Historical context vanishes. When pipelines overwrite bad values, the context behind the error — when and why it occurred — disappears. Without this, identifying recurring issues or improving upstream processes becomes impossible.
• AI models get distorted. Machine learning algorithms trained on "cleaned" data may perform well on sanitized samples but fail against messy reality. Observability ensures models learn from actual conditions and handle anomalies effectively.
• Scaling breaks down. Manual rule maintenance cannot match the pace of streaming and event driven architectures. Observability platforms scale automatically with data volume and variety.
• Late detection erodes trust. Catching errors only after they impact dashboards or models undermines trust and wastes resources. Continuous observability shortens the time to detection and recovery.

Legacy tools remain valuable — particularly for standardizing addresses or names and meeting regulatory requirements — but they must be layered with observability for continuous monitoring. Organizations that adopt this approach stand to prevent data downtime, reduce costs and boost confidence in their analytics.

In part 2 of this series, we will explore how to implement data observability successfully, including the rise of data reliability engineers, cloud‑native benefits, best practices and how AI‑powered platforms are shaping the future.