Data observability refers to practices that ensure the quality, availability and reliability of data across the enterprise. It involves a transparent view of data flows that enables the organization to understand, diagnose and manage data health. Data observability also uses telemetry data such as logs, metrics and traces to help organizations detect, triage and resolve data issues in real time.

Data has long been considered one of the most valuable business assets, a form of “currency” that organizations can leverage for competitive advantages. AI and machine learning up the ante, but unknown data quality creates significant risk. That’s why more organizations are implementing formal data observability programs to improve the accuracy of AI outputs and ensure regulatory compliance.

What Is Data Observability?

Poor quality data creates real business risk. Stale, incomplete and inaccurate data can prevent organizations from identifying and capitalizing on new opportunities, such as new customers or market trends. Relying on inaccurate or outdated financial data can lead to misinformed investment decisions, potentially resulting in lost profits or even bankruptcy. Poor data quality can lead to inaccurate financial statements, which can have legal and financial repercussions. 

Data observability aims to prevent these problems by identifying bad data before it reaches users. It includes processes for monitoring data pipelines to ensure that data is accurate, complete and delivered in a timely manner.

Data observability rests on five pillars:

  • Freshness ensures that data is kept up to date to prevent stale data from being used for decision-making.
  • Distribution checks the health of data at the field level to see if it falls within an expected range and identify anomalies or outliers.
  • Volume helps identify unexpected changes in the amount of data being processed, which can indicate issues in the data pipeline.
  • Schema tracks changes in data structure and organization, which can indicate data issues or errors.
  • Lineage provides a comprehensive view of the data’s journey from its sources to its downstream usage, which aids in troubleshooting and understanding the impact of data issues on various processes. 

How Does Data Observability Ensure Data Quality?

While users would know immediately if systems were down, they might never realize that the data they’re using is inaccurate. Data observability tools serve as gatekeepers by continuously monitoring data quality metrics, logs, metadata and lineage. They collect data about the data itself and the processes that handle it, looking for anomalies, deviations from expected behavior and potential data quality issues. 

When anomalies are detected, data observability systems trigger alerts and provide insights into the root cause of the issue. Most data observability platforms also include dashboards and reports to help organizations monitor data health and performance. 

Data observability includes a continuous feedback loop that enables organizations to improve data processes and prevent future problems. The real-time insights from the data observability system are fed back into the data pipeline to create a cycle of continuous improvement, enabling organizations to refine data transformation logic and strengthen validation rules. The feedback loop also enables organizations to address bottlenecks in the data pipeline and optimize system performance.

What Are the Benefits of Data Observability?

Data observability offers several key benefits, including improved data quality, faster issue resolution and reduced downtime. It enables teams to proactively identify and address data issues, minimizing the impact on business operations and allowing for more informed decision-making. The feedback loop ensures that data pipelines are not only monitored but refined, leading to enhanced trust in data and improved business outcomes. 

While data observability is increasingly critical, many organizations face steep learning curves. Technologent’s data team has the expertise to help you plan and implement a data observability program. We can also help you select the right tools to monitor your data and ensure that it’s accurate, up-to-date and complete.