Data Quality Score and Correction Cost

Data quality is often a subject we overlook. In a world where data is so abundant and complex, postponing the cleansing of this pond means it will increasingly stink and turn into massive piles of garbage. Let's take a look at how the accuracy rates of an average piece of data are calculated and see how much it costs us to correct them.

Data Quality Score (DQS)

The Data Quality Score is a metric that measures the overall quality of a dataset. It usually takes a value between 0 and 100, where 100 represents perfect quality and 0 represents the lowest quality. The DQS can be calculated by evaluating various data quality dimensions such as accuracy, completeness, consistency, timeliness, and uniqueness.

For example, suppose 95% of customer records in a data warehouse are complete. Also, assume that 90% of the records are accurate (free from misspellings or incorrect information), 98% are consistent (no conflicting information across different tables), and 99% are current. For uniqueness, assume that each customer record is unique and there are no duplications. The Data Quality Score can be calculated as follows:

This score indicates that the overall quality of the data in the data warehouse is quite high.

Corrected Data Cost (CDC)

The Corrected Data Cost represents the total effort cost to correct erroneous data. This cost includes the time and resources spent on detecting errors, correcting them, and verifying their correctness. Suppose there are, on average, 1000 erroneous records that need correction in a data warehouse, and each correction process takes an average of 5 minutes. If the hourly cost of a data analyst is $50, then the Corrected Data Cost can be calculated as follows:

This calculation shows that the cost of correcting data quality issues is significant, and maintaining high data quality can lead to cost savings in the long term.

In data warehouse projects, I wanted to demonstrate the importance of data quality and how we can optimize the return on investment with a simple calculation. Wishing everyone days full of data