Data Observability

Data Observability Overview

Data is the lifeblood of modern organizations. It powers decision making, innovation, customer experience, and business performance. But data is also complex, dynamic, and distributed across multiple sources and systems. How can you ensure that your data is accurate, complete, and available when you need it?

That’s where data observability comes in. Data observability is the ability to understand, manage, and improve the health and usability of data and data systems throughout the data lifecycle. It involves collecting and correlating data from different sources and tools, such as logs, metrics, and traces, to identify and resolve data issues. Data observability is important for the data quality landscape because it helps you to optimize data pipelines, ensure data quality, reduce downtime, and enhance security. Data observability tools employ automated monitoring to provide data health insights to proactively detect, resolve, and prevent data anomalies.

By adopting a data observability tool, you can gain operational advantages such as:

  • Increased confidence in your data: You can trust that your data is fresh, consistent, valid, and reliable for your use cases.
  • Reduced costs and risks: You can avoid data errors that can lead to poor decisions, lost revenue, compliance issues, or reputational damage.
  • Improved productivity and collaboration: You can spend less time troubleshooting data issues and more time on value-added tasks. You can also share data insights with your stakeholders easily and transparently.
  • Enhanced innovation and agility: You can leverage your data to discover new opportunities, test new ideas, and respond to changing needs faster.

With Blindata Data Observability features you can:

  • gain insights on the shape of your data assets by sharing data profiling information within the data catalog
  • automate anomaly detection processes by speeding up common data ops and quality checks that usually are neglected
  • lower the risk of troubles by taking a proactive approach to data monitoring
  • automatically track issues and incidents for performance monitoring over time


Terms Definitions
Monitor A monitor refers to a comprehensive assembly of profiling metrics and serves as the central configuration point for scheduling both profiling and anomaly detection processes. It acts as the control hub for overseeing data patterns, enabling the user to set parameters, and initiating actions based on the identified anomalies. Monitors play a pivotal role in maintaining the integrity and reliability of the data asset by continuously observing and analyzing the specified metrics.
Metric A metric is a quantifiable measurement used in the process of profiling and anomaly detection. It represents a specific aspect of data or system behavior, providing valuable insights into its characteristics. Metrics serve as the foundation for monitoring and assessing the normal state of a data asset. Examples include distinct count, min, max, average values, null percentage, and any other measurable quantity relevant to the profiling objectives.
Forecast A forecast refers to a predictive analysis or projection of future values for a specific metric used in anomaly detection. By analyzing historical data and patterns, forecasting provides an estimate of expected values, allowing for proactive identification of potential anomalies before they occur. Forecasts play a proactive role in enhancing the effectiveness of anomaly detection systems by providing a baseline for comparison with incoming data.
Anomaly An anomaly refers to an unexpected or irregular deviation from the established normal behavior or pattern within a system. In the context of profiling and anomaly detection, anomalies are instances where observed metrics or data points significantly differ from the anticipated values. Identifying anomalies is crucial for detecting potential issues, irregularities, or security threats, prompting further investigation or corrective actions.
Incident An incident is a collection or grouping of anomalies that share common characteristics or are interconnected. It represents a more substantial occurrence that may have a broader impact on the system or organization. Incidents are typically managed and addressed as a whole to understand the underlying causes, assess the severity, and implement appropriate responses or countermeasures.