In control theory, observability is a measure of how well internal states of a system can be inferred from knowledge of its external outputs.
Borrowing the definition from control theory, Data Observability is a set of data monitoring measures that can help predict and identify deep issues in data quality through its external symptoms.This approach goes beyond the traditional monitoring capabilities and strives to reduce the time that data is unusable by using intelligent tools that monitor the health of data, check for quality at the pipeline, trace the source of the data issues, and help troubleshoot and investigate issues. The goal of such a system is to reduce the meantime to detect (MTTD) and mean time to resolve (MTTR) data issues.
Data monitoring is a business practice in which critical business data is routinely checked against quality control rules to make sure it is always of high quality and meets previously established standards for formatting and consistency.
Data monitoring is a process that maintains a high, consistent standard of data quality. By routinely monitoring data at the source or ingestion, allows organizations to avoid the resource-intensive pre-processing of data before it is moved.
Data quality is one aspect of monitoring data whereas data observability is an umbrella term that monitors the quality of data, traces the issues in data discrepancies, and provides a platform to troubleshoot data issues.
Anomaly detection is a part of data observability that identifies outliers that deviate from a dataset’s normal behavior. Also known as outlier detection, it is the identification of certain data points which raise suspicions by differing significantly from the majority of the data.