The Data Quality Metrics Every Business Leader Should Track

Modern businesses operate in a competitive data-driven landscape, wherein their potential for success depends on the reliability of the information they use to make decisions. While some business sectors use more data than others, it is safe to assume that most companies and organizations are managing large datasets that were unthought of until the mid-2000s.

Having a lot of data to work with is ideal, but only if you can ensure it is up to par. What counts is the quality of the data. If you are working with good data, you don’t run as many risks of making mistakes; plus, you can also enjoy lower operational costs as you make better decisions, deliver better products, and provide better services.

It took a few years for business management researchers to recognize that the Big Data revolution had a significant governance problem related to quality. In 2017, researchers from Experian and the Massachusetts Institute of Technology found that poorly sourced and ill-compiled data was costing enterprises up to 25% of revenue. The factors driving this sharp revenue loss ranged from operational errors to angry customers, delays, and wasted time. In 2023, Experian updated its research to reveal that the American economy was losing up to $3 trillion in gross domestic product to poor data quality.

The Metrics of Data Governance

If you are using data to shape and improve your business operations, you must implement proper governance. Data metrics allow you to measure the adequacy of your governance. Let’s say you used a dataset to identify 100 leads that your sales team will pursue; if two leads are completely unrelated to the products and services you offer, you have a 98% lead accuracy metric, so you can try improving it to perfection or instructing your sales team managers to scrub the leads.

The metrics of data governance include:

Quality: Observes many factors, including accuracy, completeness, consistency, and reliability.

Security: Protects data from unauthorized access, use, disclosure, disruption, modification, or destruction.

Compliance: Adheres to relevant laws, regulations, industry standards, and internal policies.

Availability: Evaluates the accessibility and timeliness of data.

Usage: Monitors how datasets are being accessed, applied, and leveraged.

Effective data governance should be guided by all the metrics listed above; nonetheless, you always want to focus on data quality and its various factors. If you put quality at the forefront of your data governance strategy, the rest of the metrics fall into place more efficiently. Working with good data means having a solid foundation for making the right decisions, getting better visibility into business operations, performing more accurate analysis, and enjoying better outcomes.

Factors To Evaluate With Data Quality

Data quality is easier to determine and manage when you follow a process to evaluate the following factors:

Accuracy: This is the level to which data correctly represents real-world objects or situations.

Completeness: The extent to which all data fields or attributes have values and are not missing.

Consistency: The uniformity of values across different datasets, systems, and points in time.

Timeliness: Data must be available when you need it, and it should reflect the current state of affairs.

Validity: This refers to data conforming to defined business rules, formats, and constraints.

Uniqueness: You don’t want duplicate records or entries within a business dataset because they could lead to confusion and misinformation.

Relevance: Data must be useful and applicable to the specific purpose or analysis for which it is being used.

Improving Data Quality

The data quality metric is calculated from the average of the measured factors. Many businesses prefer to work with percentages; however, some companies may assign different weights or relevance to the factors. Accuracy, completeness, and validity should be standard for all business organizations; however, marketing operations may be more flexible in the relevance factor because of creativity extrapolation.

The best way to measure data quality factors is to integrate your datasets into unified data governance platforms. These platforms offer automated tools and dashboards to continuously monitor and report on the defined factors. A wholesale brewer, for example, can track the accuracy of its ingredient inventory, the completeness of their batch records, and the consistency of their production data across different brewing locations.

You can apply various strategies for data quality improvement. The most straightforward is to analyze each factor and improve its score as needed. If data from your human resources department is scoring low in terms of timeliness, you can consider faster generation methods, which may include making adjustments to the reporting process. A more comprehensive strategy involves data profiling, which consists of using your governance platform to detect gaps, duplicates, and errors.

Many data analysts recommend a standardization strategy for overall improvement if you are working with datasets that feature different units or are quantified in large ranges. You can accomplish this through the Z-score function of your data governance application, which subtracts values from their mean and divides them by the standard deviation.

Visualization is a more commonly recommended strategy for data improvement that everyone can grasp. When you present quality metrics in accessible visual formats such as charts and graphs, the decision-makers in your organization can quickly grasp the current state of their data and identify areas needing attention.

There’s also data validation, which is likely one of the most powerful features your governance platform offers. This automated strategy checks the type of data, its format, consistency, and uniqueness at once. This is a complicated strategy if you attempt to conduct it manually, so you want to use automated processes.

The reality of the Big Data revolution is that the value of data is inherently linked to its quality. When you prioritize data quality through data governance strategies, your business can reduce operational costs, improve decisions, stick to compliance, and deliver better outcomes.

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Source: https://www.sitepronews.com/2025/05/08/the-data-quality-metrics-every-business-leader-should-track/