Top Five Data Quality Metrics Every Marketer Should Know

A Gartner’s report says the average financial impact of poor quality data on organizations is $12.9 million per year. Conversely, companies that commit to improving their data quality have seen revenue growth by 15% to 20%.

With this being said, it’s evident that data quality holds a high importance when making strategic decisions, solving critical challenges, and achieving business goals. To stay current and to position your organization as a leader, there are some data quality metrics that may be worth implementing.

Data Quality Metrics to Always Prioritize

Are you ready to learn some effective ways to evaluate the value and quality of data that’s available in your company’s database? If so, you’re in the right place!

1. Accuracy

Accuracy is crucial for their buying decision. It indicates how well data reflects the real-world situation. It’s important to understand that accurate data allows marketers to track and reach their target audiences with relevant information. The measurement of data accuracy depends on a data error ratio. This means that if the number of data errors is less than the size of a data set, the data is accurate.

2. Completeness

In addition, another valuable data quality metric is completeness, which shows how extensive the available information within a company’s  database is. If an organization has complete data, they can receive a clearer picture of their buyers, better understand their behavior, and from there, make more informed decisions.

On the contrary, though, incomplete information is known to provide inaccurate insights, which often leads to erroneous prospecting, poor judgments, and a loss of business opportunities. Measurement of data completeness depends on the number of empty values such as missing information and/or wrong entries.

3. Consistency

When datasets move from one location to another without changing their value, it’s known as “consistency of data.” Consistent data creates coordination among all departments and ensures that everyone is under the same assumptions. It also eliminates uncertainty, inefficiency, and disorientation, which are the three major roadblocks for making informed decisions. Data that stays unchanged across various records is a deciding factor to measure consistency.

4. Timeliness

Next is timeliness, which refers to the expectation for both the availability and accessibility of information. It goes further to measure the accuracy of data at a given period. If the data that’s collected is not in real-time, it not only becomes obsolete, but also less accurate. It’s essential to evaluate timeliness by calculating data time-to-value (time data team takes to make data available, accessible for other teams). The more time a data team uses up to obtain insights from a data set often results in a lower quality of data.

5. Integrity

The last data quality metric to keep in mind is integrity, which is also known as data validation. However, data integrity refers to the overall accuracy, completeness, and consistency of data. It’s responsible for making data free from unintended errors while guaranteeing that data is never improperly categorized or stored incorrectly. By following regulatory compliance policies such as GDPR and CCPA, data integrity ensures a company’s data is protected. Also, there’s the data transformation error rate, which is a deciding factor for measuring data integrity.

Data Quality Metrics Are Crucial For Collecting and Using  High-Quality Data

Overall, the main goal of these five data quality metrics is to facilitate high quality data with the intention of reaping better business outcomes. Keep in mind that your organization is likely to achieve success if you regularly employ these metrics to improve the quality of your data, and it’s up to you to get started.

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