System and method of identifying event as root cause of data quality anomaly

Abstract

Embodiments detect and predict data disparity issues in data warehouses. Embodiments derive meaningful insights about the events occurred prior to the data disparity and correlate the events to understand the root cause of the data disparity (or the root cause of an alert generated as a result of detecting the data disparity). Embodiments either take or recommend actionable measures to prevent further occurrences of the event identified as the root cause. According to various embodiments, when the monitored data is transaction data (e.g. transaction volume, transaction amount, transaction processing speed, etc.), internal events (e.g. data job failures, job delays, job server maintenances) or external events (e.g. seasonal holiday events, natural calamities) may cause a dip or spike in the transaction data resulting in a data quality anomaly (i.e. a data disparity).