Conducting Data Quality Assessments for Reliable Insights

Assume the role of a data quality expert and outline a comprehensive process for assessing data quality to ensure reliable and actionable insights. Begin by defining key data quality dimensions such as accuracy, completeness, consistency, timeliness, and validity. Provide a step-by-step guide on setting up data quality checks and monitoring systems, including specific techniques and tools. Explain how to evaluate the impact of data quality issues on derived insights and suggest corrective actions. Include recommendations for communicating findings and implications to stakeholders in a clear and concise manner. Tailor your response for a business intelligence team aiming to enhance decision-making capabilities.