The business impacts of data quality issues—what they look like and how to think about fixes

Data quality is a critical issue for leaders in product analytics and management, as unreliable data can erode trust, delay progress, and hinder innovation. Addressing data quality challenges is essential for making informed decisions and driving organizational success. Common issues such as over-tracking, usability problems, incomplete or inconsistent data, and data duplication can disrupt decision-making and drain resources. High-quality data is not only about accuracy but also about enabling faster and more confident decision-making. Tools like Mixpanel and Avo can standardize tracking and enhance data quality, allowing teams to unlock the full potential of their analytics. The article emphasizes the need for recognizing data quality issues, understanding their business impact, and implementing a systematic approach to address them. By fostering a culture of accountability and collaboration between data, product, and engineering teams, organizations can prevent new issues and prioritize existing ones to resolve, thus driving better outcomes.