Business users and data consumers often complain that they don’t have confidence and trust in the data their IT team sends them. That they keep discovering hidden risks. The IT team on the other hand is overloaded with the laborious and extremely time-consuming work of coding 1,000’s of data validation rules. Without effective and comprehensive validation, a Azure Data Lake (ADL) becomes a data swamp. DataBuck leverages machine learning to auto recommend and auto code data validation rules. It detects data errors autonomously and measures the Data Trust Scores, that can then be connected to Microsoft Purview catalog. On a single pane of glass, you can see the trustability of all your data on Azure ADL. DataBuck categorizes the data quality errors along the following data quality dimensions: - Completeness: It determines the completeness of contextually important fields. - Conformity: Dataset should contain relevant data and follow certain rules or patterns. This data quality dimension determines conformity to a pattern, length, and format of contextually important fields. - Uniqueness: This dimension determines the uniqueness/duplicates of individual records. Detecting duplicates on ADL is a tedious task, which DataBuck has automated using ML. - Consistency: It determines the consistency of intercolumn relationships (e.g. date of employment must be before the date of retirement). - Drift: It determines the drift of the key categorical and continuous fields from the historical information. - Anomaly: It automatically detects four different kinds of anomalies like data volume anomaly, value anomaly of critical columns, inter-column relationship anomaly and data distribution anomaly.