Six Mistakes Made When Assessing Data Quality

Six Mistakes Made When Assessing Data Quality

Making the most of big data is essential for businesses in today's economy. However, making mistakes when dealing with big data can damage a company. To help get started, the experts at Databand share their list of the biggest mistakes made when assessing the quality of data.

Not Understanding What Determines Quality

Before your company can determine that its data is of the highest quality, business owners and staff need to fully understand what data quality means. Quality data combines accuracy, completeness, consistency, timeliness, and validity. Taking the time to understand what each one means can help companies better assess the quality of their data.

Data accuracy means the information is accurate and free of errors or typos. Data completeness refers to how much of the data has been captured, whether it is a complete dataset or missing values. Consistency ensures that all data elements have been entered in the same format and use the same naming conventions. Timeliness talks about how quickly data is collected, updated, and available for analysis. And validity refers to the information being up-to-date and conforming to the standards.

Not Establishing Quality Assurance Standards

To ensure that your data is of the highest quality, it’s essential to establish quality assurance standards. These standards will ensure that everyone involved in handling and managing data takes the same approach when assessing data quality.

These standards should include what is expected when managing and handling data and how often it should be done. They should also outline who is responsible for each step of the process and what processes need to be followed to ensure that all data meets the established quality requirements.

Not Testing Data Quality

Once standards have been established, the data must be tested to ensure that it meets them. The testing process should include reviewing how data is being stored and managed and any procedures in place to ensure accuracy and consistency. It is essential to look for any discrepancies or errors in the data before making decisions based on it.

Once the data has been tested, it is crucial to develop a plan for making changes. Some of the changes your company could face include identifying necessary changes and setting up procedures for future changes.

Not Automating Data Quality Processes

Automation can be used to help streamline data quality processes. Automation can save time and resources by automating the mundane tasks associated with analyzing and managing data. Some of the things automation can help with are regular checks of accuracy, completeness, timeliness, consistency, and validity.

Automation also helps reduce manual errors in data entry, as well as making sure that data meets the quality standards set. Automating data quality processes ensures that data is regularly updated and managed to ensure maximum accuracy.

Not Monitoring Data Quality

Data quality should be monitored on an ongoing basis to ensure it remains up-to-date, accurate, and consistent. Businesses need to have a system to track any changes to the data and ensure that any discrepancies are flagged quickly.

It is also essential to have processes in place for updating the data when needed. This includes implementing regular checks of accuracy, completeness, timeliness, consistency, and validity. Having these checks in place will help businesses identify any potential issues with the quality of their data quickly and take the necessary steps to correct them.

Not Documenting Data Quality

To ensure that data quality remains high, businesses must have processes to document their data quality standards and procedures. Documentation keeps everyone involved in managing the data to understand the company’s expectations of them and how they should go about meeting those.

It is also essential to document any changes made to the data to keep track of how it has evolved over time. This will help businesses quickly identify any potential data problems and take steps to address them.

Final Thoughts

Data quality is essential to any business, as it helps organizations make accurate and informed decisions. To ensure that data is of the highest quality, companies need to establish quality assurance standards, test their data for accuracy and consistency, automate processes where possible, monitor data on an ongoing basis, and document any changes or issues that arise. Following these steps from Databand will help businesses ensure that their data is of the highest quality and can be relied upon to make sound business decisions.