Data Observability: 5 Key Elements, Benefits & Capabilities

What Is Data Observability? 

Data observability stems from the broader concept of observability, which originated in the field of control theory and engineering. Observability is the measure of how well the internal states of a system can be inferred from its external outputs. When we talk about data observability, we are referring to the ability to fully understand the state of your data across a system or pipeline, including its origin, destination, transformations, and dependencies.

With the explosion of data in today’s organizations, being able to observe, track, and understand the behavior and characteristics of the data is crucial. It is the foundation for data trust, enabling data teams to ensure the reliability, integrity, and accuracy of their data, thereby driving effective decision-making and enhancing business outcomes.

This is part of a series of articles about data security.

In this article:

• 5 Elements of Data Observability
• Data Observability vs. Data Quality vs. Data Governance
• Data Observability Benefits
• Key Features of Data Observability Tools

5 Elements of Data Observability 

Here are the five essential elements of data observability:

1. Freshness

The freshness of data refers to its recency or how up-to-date it is. In a fast-paced business environment, stale data can lead to misguided decisions and missed opportunities. The ability to track the freshness of data helps maintain its relevance and usefulness. For instance, if you operate in the retail industry, having fresh data about your stock levels is crucial to avoid under or overstocking.

2. Quality

Generally speaking, data quality means the accuracy and consistency of data. It involves monitoring for anomalies, errors, missing values, duplicate entries, and other data quality issues that can compromise the integrity of your data analysis. High-quality data boosts confidence in the insights generated from it, fostering a data-driven culture within an organization.

3. Volume

Volume is another key aspect of data observability. It refers to the quantity of data being generated, processed, and stored. Monitoring data volume helps data teams anticipate and manage storage needs, optimize processing, and detect unusual data activity that may signal a problem or a potential opportunity.

4. Scheme

The scheme pillar refers to the structure or format of your data. It includes the data types, relationships, and constraints that define how your data is organized and interpreted. By observing the scheme, you can ensure your data is correctly structured for your specific use case, and it also aids in detecting any structural changes that could affect your data processing and analysis.

5. Lineage

Finally, data lineage refers to the full life cycle of your data, including its origin, the transformations it undergoes, and where it ends up. Understanding data lineage is critical for tracing errors back to their source, validating your data for regulatory compliance, and ensuring your data is trustworthy.

Data Observability vs. Data Quality vs. Data Governance 

Data observability, as discussed, is all about understanding the state and behavior of data across the system or pipeline. It is a proactive approach to data management that involves continuously monitoring and analyzing data to ensure its reliability and integrity.

Data quality refers to the condition of data based on factors like accuracy, consistency, completeness, and reliability. It is a component of data observability, but it focuses more on the data itself rather than the processes and systems that handle the data.

Data governance involves the overall management of the availability, usability, integrity, and security of data in an organization. It is a set of processes, roles, policies, and metrics that ensure the effective use of data. Data observability can be seen as a mechanism to support data governance by providing the visibility needed to enforce governance policies and processes.

Data Observability Benefits 

Improved Data Quality

Data observability can help ensure that the data being used is accurate, reliable, and consistent. By providing a clear view into the data’s lineage and dependencies, data observability allows teams to identify and rectify errors quickly, ensuring data integrity. It also enables data teams to establish data quality benchmarks and continuously monitor them, leading to higher data quality overall.

Furthermore, data observability ensures that only relevant and valuable data is used. By tracking data in real-time, it helps filter out redundant and irrelevant data, thereby improving the overall quality of data. In essence, observability promotes higher data quality by enabling proactive data management and continuous quality control.

Faster Troubleshooting

In large data systems, errors and failures are inevitable. These could range from minor discrepancies in data values to major system failures. Data observability provides a real-time view of data, allowing teams to identify these issues as they arise.

By providing a comprehensive view of data’s health and dependencies, data observability allows data teams to understand the root cause of issues quickly. This, in turn, enables faster troubleshooting. With data observability, data teams can detect, diagnose, and resolve problems faster, minimizing downtime and reducing the impact on business operations.

Improved Collaboration

Data observability also promotes better collaboration among teams. In many organizations, data is handled by different teams—each with their own tools, processes, and objectives. This often leads to silos, with each team working in isolation. Data observability breaks down these silos by providing a unified view of data across all teams.

By providing visibility into data’s lineage and dependencies, data observability allows teams to understand how their work impacts others and how others’ work impacts them. This fosters a collaborative culture, in which data teams can work together more efficiently.

Enhanced Customer Experience

Data observability can lead to an enhanced customer experience. Today’s customers expect personalized, timely, and seamless experiences. Delivering such experiences requires a deep understanding of customer data. Data observability provides a comprehensive view of customer data in real-time.

With data observability, businesses can track customer data from various touchpoints, understand how it’s being transformed, and use it to deliver personalized experiences. It also enables businesses to identify and rectify any issues with customer data quickly, ensuring a seamless customer experience.

Key Features of Data Observability Tools 

Data observability is typically facilitated by one or more automated tools. Here are the key features of tools that enable data observability at large organizations.

Collection and Processing of Telemetry from Data Sources

One of the primary features of data observability tools is their ability to collect and process telemetry data across multiple data sources. This allows you to have a comprehensive view of your data, from various sources, in one place. It helps in keeping track of data flow, understanding data patterns, and identifying any anomalies or issues in real-time.

The ability to collect data from multiple sources ensures that you have a complete picture of your data landscape. This is crucial for businesses that rely on data from different platforms and systems. By consolidating this data, observability tools provide a unified view of data, which simplifies analysis and decision-making.

Sampling and processing data can help in identifying trends, patterns, and anomalies. This is particularly useful for companies that deal with large volumes of data. Sampling helps in reducing the amount of data that needs to be analyzed, making the process more manageable. Processing the data makes it ready for analysis.

Comprehensive Monitoring Across the IT Environment

Data observability tools offer comprehensive monitoring of IT systems that have an impact on data, including network, infrastructure, servers, databases, cloud applications, and storage. This level of monitoring ensures that you have visibility into every aspect of your data systems.

With this feature, you can monitor the health and performance of your databases, servers, and other data systems. This can help in identifying any issues that may affect data quality or accessibility. For instance, if a server is underperforming, it could slow down data processing and impact data quality. With observability tools, you can detect such issues in real-time and take corrective action before they escalate.

Visualize Data Trends

Data observability tools typically include advanced visualization capabilities. This feature allows users to create intuitive and interactive dashboards and reports that provide insights into data trends, patterns, and anomalies. Visualization makes it easier to understand complex data sets and helps in communicating findings to stakeholders who may not be technically inclined.

Interactive visualizations enable teams to drill down into specific data points for detailed analysis. This is particularly useful in identifying the root cause of issues or uncovering hidden insights in the data. For example, a sudden spike in data volume can be further investigated through visualizations to determine its cause. These tools often support real-time data visualization, offering an up-to-the-minute view of the data landscape, crucial for making timely decisions.

Automated Alerts and Notifications

Another key feature of data observability tools is the automated alerting and notification system. These systems are designed to automatically detect anomalies, errors, or deviations from expected patterns in your data and notify the relevant team members. This feature is critical in proactive data management, allowing teams to address issues promptly before they escalate into more significant problems.

Customizable alerts can be set based on specific criteria, such as data volume thresholds, unusual access patterns, or changes in data quality metrics. This ensures that teams are only notified about issues that are relevant to their area of responsibility, reducing noise and improving efficiency. For instance, if a data quality metric falls below a certain threshold, the system can automatically alert the data quality team, enabling them to take immediate action to investigate and resolve the issue.

Related content: Read our guide to data security solutions

Data Management with Cloudian Secure Storage

Managing your data requires powerful storage technology. Cloudian storage is easy to deploy and use, letting you store petabyte-scale data and access it instantly. Cloudian supports high-speed data transfer with parallel access (86TB per hour writes with 6 nodes). And it is compatible with popular data observability solutions such as Splunk and Cribl. 

In HyperStore, storage occurs behind the firewall, you can configure geo boundaries for data access, and define policies for data sync between user devices. HyperStore gives you the power of cloud-based storage in an on-premise device.

Learn more about data protection with Cloudian.