Data Quality for Analytics Using SAS

Analytics offers many capabilities and options to measure and improve data quality, and SAS is perfectly suited to these tasks. Gerhard Svolba’s Data Quality for Analytics Using SAS focuses on selecting the right data sources and ensuring data quantity, relevancy, and completeness. The book is made up of three parts. The first part, which is conceptual, defines data quality and contains text, definitions, explanations, and examples. The second part shows how the data quality status can be profiled and the ways that data quality can be improved with analytical methods. The final part details the consequences of poor data quality for predictive modeling and time series forecasting.

With this book you will learn how you can use SAS to perform advanced profiling of data quality status and how SAS can help improve your data quality.

Table of Contents

Part I: Data Quality Defined
Chapter 1: Introductory Case Studies
Chapter 2: Definition and Scope of Data Quality for Analytics
Chapter 3: Data Availability
Chapter 4: Data Quantity
Chapter 5: Data Completeness
Chapter 6: Data Correctness
Chapter 7: Predictive Modeling
Chapter 8: Analytics for Data Quality
Chapter 9: Process Considerations for Data Quality

Part II: Data Quality-Profiling and Improvement
Chapter 10: Profiling and Imputation of Missing Values
Chapter 11: Profiling and Replacement of Missing Data in a Time Series
Chapter 12: Data Quality Control across Related Tables
Chapter 13: Data Quality with Analytics
Chapter 14: Data Quality Profiling and Improvement with SAS Analytic Tools

Part III: Consequences of Poor Data Quality-Simulation Studies
Chapter 15: Introduction to Simulation Studies
Chapter 16: Simulating the Consequences of Poor Data Quality for Predictive Modeling
Chapter 17: Influence of Data Quantity and Data Availability on Model Quality in Predictive Modeling
Chapter 18: Influence of Data Completeness on Model Quality in Predictive Modeling
Chapter 19: Influence of Data Correctness on Model Quality in Predictive Modeling
Chapter 20: Simulating the Consequences of Poor Data Quality in Time Series Forecasting
Chapter 21: Consequences of Data Quantity and Data Completeness in Time Series Forecasting
Chapter 22: Consequences of Random Disturbances in Time Series Data
Chapter 23: Consequences of Systematic Disturbances in Time Series Data

Appendix A: Macro Code
Appendix B: General SAS Content and Programs
Appendix C: Using SAS Enterprise miner for Simulation Studies
Appendix D: Macro to Determine the Optimal Length of the Available Data History
Appendix E: Short Overview on Data Structures and Analytic Data Preparation