As per the old axiom, you cannot improve something if you cannot measure it. Therefore, measuring the data quality is highly essential as it helps in assessing your organization’s ability to reach goals and solve challenges. Data quality must be measured continuously to establish a baseline and trend. In this article at Staceybarr.com, Stacey Barr explains how to improve data integrity in a way that doesn’t ruin our KPI interpretation.
How to Measure Data Integrity?
The Ratio of Data to Errors
This allows you to track the number of known errors, such as incomplete or redundant entries. If you find fewer errors, you know that your data quality is improving.
Empty values indicate that information is either missing or recorded in the wrong field. The number of empty values within the data set is an easy way to track the data quality problem.
Data Storage Costs
Is your data storage cost rising while the amount of data you use remains the same? Well, this is the possible sign of data quality issues.
Data Quality and Metrics to Track
Data must have no contradictions in your databases. For instance, the budget amount for a specific department must be consistent across the organization so as not to exceed its total budget.
Data points must exist in the same and correct format everywhere they appear. Having a high rate of validity indicates that your data aligns with your established formatting rules.
Collecting data promptly to track changes effectively helps in measuring the impact immediately. “If we don’t measure frequently, we have to wait too long before we get insights from our measures. We need to make sure it’s regular enough to establish new baselines and pick up signals of change quickly,” explains Stacey.
To learn more about data integrity and KPI, click on https://www.staceybarr.com/measure-up/does-improving-data-accuracy-make-historic-kpi-values-useless/.