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Data Observability vs. Data Quality: A Comprehensive Discussion

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In today's data-driven world, organizations rely on data to make critical decisions, gain insights, and drive business growth. However, as data becomes more abundant and complex, ensuring its accuracy, reliability, and timeliness becomes increasingly challenging. This is where data observability and data quality practices come into play.

Data observability is a relatively new concept that focuses on monitoring and understanding data flows within data-driven organizations. It involves collecting metadata from various stages of the data pipeline, including data ingestion, transformation, and consumption. By continuously monitoring data pipelines and applying anomaly detection techniques, data observability enables real-time issue detection and resolution.

Data quality, on the other hand, refers to the process of ensuring that data meets specific standards for accuracy, completeness, consistency, and validity. It encompasses various practices, such as data profiling, data cleansing, and data validation, to maintain data integrity and fitness for business use. In this article, we will delve into a comprehensive conversation between Diana and George Firican, co-hosts of the Lights On Data Show podcast and industry experts Ryan Yackel, CMO of Databand – an IBM Company, and Stephanie Valarezo, Senior Product Manager at IBM Data & AI Data Integration (DataStage). They discussed the significance of data observability and its relationship with data quality. Furthermore, we will highlight the role of Databand.ai, a leading data observability platform, in facilitating effective data management.

Understanding Data Observability

What is Data Observability?

Data observability is a practice similar to application observability but focuses specifically on monitoring and understanding data flows in data-driven organizations. It involves collecting metadata from various areas of the data environment, including data ingestion, orchestration, transformation, and consumption. Anomaly detection plays a critical role in data observability, enabling real-time identification of deviations from expected data behavior.

The Benefits of Data Observability

There are several benefits of data observability, to name a few:
  1. Real-Time Issue Detection: Data observability allows immediate detection of data issues as they occur, leading to reduced Mean Time to Resolution (MTTR). This real-time detection enables data teams to address problems promptly, preventing potential data discrepancies and inaccuracies.
  2. Continuous Approach: Unlike traditional data quality checks that are periodic, data observability takes a continuous approach to monitoring data pipelines. This ongoing monitoring ensures that data issues are identified and resolved as soon as they arise, minimizing the risk of data-related incidents.
  3. Collaboration Between Teams: Data observability fosters collaboration among different teams, including data engineering, data platform, and analytics engineering teams. This collaboration is essential for creating a unified approach to data management and ensuring data reliability and accuracy.

The Relationship Between Data Observability and Data Quality

Data Observability and Data Quality Working Together

Contrary to the misconception of data observability versus data quality, the two are not in opposition but rather complement each other. Data observability helps identify data issues in real-time, while data quality ensures that the data meets specific standards for business use. The relationship between data observability and data quality is one of collaboration rather than conflict, working together to ensure data integrity and usability.

Use Cases for Data Observability

Here are a few use cases where data observability plays a crucial role:
  • Automating Data Quality Tasks: Automation is a key aspect of data observability, helping prioritize issues based on their impact. This automation allows business users to focus on the most critical data issues, ensuring that resources are utilized efficiently.
  • Measurement and Metrics: Data observability can be measured through various metrics, such as data latency, data movement, and anomaly detection. These metrics provide insights into the health of data pipelines and enable data teams to proactively address potential issues.
  • Empowering Business Users: Data observability empowers business users by allowing them to prioritize data issues based on their business value. Business users can determine the significance of specific data and allocate resources accordingly, ensuring that high-impact data is given the utmost attention.

Roles and Responsibilities in Data Observability

Effective data observability requires collaboration between technical teams (data engineering, data platform, and analytics engineering) and business stakeholders. For successful data observability, there shouldn’t only be one side that is responsible for it. The involvement of both parties ensures that data issues are not only identified but also prioritized based on their business impact.

That being said, there the future might see the creation of Data Reliability Engineers (Dr. DREs). This emerging role of data reliability engineer would be responsible for overseeing data observability and data quality practices within organizations. These professionals play a pivotal role in ensuring the overall reliability and trustworthiness of data. Though a major role to play, they would still need the involvement from business stakeholders to get the most benefits out of data observability.

Databand: Empowering Data Observability

Databand is a prominent player in the data observability space, empowering organizations to manage their data effectively. As a comprehensive data observability platform, Databand offers a range of features and capabilities to streamline data management processes. Some key points about Databand include:

  • Automating Data Quality Checks: Databand enables organizations to automate data quality checks, allowing for real-time issue detection and resolution. By setting thresholds and prioritizing issues, business users and data teams can focus on the most critical data problems.
  • Continuous Monitoring and Alerting: With Databand's continuous monitoring capabilities, organizations can proactively detect anomalies and deviations in data pipelines. Automated alerts and notifications ensure that data issues are addressed promptly, minimizing potential data downtime.
  • Collaboration and Business Empowerment: Databand facilitates collaboration between technical teams and business stakeholders. Business users can prioritize data issues based on their impact on business operations, ensuring that valuable resources are allocated efficiently.
  • Augmented Data Quality: The conversation touches on the concept of augmented data quality, wherein data observability plays a core role. Databand's platform embraces this approach, further strengthening the relationship between data observability and data quality.


Data observability and data quality are pivotal components of a robust data management strategy. They work hand-in-hand to ensure data integrity, accuracy, and reliability. By employing data observability practices, organizations can benefit from real-time issue detection, continuous monitoring, and effective collaboration between teams. Databand emerges as a leading data observability platform, offering automation, monitoring, and collaboration features to empower organizations in their data journey. As businesses continue to embrace data-driven decision-making, the significance of data observability and platforms like Databand will only grow, ensuring that data remains a trustworthy and valuable asset. 

If you’d like to listen to or watch the entire conversation, you can do so here:


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