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In control theory, observability is a measure of how well internal states of a system can be inferred from knowledge of its external outputs. Observability is the ability to infer the internal state of a system from its external outputs. In the context of software systems, observability refers to the practice of making a system’s internal state visible through metrics, logging, and tracing. This allows developers and operators to understand the behavior of a system, diagnose problems, and make informed decisions about how to improve it.

Data monitoring is a practice in which data is constantly checked for pre-defined or ML calculated data metrics against acceptable threshold to alert on issues. Output of monitoring is usually an alert or a notifications but could be an automated action. For example : Notify user when the below policy fails.

Data observability refers to the ability to understand and monitor the behavior of data within a system, while data quality refers to the accuracy, completeness, consistency and reliability of the data. Data observability allows to see how data flows through the system and identify any errors or discrepancies in the data, it enables to detect problems early, understand the overall health and performance of the system and take actions accordingly. Data quality, on the other hand, is more focused on ensuring that the data is accurate, complete, and consistent. This includes identifying and correcting errors in the data, removing duplicate or inconsistent information, and ensuring that data is entered and stored in a consistent format.They both provide visibility into the health of the data and can detect data quality issues against predefined metrics and known policies. Data observability takes data quality further by monitoring anomalies and business KPI drifts. Employing ML has made data observability tools a more intelligent system with lower maintenance and TCO as compared to what traditional data quality was capable of doing.

Data outliers and data drifts are both terms used to describe unusual or abnormal data points, but they refer to different types of anomalies. Data outliers are data points that are significantly different from the other data points in the dataset. These data points can be caused by measurement errors, data entry errors, or other issues, and they can skew the overall statistics and patterns of the data. Outliers are often identified by statistical methods such as mean, standard deviation, and quantiles, and can be removed or handled in different ways. Data drifts, on the other hand, refer to changes in the statistical properties of a dataset over time. These changes can be caused by changes in the underlying process, such as changes in the data collection methods or changes in the system.

There are several techniques for finding anomalies or unusual data points within a dataset, some of which include: ‍Statistical methods: use statistical properties of the data such as mean, standard deviation, and quantiles to identify unusual data points. For example, data points that fall outside of a certain number of standard deviations from the mean are considered outliers. ‍Clustering: use techniques such as k-means, density-based clustering, or hierarchical clustering to group data points together and identify data points that do not belong to any cluster. ‍Classification: train a classifier to learn patterns and behaviors in the data, and use it to identify data points that do not conform to those patterns. ‍Rule-based methods: use a set of predefined rules to identify anomalies, such as looking for data points that fall outside of a certain range or that do not conform to certain constraints. ‍Machine Learning: use machine learning algorithms such as Autoencoder, Isolation Forest, Local Outlier Factor, which are designed to find anomalies in a dataset. Finding anomalies in data is not a one-time process, it’s an ongoing process that requires continuous monitoring, updating, and maintenance to ensure that the data remains accurate, complete, and consistent over time. Data Observability tools like Telmai are natively designed to automatically find anomalies in data.

Data anomaly monitoring is the process of continuously monitoring data to detect unusual or abnormal data signals, also known as anomalies. Anomalies could indicate problems with the data pipeline or data collection process, or they can also reveal valuable insights about change in underlying business or process.