Data quality has become a fundamental aspect for any company who wants to develop optimal business operations that are trustworthy and efficient.

Data quality is a measure of the status of data that is based on several factors: accuracy, consistency, and reliability, among others. Measuring data quality levels can help organizations identify mistakes that need to be resolved and assess whether the data in their computer systems is fit for the purpose for which it was collected.

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The emphasis on data quality in business systems has increased as data processing has become more closely linked to business operations. In addition, organizations are increasingly using data analysis to help drive business decisions, so data quality management has become a core component of the overall data management process. Data quality improvement efforts are often closely linked to data governance programs that aim to ensure that data is used consistently across the organization.

Using poor quality data can have significant business consequences for companies. This data is often the source of operational inefficiencies, inaccurate analysis, or poorly conceived business strategies. For example, data quality issues can lead to additional costs, lost sales opportunities, or penalties for incorrect or inaccurate financial reports or lack of compliance.

In addition, these problems can lead to executives losing confidence in their data, which can mean that business intelligence and analytical tools to improve decision making in organizations can cause them mistrust. Ultimately, this can affect the data culture of the entire company.

From a financial standpoint, maintaining high levels of data quality allows organizations to reduce the cost of identifying and repairing faulty data in their systems. Companies can also avoid operational errors and business process breakdowns that can increase operating costs and reduce revenue.

In addition, good data quality increases the accuracy of analytical applications, which can lead to better business decision making that drives sales, improves internal processes and gives organizations a competitive advantage over their competitors. Good quality data can help expand the use of BI dashboards and analytical tools. In addition, if analytical data is perceived as reliable, business users are more likely to trust it rather than base their decisions on their instincts or their own spreadsheets.

Effective data quality management also frees up data management teams to focus on more productive tasks than cleaning up data sets. For example, they can spend more time helping business users and data analysts leverage the data available in the systems and promoting better practices on data quality and business operations to minimize data errors.

Posted by Núria E