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, and more. As data becomes the most valuable asset for organizations, organizations need to manage it properly so they can realize the full value. This has led to the need for strong data governance that provides control over how data is collected, analyzed, stored, shared and used.
But despite the growing attention to data governance, little is said or done about observability. So what is observability? Why is it important? And how does it relate to data governance? Keep reading for answers to all of this and more.
The growing importance of data governance
As data security becomes a global concern, data governance helps in the formation of the policies and procedures required to ensure data availability, usability, integrity, and security. It allows 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 enables organizations to trust their data and use it to support business decisions.
The right approach to data governance can:
- Give people a simplified way to access data, use common terminology to discuss data, and a standard way to make sense of it
- Establish a strong data quality framework that creates a sense of shared understanding for better insights and decisions
- Ensure data consistency across systems, processes and workflows through standardization
- Clearly outline roles and responsibilities in terms of data management and data access among stakeholders
- Improve compliance with evolving privacy and data protection laws and regulations and enable faster response and resolution to data incidents
The importance of observability
Although data governance helps establish the right set of data management policies and procedures, today’s data stacks are growing across borders. With datasets now evolving with more data sources, more tables, and more complexity, there is an urgent need to maintain constant momentum on the health of these systems. Since any downtime can result in partial, erroneous, missing, or inaccurate data, organizations need to do more than just implement a handful of policies.
Data observability enables organizations to respond to these increasingly complex data systems and support an infinite ecosystem of data sources and formats. By providing a real-time view of the health and status of data across the enterprise, it enables them to identify and resolve issues and go far beyond simply describing the problem. Observability provides much-needed context to the problem, paving the way for rapid resolution while ensuring it doesn’t happen again.
The close link between the two
As data management becomes complex, data engineers and developers are under immense pressure to meet availability, security, and consistency requirements. Since any type of downtime means a waste of time and resources while eroding confidence in decision-making, data governance, along with data observability, helps address quality issues and data security in a streamlined way.
Instead of implementing quick workarounds on an ad hoc basis, the two together help spread due diligence coverage and take a more holistic approach to data quality. Here’s why the two are closely related:
- Data governance helps set the framework for data observability, setting guidelines on what data to monitor and how often, while establishing necessary thresholds. Once these guidelines are set, Data Observability helps manage these activities through the ongoing identification, troubleshooting, and resolution of issues outlined in the Data Governance Strategy.
- Data governance and observability form the foundation of a comprehensive data pipeline. Together, they streamline data management and ease maintenance with continuous monitoring of data metrics, logs, and traces to predict anomalies and abnormal behavior.
- Data observability also helps close the gaps that exist in data governance. Since most data governance policies are owned and managed by IT/Dev teams that don’t have an end-to-end business perspective, data observability ensures a complete, comprehensive, and contextual approach to resolving bottlenecks and drive results.
- Another critical benefit that data governance and observability brings to the table is the ability to identify priorities and pain points. Instead of simply solving problems through trial and error loops, both concepts help uncover the root cause of problems and strengthen the data pipeline for long-term results and value.
- When used together, data governance and observability also help organizations check if performance is dropping below the minimum threshold and alert engineers in advance to implement proactive fixes.
In a word
The endless volume of data and the dizzying number of unreliable sources have put data quality at stake. With data forming the basis of every business decision, it has become essential to ensure consistency, reliability, availability and data security. While data governance helps define the right data management policies and procedures, data observability facilitates constant monitoring of data to identify, resolve, and prevent issues, thereby strengthening the overall data architecture.
Through active monitoring and in-depth analysis, it enables organizations to understand the causes of failures. The intersection of data governance and data observability helps define a standard set of data quality metrics, observe them consistently across all databases, and lay the foundation for a strong data culture. data across the enterprise.
The article was written by Anurag Sinha, Co-Founder and Managing Director, Wissen Technology (Wissen.com)