Data Quality Shift — Part 2: Embracing Observability for Lasting Data Excellence

In the first part of this series, we examined how legacy data‑quality tools rely on pre‑defined rules and manual cleansing and why those approaches struggle in today’s cloud‑native, distributed environments. Data quality focuses on accuracy, completeness and consistency, whereas data observability emphasizes real‑time vigilance and proactive detection. Observability platforms learn what healthy data looks like and automatically surface anomalies — missing records, schema drift or delayed arrivals — before they harm downstream analytics.

This second part dives deeper into how organizations can implement observability effectively, the emerging role of data reliability engineers, cloud‑native benefits, best practices for success and the integration of AI with modern platforms.

Although observability is often framed as a replacement for data quality, they are mutually reinforcing. Atlan highlights that data quality ensures the data’s inherent attributes are correct, while observability provides continuous monitoring and alerts when those attributes deviate.

What we find in practice:

• Data quality defines the target,  such as acceptable ranges for transaction amounts or the required completeness of customer profiles.
• Observability continuously checks against those targets using machine learning and statistical baselines. It helps catch unknown issues that rules may not anticipate.
• Both feed into governance and testing.  Observability alerts inform data stewards, while data quality rules enforce compliance for critical business processes.

By combining rule‑based checks with observability, organizations create a feedback loop. When an alert surfaces, teams can update rules or refine data pipelines. This hybrid approach yields more reliable analytics and models.

As data pipelines become critical infrastructure, a new specialty has emerged: the data reliability engineer (DRE). DREs apply principles borrowed from site reliability engineering to ensure that data pipelines, storage and retrieval systems remain robust, scalable and available. Data reliability engineering is defined as a specialization within data engineering focused on ensuring that data systems are reliable, robust and scalable.

The role of a DRE includes:

Source: Atlan’s Data Reliability Engineer: 9 Skills to Help You Become One!

DREs serve as the guardians of data integrity and availability. They bridge technical teams and business users, enabling shared accountability for data quality and reliability.

Legacy data quality software was built for on‑premises environments and required custom installations and months of configuration. Modern data observability platforms are cloud‑native, often delivered as Software‑as‑a‑Service (SaaS), with plug‑and‑play integrations.

For example, Metaplane — an observability platform now part of Datadog — offers a 15‑minute setup with quick integration and automated monitor configuration. Usage‑based pricing lets teams pay only for what they monitor. Such tools natively integrate with Snowflake, Databricks, Microsoft Fabric, BigQuery and other cloud warehouses, making observability accessible to organizations of all sizes.

Cloud‑native observability provides several advantages:

• Fast time‑to‑value. Teams can start monitoring critical pipelines in minutes instead of weeks. Quick deployment reduces the learning curve and initial investment.
• Elastic scaling. SaaS platforms automatically scale up or down based on data volume and complexity. Organizations pay for what they use and avoid overprovisioning infrastructure.
• API‑first integrations. Modern platforms integrate with scheduling and orchestration tools (e.g., Airflow, dbt and CI/CD) and incident management systems (e.g., PagerDuty and Jira). This integration embeds data quality into engineering workflows.
• Security and compliance. Vendors often obtain certifications (e.g., SOC 2 and ISO 27001) and support data access controls, making them suitable for industries with strict regulatory requirements.

For organizations still using on‑premises or hybrid stacks, observability vendors offer connectors that monitor both cloud and legacy environments. However, as the Sifflet comparison notes, some tools still require significant configuration and may lack deep infrastructure monitoring, highlighting the importance of choosing a platform aligned with your needs.

Transitioning to observability can be daunting. The following practices help ensure a successful rollout and maximum value:

1. Define “good data” early. Identify critical pipelines and establish metrics for freshness, volume, schema integrity and distribution. Align these metrics with business objectives and regulatory requirements.

2. Start with high‑impact pipelines. Monitor data flows that feed executive dashboards, AI models or revenue‑generating applications. Demonstrating quick wins builds support for broader adoption.

3. Automate alerts and integrate them into workflows. Use Slack, PagerDuty or issue trackers to notify the right people when anomalies occur. Automatic notifications help teams react quickly and assign ownership.

4. Pair observability with governance and testing. Observability is most effective when layered with clear data governance policies and automated tests. DREs and data stewards should collaborate to update rules and documentation based on observations.

5. Embrace an event‑driven mindset. Preserve the context of anomalies using event logs and bitemporality — recording when an event occurred in the real world and when it was recorded. This preserves lineage and enables forensic analysis.

6. Invest in people and culture. Train data engineers, analysts and business users on observability concepts. Encourage a culture where data reliability is everyone’s responsibility, not just the domain of a quality team.

Real‑world examples highlight the benefits of these practices:

• A financial institution used anomaly detection within observability tools to reduce fraud‑detection time and improve operational resilience. Monitoring metrics and setting automated alerts allowed the team to identify suspicious transactions quickly, preventing losses and ensuring compliance.
• A technology company implemented distributed tracing and observability across its microservices architecture. Real‑time metrics and centralized logs enabled engineers to pinpoint latency issues before they affected users, reducing downtime and improving customer satisfaction.
• A retail startup adopted a cloud‑native observability platform with rapid deployment and usage‑based pricing. The ability to monitor critical metrics in minutes allowed the company to identify data issues during a product launch, avoid bottlenecks and maintain a positive customer experience.

These cases show that observability is not just about detecting errors; it’s about building reliability into the data product lifecycle.

Modern observability isn’t limited to monitoring. AI‑powered features help teams focus on the most important issues. Many platforms now provide smart anomaly detection, context‑aware root‑cause analysis and adaptive thresholds that adjust to seasonal or business changes. As data platforms such as Snowflake, Databricks and BigQuery add native quality and observability capabilities, standalone tools will evolve into glue that integrates seamlessly with orchestration, version control and incident‑tracking systems.

Additionally, AI‑driven platform integrations — for example, combining observability with customer relationship management (CRM) systems like Salesforce or productivity suites like Microsoft Azure — enable predictive analytics and personalized recommendations across the organization. By connecting data health with business outcomes, these integrations provide a more complete picture of how data quality affects customer experiences and revenue.

• Start with observability. Modern platforms deliver rapid visibility with minimal setup. You don’t need months of configuration to begin monitoring your critical pipelines.
• Hybrid approach. Use legacy data quality tools for tasks they handle well — standardization and compliance — while leveraging observability for continuous monitoring and anomaly detection. The combination delivers both control and agility.
• Invest in context and learning. Preserve the history of anomalies and learn from them. Data reliability improves over time when teams study patterns and adjust pipelines accordingly.
• Empower people. Establish or hire data reliability engineers to champion observability, but ensure that data reliability is embedded in the daily work of engineers, analysts and business users.
• Futureproof your data. As cloud‑native architectures and AI adoption accelerate, observability will become a core component of the data stack. Don’t let yesterday’s rulebook limit your ability to innovate.

By embracing observability alongside traditional data quality, organizations can build trustworthy, resilient data systems. In an era where data fuels AI and business strategy, this shift isn’t optional — it’s essential.