Bringing data quality and observability together: the ultimate stack to achieve healthy data

September 16, 2022

For most organizations today, data is their most important asset. Or, at the very least, it has the potential to be. However, in order to reap the full benefits, firms must properly manage their data. This management includes everything from data collection to data maintenance and analysis. A key result of managing data well is that its quality remains constant and does not deteriorate. 

In this blog, we will understand what data quality and observability are, and how we can bring them together to achieve healthy data on which organizations' decisions can rely. 

Content of the blog:

  1. What is Data Quality
  2. What is Data Observability
  3. Why is Data Observability Important
  4. Benefits of Data Observability
  5. Conclusion

What is Data Quality

Data quality determines whether or not data is appropriate for use in making trustworthy business choices. Measuring data quality is crucial to comprehend if you want to confidently use company data in operational and analytical applications. Only high-quality data can fuel correct analysis, which leads to reliable business choices.

There are 9 dimensions on which data quality relies.

  1. Data Observability 
  2. Rules engine based on SQL 
  3. When columns are added or removed from a schema. 
  4. Shapes - Typos & Formatting Errors 
  5. Duplicates - Fuzzy matching, identifying items that are similar but not identical 
  6. Outliers include anomalous records, clustering, time series, and categorical data. 
  7. Anomalies in pattern classification, cross-column, and parent/child relationships 
  8. Deltas for a certain column (s) 
  9. Reconciliation from source to destination

What is Data Observability

Modern businesses must monitor data across several tools and apps, but few have the visibility required to see how those tools and applications interact. Data observability may assist businesses in understanding, monitoring, and managing their data across the whole technology stack. 

The capacity to analyze, diagnose, and manage data health across numerous IT technologies across the data lifecycle is referred to as data observability. A data observability platform assists companies in detecting, triaging, and resolving real-time data issues by utilising telemetry data such as logs, metrics, and traces. Beyond monitoring, observability enables enterprises to increase security by tracing data transfer across diverse apps, servers, and tools. Companies may use data observability to simplify business data monitoring and control the internal health of their IT systems by examining outputs.

In short, data observability: 

  • Monitors the status of business data systems. 
  • Aids in diagnosing the whole data value chain
  • Enables large-scale data quality management 
  • Reduces data downtime 
  • Ensures quick access to reliable data

Why is data observability important? 

Traditional data quality focuses on resolving data problems reactively. It may miss the whole data path across the company while scanning the data sets. Data observability, on the other hand, enables the diagnosis of the whole data value chain. It monitors the health of business data systems proactively, alerting you about any problems in advance.

Benefits of Data Observability

Although there are many advantages of Data Observability, down below we have jotted them down to the four most important ones.

  1. Creating a clear data quality methodology to bring data together and build a shared understanding for better insights and choices. 
  2. Improving data consistency across systems and processes for effective data integration 
  3. Clearly establishing data-related rules and processes to achieve homogeneity across the whole company 
  4. Outlining roles and duties in data management and data access for stakeholders' clarity Improving compliance by enabling quicker data incident response and resolution 

On the other hand, inadequate data governance can stymie regulatory compliance activities, causing organisations to struggle to comply with new data privacy and protection requirements.

What is the difference between data observability and data quality? 

On numerous critical aspects, data observability varies from standard data quality. Data observability enables DataOps and data engineers to track the course of data, go upstream from the point of failure, identify the main cause, and assist in resolving it at the source.

  • Data Quality is defined as trustworthy reporting and compliance [downstream], whereas Data Observability is defined as anomaly detection, pipeline monitoring, and data integration [upstream].
  • The importance of data quality is repairing data mistakes whereas Data Observability reduces the cost of rework, remediation, and data outage by watching data, data pipelines, and event streams.
  • Data Quality deals with finding 'known' concerns whereas detecting 'unknown' difficulties is dealt with by data observability.
  • Data Quality employs human static rules and measurements, whereas data observability uses machine learning-generated adaptive rules and metrics.

How to Put Data Observability with Data Quality into Practice

If you already use data quality tools, consider if they truly enable you to achieve end-to-end quality. Most tools offer relatively limited automation and scalability. Their assistance with root cause investigation and processes is also insufficient. A mature approach to rules uses ML to make them more understandable and shared. As a consequence, data quality operators no longer have to rewrite rules when data travels between environments. They can then manage migration and scaling more effectively. The simplicity with which rules may be shared across multiple systems frees business users from having to worry about coding languages.

The 5-step procedure for improving predictive data quality and observability:

Step1: Connect and scan a diverse set of data sources and processes, including files and streaming data.

Step2: Profiling statistics, including hidden associations and time series analysis, are displayed for each data collection, table, and column.

Step3: Using automated technical rules, create generic DataOps and statistical controls to discover unexpected issues and expand your data quality activities.

Step4: Create adaptable, non-proprietary, explainable, and shareable domain-specific controls using automated and bespoke business rules.

Step5: Integrate data quality procedures into mission-critical business activities. When data quality scores drop, send warnings to the appropriate data owners to rectify errors as soon as possible.


Allowing business users to identify and assign quality concerns means that data quality activities are coordinated across the company rather than being restricted to a small team. Using metadata to complement this technique provides the correct context for quality concerns for impact evaluation. 

A unified data management strategy (keeping data quality and operability together) integrates data quality, observability, catalog, governance, and lineage. It enables you to consolidate and automate data quality operations in order to provide a comprehensive approach to data management and get the most out of your data and analytics expenditures.

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