Data are an organization’s most valuable asset. However, too much data are inaccurate, incomplete, irrelevant, obsolete, not well defined, or otherwise not fit for use. Bad quality data can lead to operational inefficiencies, poor decision making, non-compliance issues, and lost revenue.

According to Gartner’s research, the average financial impact of bad data quality on organizations is $9.7 million per year. Hence, it is imperative for organizations to manage their data efficiently, and ensure that their data are of high quality.

Data profiling is about discovering and thoroughly reviewing the underlying data available to ascertain the characteristics, the data patterns, and essential statistics about the data¹. It is essentially the first step in climbing the ladder of data quality, and it provides a proactive approach to understanding an organization’s data. In this article, we discuss the different data profiling methodologies that can be used to assess the quality of data in an organization.

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