Poor data quality costs businesses millions of dollars annually. While organizations understand that their data needs to be in good health to realize its value, a typical problem is they don’t know how to measure it. To improve your data quality, it is essential to track the right data quality metrics.
What are data quality metrics?
Data quality metrics are benchmarks for how useful your business data is. The aim is to give quantifiable evidence as to whether the data your hold is high or low quality. Making the right business decisions depends on the quality of your data. It impacts the ability of a business to solve problems and attain goals. Moreover, it can directly influence the customer experience which is crucial in a digital world.
There are six common data quality metrics that every business should have in place.
In simple terms, this data quality metric looks at whether record information is missing or recorded in the wrong place. To monitor completeness, take the number of records and review the percentage with have values. For example, you may want to ensure that every address has a zip code, monitoring that over time as the business transforms.
Naturally, you don’t want to have data that contains errors. The context of accuracy will depend on your business to decide the most relevant metric, but it checks if the information is correct or not. For example, there should be no outdated information, redundancies and typos. The ratio of total volume of data to the number of errors would be a way of measuring the accuracy.
A data quality metric for consistency is ensuring that values in different records and reports match. For example, if there is a budget number set in one report, that should be the same in all reports so that everybody is working from the same set of figures. The percentag of value that match between systems or datasets can be used as the data quality metric.
Data within a system or database should always conform to the same fromat. For example, all dat fields should be in “dd/mm/yyyy” format or “mm/dd/yy.” It doesn’t matter which, but should also be consistent. The data quality metric will look at a field and report the percentage of values that meet the necessary field validity.
Data needs to be accurate at a specific point in time. For example, you should not use data that is 6 months old to justify a decision that you make today. Instead, the data must be updated to the relevant period for efficient decision making. A data quality metric for timeliness could be checking the last date a field was updated or created. Ideally, businesses should be looking towards real-time data wherever plausible.
An example of a data quality metric for integrity would be the percentage of data that is the same across multiple systems or datasets. When you migrate data between systems, it should remain intact and not be subject to unintended amendments.
The data quality metrics that make the most sense to measure will depend on the nature of your business. However, the key takeaway here os to ensure you have a data quality assessment framework in place, that can support governance and give the business a trusted view of data. Quality of information is one of the biggest problems that slows down businesses looking to derive value from data, and should be an organizational priority.