How to Use Data Observability to Improve Data Quality and Ensure Proper Flow of Data

It is impossible to run a successful organization without access to high-quality data that will inform decisions across the business. And although there are plenty of ideas—and tools—around how to ensure business users have the right data at the right time, there still needs to be a mechanism in place that ensures the data is, in fact, the quality that you need it to be.

This becomes increasingly more difficult when you’re working with tens, if not hundreds, of different data sources that require the development and maintenance of countless numbers of data pipelines, data storage solutions, data warehouses, databases and… well you get the point—the complexity of these systems is large. Enter data observability.

What Is Data Observability?

Data observability is a broad category of activities that allow you to maintain a constant pulse—to monitor, track, and triage any breakdowns in your data pipelines and workflows—in near real time. These activities allow data engineers and data analysts to quickly identify issues should they arise but, more importantly, they are designed to allow for proactive issue resolution before downstream data consumers are impacted.

Data observability is not performed using a single tool, but rather a mix of different activities that when combined, allow data engineers to identify, troubleshoot, and resolve issues quickly.

It is critical to implement this foundational component before your organization’s data complexity grows. Without healthy, reliable, accurate, and timely data, the organization risks data quality issues—resulting in erosion of trust and disruptions in decision-making capabilities across the organization.

What are the Activities Needed for Data Observability?

Data observability activities include monitoring, logging, comparisons, tracking, alerting, and analysis and allow data engineers to stay on top of any data issues before they arise, giving the developer enough time to address them before they lead to bigger problems.

Data observability activities—monitoring and alerting, logging and tracking, comparisons, and analysis—enable data teams gain powerful information on the status, quality, durability, and wellbeing of their data ecosystem.

• Monitoring and Alerting allow for a user, like a data engineer, the ability to analyze running pipelines or workflows to ensure data is flowing as expected. If a failure or some other event occurs, a descriptive notification will be sent to the appropriate party.
• Logging and Tracking allow a user to review running and previously run pipelines and workflows with status, start and end times, duration, etc. to inform the user of the system’s health.
• Comparisons allow a user to compare changes to a data pipeline to ensure proper output and health.
• Analysis allows a user to see trends within pipelines and workflows to ensure the developer can stay ahead of any growing problems. Another part of analysis is allowing the user to analyze the data outputs to ensure the proper information is being generated.

Like the infinity stones from Marvels comics, when these activities are combined, data teams gain powerful information on the status, quality, durability, and general wellbeing of their data ecosystem. As data observability advances, additional activities may be added, further increasing data quality within an organization.  

How Does Data Observability Improve Data Quality?

There isn’t one singular cause of bad data quality—it could be from a number of things including business process issues, lack of data standards, etc. And data quality can’t be fixed with just technology alone—you need people and processes, too. Data observability, however, enables organizations to address each dimension of data quality based on the five pillars in which it stands.

• Freshness refers to the concept of having the latest available data as quickly as possible without any gaps. This pillar ensures the completeness of data.
• Distribution relates to the data’s attribute level health. Are the metrics derived from the data’s attributes falling within an acceptable range of values? This pillar improves the accuracy and validity of the data.
• Volume is the amount of data in the source and target. As the source data travels down the data pipeline, the record count may change based on filtering and join logic. Are we extracting several thousand records and loading nothing? Are we loading a million records when only a few thousand is expected? This pillar ensures completeness and uniqueness.
• Schema refers to the structure of the data. At times, fields are changed (field name or datatype), removed and added (enriched). These changes can affect downstream processes. This pillar ensures consistency, accuracy, and validity of the data.
• Lineage is the path the data takes, often the most important aspect of data quality. Similar to a family tree, lineage provides the traceability of the data to its origin. Lineage is a holistic pillar; it touches on every dimension of data quality as it allows data teams the ability to trace any error throughout the data ecosystem.

These pillars help to build the foundation of a strong data-driven organization by enabling data teams to deliver high quality data across the business.

Why Organizations Should Invest in Data Observability

With tight budgets, limited resources, and short timelines, why should your organization invest in data observability? It may not be as attractive as a new business intelligence (BI) tool or as impressive as a new data warehouse, but that is the point of data observability—the pipelines and workflows should just work.

While pipeline failures and data issues aren’t always predictable, they will happen and implementing tools and processes to identify those earlier, and quickly, are going to pay dividends in the future.

Implementing data observability activities in your organization’s data ecosystem has the following benefits:

• Increased Trust: Arguably the most important aspect in a data-driven organization is trust. If data teams can proactively resolve issues, triage them quickly, or communicate any impacts to downstream processes/users, the trust in the data increases. With increased trust comes increased use and, since data observability increases data quality, then decisions are made more accurately with beneficial outcomes.
• Reduces Downtime: Knowing when and where something breaks can be one of the most time-consuming endeavors when an issue occurs in a data pipeline. With data observability, it is easy for a data team to identify where an issue occurred in near real-time, reducing downtime. For organizations with service-level agreements, this is even more important.
• Increases Accuracy: Transformations in complex enterprise reporting environments are common and knowing if data changes outside of expected ranges or values is easy when data observability activities have been implemented. Engineers can be alerted immediately after a pipeline is finished if there are any unexpected results, triage can occur, and accuracy can be ensured for report users.
• Data Lineage: If you’ve ever received the question “Where does this data come from?” you know it’s sometimes very difficult to answer. With data observability, knowing where data is sourced and what transformations occur is easy. Those questions go from hour-long investigatory adventures to minute-long searches.

Talk to an expert about data observability.

What Data Observability Tools Are Available?

Since data observability is a series of activities that occur in various places within your organization’s data stack, there is not one out-of-the-box solution. Most modern data and analytics tools used today have some features already built in to help with alerts, monitoring, and analysis. There are also tools that help bridge the gap for the remaining activities, including:

• Monte Carlo: This is an end-to-end tool that is used to monitor data pipeline failures, helping to avoid downtime and ensure data completeness.
• Databand: This tool enables data engineers to work more efficiently in a complex modern infrastructure.
• Datafold: This is a data observability tool that helps data teams monitor data quality through diffs, anomaly detection, and profiling.

In addition to specific data observability tools, there are also some best practices that can be employed to enable other tools to perform data observability tasks, including:

• Alerting: When picking a tool, make sure that there is some basic level of alerting, especially when something fails. You cannot fix a problem if you don’t know there is a problem.
• Monitoring: Ensure that there is an ability to monitor pipelines or transformations. Knowing that something is running and being able to monitor it allows your data engineers to communicate the progress to users and to another team member. This can help with development and saving time.
• Logging: Ensure the tool has logging, especially error, warning, and even meta data type logging. Being able to read error or warning logs helps ensure resolutions more quickly and provides engineers information to build better fault-proof pipelines in the future. Metadata logging allows additional automated alerting for things like volume and completeness.
• Lineage: If a tool has automated lineage documentation, it should be considered. It helps data engineers and data analysts know where the data is coming from, what transformations are occurring, and where the data is available helping identify and triage any issues.

Accounting for these four areas will help your organization reduce downtime and improve data quality.

As the complexity of data ecosystems increase through the acquisition of new data sources, tools, and reporting needs, the implementation of data observability activities will only play an ever-increasing role in your data journey.