Robust Quality : Powerful Integration of Data Science and Process Engineering

Historically, the term quality was used to measure performance in the context of products, processes and systems. With rapid growth in data and its usage, data quality is becoming quite important. It is important to connect these two aspects of quality to ensure better performance. This book provides a strong connection between the concepts in data science and process engineering that is necessary to ensure better quality levels and takes you through a systematic approach to measure holistic quality with several case studies.

After realizing the importance of data, organizations have begun to view data as a critical asset, being equally important as other assets such as people, capital, raw materials, and facilities. In order to ensure data are fit for the purpose, there is a need to have high quality levels of data. This book will provide an integrated approach by combining data quality and process quality approaches for ensuring robust quality. When the two concepts are combined, industry can achieve rapid improvements with accurate results and decisions. The methodology is combined with data quality, Lean and Six Sigma philosophies. The approach will guarantee improvements and stable operations.