What is data observability?

Data observability is the quality of making data sets and their metrics visible to appropriate stakeholders. Learn reasons data observability is beneficial.

Data is becoming increasingly important to companies as it offers several operational, security, compliance and productivity benefits. Organizations that want to get the maximum value from data must keep the data in good health through the entire data value chain. This is where data observability can be extremely useful to an organization.

What is data observability?

Data observability refers to an organization’s ability to understand the health of data throughout the data lifestyle. It helps companies connect the data tools and applications to better manage and monitor data across the full tech stack.

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One of the core objectives of data observability is to be able to resolve real-time data issues, such as data downtime, which refers to periods where data is missing, incomplete or erroneous. Such issues with data can be extremely costly for an organization as it can lead to compromised decision-making ability, corrupted data sets, disrupted daily operations and other serious problems.

It is a common misconception that the scope of data observability is only limited to monitoring data quality. That might have been true a few years ago, however, with the increasing complexity of IT systems, the scope of data observantly now includes the entire data value chain.

Benefits of data observability

Data observability is a must-have for an organization that seeks to accelerate innovation, improve operational efficiency and gain a competitive advantage. The benefits of data observability include better data accessibility, which means the organization has access to uninterrupted data, which is needed for various operational processes and business decision-making.

Another key benefit of data observability is that it allows an organization to discover problems with data before they have a significant negative impact on the business. The real-time data monitoring and alerting can easily be scaled as the organization grows larger or has an increase in workload.

An organization can also benefit from improved collaboration among data engineers, business analysts and data scientists using data observability. The trust in data is also enhanced by data observability, so an organization can be confident in making data-driven business decisions.

Drawbacks to data observability

Data observability has several advantages for an organization, but there are also some downsides and risks. One of the major challenges of data observability is that it is not a plug-and-play solution, which means it requires an organization-level effort for its proper implementation and use. Data observability won’t work with data silos, so there needs to be an effort to integrate all the systems across the organization. This may require all data sources to abide by the same standards.

Another downside of data observability is that it requires a skilled team to get the maximum value from data observability. This means an organization needs to dedicate resources that have the capacity, experience and skills to observe the data. Several data observability tools, provided by various companies, can help but eventually it will be the responsibility of the data engineers to interpret the information, make decisions and determine the root cause of any data-related issues.

There has been significant progress in using machine learning and artificial intelligence to automate some of the data observer roles and responsibilities, however, there is still a long way to go before data observability can be automated.

Key features of data observability

Freshness

Data observability has up-to-date data, which means there are no gaps or errors in the data. If there is an issue with the freshness of data, it can lead to multiple errors in the data sets as a single gap or inaccuracy can have a cascading effect through to the data sets.

Distribution

Distribution refers to the health of data in terms of whether the data is within the accepted range. Data observability checks whether there is a gap in actual data value and expected value.

Volume

This refers to the amount of data in a database or file. Data observability can check the health of the data by checking whether the data intake meets the required threshold.

Schema

Schema is the structure of data that should meet the requirements of the database management system. Data observability allows for real-time monitoring and regular auditing to ensure the good health of data.

Lineage

Data observability can be used to check data lineage to determine if any downstream or upstream consumers were affected by issues in the data pipeline.