Data Quality for Analytics Using SAS

摘要

In line with Ozmen-Ertekin and Ozbay (2012), just like any other scientific research field, the value of data quality is undisputed in many fields. From policy planning to performance evaluation, from model development to impact studies, good quality data is essential to generate ideas and clear-cut solutions to be implemented by decision makers. Data quality is getting a lot of attention in the market. Depending on the data retrieval area and the analytical domain, data quality is a very heterogeneous and widespread topic (Storey, Dewan, & Fre-imer, 2012; Loshin, 2011). However, most of the initiatives, publications, and papers on data quality focus on classical data quality topics. This is the first book that deals with data quality from the viewpoint of a statistician, data miner, engineer, operations researcher, or other analytically minded problem-solver. This book is a brilliant practical guide to data quality control.