Learn how data quality is different from data observability, see the basics of both concepts, and understand how to handle them successfully in SAP systems.
Data observability and data quality are two similar yet different concepts. The key thing is that both of them are important for informative, valuable insights from the collected data.
In this post, we’ll discuss the differences between data observability and data quality, learn how they contribute to each other, and show how to handle both successfully in SAP systems.
Data quality refers to how accurate, complete, relevant, valuable, compliant, and reliable your data is, and how effectively it can be used in your business. Certainly, data should be high-quality, empowering companies to make relevant, informed decisions, maintain operational efficiency, and comply with regulatory requirements.
Data quality encompasses six key dimensions:
Data observability is the ability to monitor, understand, and maintain the health and reliability of data systems. This is ensured by comprehensive visibility into data pipelines, infrastructure, and usage. Data observability allows organizations to proactively detect, diagnose, and resolve data issues, making sure that all the company’s data remains trustworthy and systems operate efficiently.
Data observability includes the following five pillars:
Understanding these concepts becomes clearer when we examine specific SAP scenarios where data quality and observability challenges commonly arise:
In SAP Material Master (MM), data quality issues often emerge when multiple plants create similar materials with slight variations in descriptions or specifications. For example, "Steel Pipe 10mm" vs "Steel Pipe 10 mm" vs "Steel Pipe (10mm)" represent the same item but appear as different materials, leading to inventory inefficiencies and procurement errors.
Data observability helps by monitoring material creation patterns, detecting duplicate entries in real-time, and tracking data lineage from creation through usage across different SAP modules (MM, SD, PP).
During month-end financial close in SAP FI/CO, data quality problems can delay reporting when cost center assignments are incomplete or GL account mappings contain errors. Missing or inconsistent profit center data can make consolidation impossible.
Data observability provides visibility into the financial data pipeline, monitoring posting patterns, detecting anomalies in account balances, and ensuring all required data flows from operational modules (SD, MM, PP) to financial reporting are complete and timely.
When integrating SAP with external suppliers through EDI or APIs, data quality issues arise from format inconsistencies, missing mandatory fields, or invalid reference numbers. A single incorrect material number can disrupt the entire procurement process.
Data observability tracks these integration points, monitors data volume and freshness from external sources, detects schema changes in supplier data formats, and provides lineage visibility from external systems through SAP processing to final business outcomes.
Both data quality and data observability relate closely to data management, and both serve to ensure data unity, accuracy, and reliability. Still, in terms of “what was first”, data observability is a second-level concept that is based on data quality. In other words, data quality ensures the health of the initial data that enters an enterprise’s system, while data observability is aimed at continuous monitoring and analysis of this data throughout its whole lifecycle.
Because they are closely interconnected, data observability and data quality have some similarities:
Yet, data quality and data observability are different. Despite their core goal being the same, they target different aspects of data management.
First, data quality and data observability have different objectives. Data quality focuses on ensuring data itself is accurate, complete, reliable, consistent, and valid, while data observability emphasizes visibility into the health and reliability of data systems, monitoring pipelines, infrastructure, and usage patterns to detect and fix problems proactively.
Besides, data quality is primarily concerned with data accuracy, completeness, validity, uniqueness, consistency, and timeliness. Data observability expands monitoring to the overall data ecosystem, tracking freshness, distribution, volume, schema changes, lineage, pipeline health, infrastructure performance, and user behavior.
Data quality and data observability also have different approaches. Data quality takes a reactive approach, correcting data once an issue is identified, while data observability usually takes a proactive approach, foreseeing, diagnosing, and addressing data issues before they significantly impact operations.
As may be seen from the name, data quality focuses on the quality and accuracy of data records themselves, while data observability is focused on visibility into the entire data infrastructure: pipeline stability, data movement, transformations, system performance, and usage patterns.
Data quality directly impacts decision accuracy by ensuring that the data is trustworthy. Data observability impacts operational efficiency by reducing system downtime, quickly diagnosing failures, and maintaining continuous data flows.
These two also rely on different techniques. Data quality uses validation rules, cleansing techniques, quality audits, and data profiling, while data observability relies on continuous monitoring, alerts, metrics tracking, anomaly detection, and automated root-cause analysis.
The summary table below will help you determine the key differences between data observability vs. data quality.
| Aspect | Data Quality | Data Observability |
| Focus | Accuracy and fitness of the data itself | Health and status of data flows and pipelines |
| Measurement | Data correctness, completeness, and consistency | Monitoring data freshness, latency, and anomalies |
| Outcome | Reliable and trusted data for decisions | Early detection and resolution of data issues |
| Typical tools/methods | Data cleansing, validation rules, and profiling | Monitoring dashboards, alerts, and lineage tools |
| Impact | Quality of decisions | Efficiency and reliability |
| Approach | Reactive (Correcting errors) | Proactive (Early detection) |
Data quality and data observability issues can occur in many data systems, and SAP is no exception. Finding the right solution to the problem is the key to keeping your SAP environment operational capacity stable and accurate, allowing you to make data-based decisions, and stay ahead of the market.
DataLark, a sophisticated data management solution, focuses specifically on data quality and data observability in SAP and non-SAP systems. Its data management capabilities and user-friendly interface allow companies to successfully maintain data quality and ensure data observability, even without a coding background.
DataLark ensures exceptional data quality within SAP environments due to:
As for the data observability within SAP systems, DataLark offers the following:
Data observability and data quality are different, yet they have a common goal: to ensure data consistency, compatibility, and health, and to make SAP systems process only valuable data that drive results.
We hope that this has helped you to understand the differences and key ideas of data quality and data observability, as well as to find a solution to possible data management issues.