One of the most important aspects of data quality is being able to identify anomalies within your data. There are many ways to approach this, one of which is to test the data against Benford’s Law. This blog will take a look at what Benford’s Law is, how it can be used to detect fraud, […]
Identifying outliers and errors in data is an important but time-consuming task. Depending on the context and domain, errors can be impactful in a variety of ways, some very severe. One of the issues with detecting outliers and errors is that they come in many different forms. There are syntactic errors, where a value like
Outlier Detection – What Is It And How Can It Help In The Improvements Of Data Quality? Read More »
With Snowflake connectivity, SQL rule wizard and the ability to bulk assign data quality breaks, ADQ 1.4 makes end-to-end data quality management even easier.
