In today’s era, data powers everything we do: from the processes we build to the tools we use, the decisions we make, the interactions we have, etc. As data becomes the most valuable asset for organizations, organizations must manage it correctly, so they can realize full value. This has led to the need for strong data governance that helps in keeping a check on how data is collected, analyzed, stored, shared, and used. 

But despite the growing focus on data governance, not much is being said or done about observability. So, what is observability? Why does it matter? And how is it connected to data governance? Keep reading to get answers to all this and more.

The Growing Importance of Data Governance

As data security becomes a global concern, data governance aids in the formation of the required policies and procedures to ensure data availability, usability, integrity, and security. It enables organizations to develop a clear understanding of what data needs to be collected, where it comes from, and how it is stored, analyzed, and used. By constantly checking for integrity and consistency, data governance allows organizations to have trust in their data and use it to power their business decisions.

The right approach to data governance can: 

• Give individuals a streamlined way to access data, use common terminology to discuss data, and a standard way to make it meaningful
• Establish a sound data quality framework that creates a shared sense of understanding for better insights and decisions
• Ensure consistency of data across systems, processes, and workflows via standardization 
• Outline clear roles and responsibilities in terms of data management and data access among stakeholders
• Improve compliance with evolving data privacy and protection laws and regulations and enable faster response and resolution to data incidents

The Significance of Observability 

Although data governance helps in establishing the right set of data management policies and procedures, current data stacks are growing beyond boundaries. With data sets now scaling with more data sources, more tables, and more complexity, there is a pressing need to maintain a constant pulse on the health of these systems. Since any amount of downtime can lead to partial, erroneous, missing, or otherwise inaccurate data, organizations need to do better than just implementing a handful of policies.

Data observability enables organizations to cater to these increasingly complex data systems and support an endless ecosystem of data sources and formats. By providing a real-time view of the health and state of data across the enterprise, it empowers them to identify and resolve issues and go far beyond just describing the problem. Observability provides much-needed context to the issue, paving the way for a quick resolution while also ensuring it doesn’t transpire again.

The Tight Connection Between the Two 

As data management becomes complex, data engineers and developers are under immense pressure to keep up with availability, security, and consistency requirements. Since any type of downtime means wasted time and resources while eroding confidence in decision-making, data governance, along with data observability, helps tackle data quality and security problems in a streamlined manner.

Instead of implementing quick workarounds on an ad hoc basis, the two together help in spreading a blanket of diligence and embracing a more holistic approach to data quality. Here’s why the two are tightly connected:

• Data governance helps set the framework for data observability, setting guidelines for what data to monitor and how frequently while also establishing necessary thresholds. Once these guidelines are set, data observability helps handle these activities through continuous identification, troubleshooting, and resolution of problems outlined in the data governance strategy. 
• Data governance and data observability form the foundation of a comprehensive data pipeline. Together, they streamline data management and make maintenance easy through continuous monitoring of data metrics, logs, and traces to predict anomalies and abnormal behavior. 
• Data observability also helps in bridging the gaps that exist in data governance. Since most data governance policies are owned and managed by IT/dev teams that do not have an end-to-end business perspective, data observability ensures a well-rounded, comprehensive, and contextual approach to resolving bottlenecks and driving results.
• Another critical benefit data governance and observability bring to the table is the ability to identify priorities and pain points. Instead of just fixing problems via trial-and-error loops, the two concepts help unearth the root cause of issues and strengthen the data pipeline for long-term results and value. 
• When used together, data governance and observability also help organizations in checking if performance drops below the minimum threshold and alert engineers in advance to implement proactive fixes.

In a Nutshell

The unending volume of data and the snowballing number of unreliable sources has put data quality at stake. With data laying the foundation of every business decision, ensuring consistency, reliability, availability, and security of data has become critical. While data governance helps in setting the right data management policies and procedures, data observability aids in the constant monitoring of data to identify, resolve, and prevent issues, thus strengthening the overall data architecture.

Through active monitoring and in-depth analysis, it enables organizations to understand the causes behind failures. The intersection between data governance and data observability helps define a standard set of data quality metrics, consistently observe them across all databases, and build the foundation of a strong data culture across the business. 

The article has been written by Anurag Sinha, Co-Founder & Managing Director, Wissen Technology (Wissen.com)