Data Observability

Data Observability Definition

Data observability is the use of various tools and techniques to automatically monitor, track, alert, and triage all data endpoint activity in real-time in order to prevent data loss and data exfiltration, and improve overall security. Data observability solutions provide unified visibility, identity federation, and granular access controls, thereby enabling users to observe, protect, and control data endpoints. The primary data classes used in observability include plain text logs, numeric metrics, and traces, which show the end-to-end journey of a request through a distributed system.

Data Observability FAQs

What is Data Observability?

Data observability refers to an organization’s ability to understand and manage the health and state of data across multiple tools throughout the data lifecycle. Essentially an evolution of application performance monitoring, observability adds a deeper level of analysis by aggregating the continuous stream of performance data from a highly distributed architecture. The goal of advanced data observability tools is to enable users to intercept all data endpoint requests coming from human users, applications, and third party tools, thereby helping users determine who is accessing which data and whether or not the data is being manipulated. Observability also helps to eliminate blindspots where troubleshooting failures, performance degradations, and security issues are concerned.

Data observability covers an umbrella of methods and technologies that facilitates the identification and resolution of data issues in real-time. Observability paints a multi-dimensional picture of an organization’s entire data value chain, giving deeper insights into system performance and data stack health and quality, and measuring to what degree the data supports business requirements and objectives. Primary goals include decreasing data downtime incidents and ensuring that the organization is compliant with the highest security standards.

Data Observability Explained

How Does Data Observability Work?

Data observability brings visibility and connectivity to the activities that data teams are already performing. For many organizations, observability is siloed. Activities like monitoring, alerting, tracking, analysis, logging, and comparisons are performed by different data teams with different standardized logging policies and business rules without a centralized dashboard where data can be reported and viewed across teams, and without a way for users to monitor how the data is being accessed or manipulated. Observability works to solve these issues by enabling teams to standardize and centralize these activities, providing full awareness and granular visibility of the full data landscape across all teams. 

Once an organization has embraced and adopted the observability culture, and standardized data and data observability platforms have been implemented, there are specific metrics that should be tracked for different parts of the observability framework:

• Operational Health: delays, execution metadata, pipeline state
• Dataset Monitoring: availability, changes in schema, freshness, volume metric for both data at rest and data in motion
• Column-Level Profiling: anomaly detection, summary statistics
• Row-Level Validation: business rule enforcement, elimination of bad data 

Why is Data Observability Important?

According to a prediction made by Gartner, “by 2024, 30% of enterprises implementing distributed system architectures will have adopted observability techniques to improve digital business service performance, up from less than 10% in 2020.” So while the data observability market is relatively nascent still, it is picking up steam quickly and experts predict it will continue to do so. 

The advantages of data observability are numerous and varied:

• Individual developers and data engineers benefit from the visibility that observability gives them of their whole data architecture. 
• Teams benefit from the collaborative view of the environment; they can also improve their understanding of the data mesh layout, health, and performance.
• Businesses benefit from having access to tools that enable them to quickly pinpoint, diagnose, troubleshoot, and resolve issues, such as suspicious or usual activity, unexpected downtime, broken pipelines, redundant data, and more. 

Data observability companies can help businesses create trusted data by improving data reliability and increasing the usefulness, completeness, and quality of the data. Data observability also facilitates faster delivery of data with full context, which inspires teams to make better, more accurate, data-driven decisions and decreases downtime incidents. 

Arguably the most important advantage of observability is data security. As data landscapes grow, access points also grow in number, increasing vulnerability points. It is more important than ever that all authorized eyes in organization have a clear, comprehensive, and granular view of their data so that there are no blindspots when it comes to troubleshooting failures, performance degradations, and security issues.

Data Observability vs Data Quality

Data quality refers to the suitability of a data set to serve its intended purpose. Data quality solutions, such as rules-based monitoring systems and AI-powered technologies, are used to  track real-time data to detect data quality issues and anomalies. Anomaly detection triggers an alert, an intervention, and a solution. This process of monitoring, diagnosing, and fixing data quality issues is data observability. 
While data quality measurements are based on quality characteristics such as accuracy, completeness, consistency, timeliness, uniqueness, and validity, data observability monitors the overall health of data systems, and works in tandem with data quality to identify and prevent data-related issues. Data observability can help improve data quality by giving insight into big-picture data pipeline issues, and guarantees the reliability and usefulness of the whole data collection and delivery system.

How to Implement Data Observability

Implementing data observability involves the organizational adoption of both observability philosophies and technologies. All teams need to embrace end-to-end ownership and accountability, and have access to infrastructure that enables them to speak a common language and openly communicate about issues. This requires libraries for API and data management and data quality, source code tracking, data versioning, and CI/CD processes. 

Once you’ve laid the groundwork, you can implement your observability strategy. Standard implementation practices include: 

• Identify your organization’s key objectives in order to develop the most effective observability strategy.
• Once you’ve determined your business goals, you can identify which metrics to focus on. 
• Adopt event logging tools to record occurrences such as instances of unplanned downtime, critical system failures, and traffic overloads.  
• Leverage request tracing tools to pinpoint precisely where a problem occurred. 
• Compile aggregated observability data into straightforward visualizations that teams can easily share with each other. 
• Select a data observability platform that fits your organization’s specific needs. These tools are available in both proprietary and open-source data observability options.

What are Data Observability Best Practices?

Implementing observability technologies helps facilitate easy data analysis and quick actionable insights that drive better, data-driven decisions, which will ultimately lead to healthier, more secure data and more productive, confident data teams. Implement these data observability best practices to ensure that you can fully trust your IT platform’s outputs:

• Audit Your Platform: A discovery engine should be used to automatically update the platform as additions or deletions of resources are made. This will make it easier to identify all possible data sources. 
• Standardize Your Data Logging: Use the Simple Network Management Protocol to provide a common language for sharing information.
• Prioritize Security in a Data Analysis Tool: The data analysis tool you select should be capable of identifying zero-day attacks and early-stage problems at any level across an IT platform. Some data security and data governance tools combine these capabilities with observability offerings.
• Leverage Automated Remediation: Integrating observability systems into automated remediation systems ensures there are tools in place to automatically perform tasks like system upgrades and patching, freeing up IT professionals to focus on more important issues. 
• Make Sure Data Can Be Aggregated, Centralized, & Filtered: Ensure that your data observability platform has been designed not only as a space where data can be aggregated and centralized, but also to filter out excess data that would otherwise slow down data analysis. 

Streamline Reports & Feedback: System admins need to be able to quickly report their findings to the right people in a concise manner. To achieve this, a feedback loop should exist between observability systems and the help desk. Issue reports should be delivered to IT in real-time, and business impact reporting should be simple and easily communicate the bottom line to anyone.

Does Cyral Offer a Data Observability Solution?

Cyral has built an innovative, stateless interception technology that enables real-time logs, metrics, and traces for all data activity without impact to performance and scalability providing unprecedented observability for the data cloud. Giving your organization higher resolution data activity metrics is one of many ways Cyral helps answer questions about the data layer, like understanding where your cloud data usage is highest or finding the root cause of an ETL slowdown. Find out more about Cyral’s Data Observability solution or read our white paper on Using Observability to Detect Data Exfiltration.