Best practices to improve data quality

Data quality improvement is often a difficult, expensive, and time-consuming proposition. Read about these critical practices to keep your data clean and your reporting accurate.

The right data can be used for multiple purposes, including decision-making, business planning and operations. With data as the foundation for most enterprise IT systems, the quality of data becomes vital to the overall success of the IT ecosystem.

Data that is of poor quality can result in inaccurate analytics, operational inefficiencies and other types of issues that won’t let your business reach its maximum potential. In this guide, we offer best practices for improving data quality across your organization’s various data sets and systems.

Data quality describes the condition of an organization’s data in terms of consistency, accuracy, reliability and completeness. Data that is high quality fits the purpose it was intended for and offers an accurate representation of the real-world construct that it refers to.

Data quality itself is an easy enough concept to understand, but maintaining high data quality standards can be difficult without the right strategies and best practices in place.

The emphasis on data quality has increased as organizations realize the value of data in driving business decisions and improving the efficiency of operations. Improving data quality is a never-ending process, and that is exactly how it should be treated. Here are a few tips to improve data quality at all stages of the data management lifecycle.

There are many ways data quality can be measured, and there are no set standards for the metrics that should be used to measure data quality. Ideally, an organization should measure data using metrics that are meaningful to their business. The metrics should be measurable and specific so you can objectively evaluate and improve the quality of data. Some examples of metrics you can use for data quality include the number of data test failures or the percentage of data test coverage.

When you encounter issues or errors related to data, you must have an established process to investigate the problem. This will help you understand the issue and take steps to improve data quality. Identifying the problem would be one of the first steps in the process. Every time a problem is resolved, steps should be taken to minimize the likelihood of this problem occurring again.

The process can include a data quality checklist to determine if there are any data incoherencies, gaps in the timeline, formatting errors or missing attribute values.