Data quality is essential for the success of analytics and data analysis. At Data Science Connect, we explore the importance of data quality in achieving accurate and reliable insights.
Discover why data quality is crucial for effective analytics, from building trust in data to improving decision-making and reducing errors and risk. Learn how to implement best practices for data quality management, including data profiling, data cleansing, and data reconciliation techniques. Explore real-world examples of how companies have improved their data quality to enhance their analytics capabilities.
Arun Velu
Director, Advanced Analytics
Equifax