The deep prevalence of technology across nearly every industry means that many enterprise organizations now need to contend with various data-related issues. Digitally-stored data has become a critical part of day-to-day operations, even in traditional industries. While high-quality data can help a business thrive, low-quality data can lead to numerous problems and even dangerous situations.

However, defining data quality and how data quality monitoring can benefit your business can sometimes be a tricky task. Although these concepts may seem vague, they're fundamental to ensuring your organization's data adequately supports its business operations and goals.

What is Data Quality?

There are several different ways of describing data quality, usually described as data quality dimensions. One common method of describing data divides quality into intrinsic, contextual, representational, and accessibility categories. These categories broadly describe the dimensions where organizations can monitor and improve data quality.

Intrinsic and contextual quality describe two sides of the same coin. Intrinsic quality is inherent to the data itself. Data with poor intrinsic quality may be inaccurate, contain errors, or come from untrustworthy sources. On the other hand, contextual quality deals primarily with how valuable the data is in context. For example, out-of-date data may have poor contextual quality.

Likewise, the representational and accessibility categories also go hand-in-hand. Data representation describes how easily data consumers (whether humans or software) can read and understand the information. However, well-represented data must also be accessible and secure, two quality dimensions covered by the accessibility category.

What Are the Practical Benefits of Data Quality Monitoring?

Data quality monitor software helps organizations track one or more of the core categories of data quality. Poor quality in any area can lead to potentially severe business outcomes. For example, intrinsically poor data may contain errors that make billing or contacting customers impossible. Representationally poor data may be slow to access or challenging to integrate into other systems.

By utilizing software to automate your organization's data quality monitoring process, you can ensure your data meets critical metrics for your business operations. This monitoring can reduce the potential for confusion or issues between departments or separate software systems. Maintaining a high standard of data quality can also improve data security and make data audits easier to manage.

Ultimately, every sizable organization relies on large quantities of data for day-to-day operations, analysis, and decision-making processes. Data quality monitoring ensures that your organization's data remains accurate, up-to-date, relevant, and accessible. By using effective data quality monitoring software, you can ensure your organization's data will always be fit for the tasks at hand.

Contact a company that provides data quality monitoring software to learn more.