Robust Quality: Powerful Integration of Data Science and Process Engineering

This book provides a strong connection between data science and process engineering to ensure better quality levels and takes a systematic approach to measuring holistic quality with case studies.

Format: Paperback / softback
Length: 124 pages
Publication date: 31 March 2021
Publisher: Taylor & Francis Ltd

Provides real-world case studies to illustrate the application of the concepts
Historically, the term "quality" was employed to assess the performance of products, processes, and systems. However, with the exponential growth of data and its widespread utilization, the importance of data quality has surged. It is crucial to establish a strong connection between these two facets of quality to enhance performance. This book offers a robust bridge between the concepts of data science and process engineering, essential for achieving higher quality levels. It guides a systematic approach to measuring holistic quality, supported by numerous case studies.

Key Features:

Integration of Data Science, Analytics, and Process Engineering Concepts: This book delves into the intersection of these disciplines, providing a comprehensive understanding of how they can be leveraged to create value. It discusses how data, analytics, and processes can be integrated to optimize performance and improve decision-making.

Exploration of Value Creation through Data, Analytics, and Processes: The book emphasizes the importance of considering data, analytics, and processes in creating value. It examines how organizations can leverage these tools to enhance their competitive advantage, improve customer satisfaction, and drive innovation.

Metrics Management Techniques for Evaluating Performance: The book explores various metrics management techniques that can help evaluate the performance levels of processes, systems, and models, including AI and machine learning approaches. It discusses the importance of selecting the right metrics, measuring them accurately, and interpreting them to make informed decisions.

Structured Approach for Analytics Execution: The book provides a structured approach for executing analytics projects. It outlines the steps involved in data collection, analysis, interpretation, and visualization, ensuring that projects are executed efficiently and effectively.

Real-World Case Studies to Illustrate the Application of the Concepts: The book includes real-world case studies that illustrate the application of the concepts discussed in the text. These case studies provide practical insights into how organizations have successfully implemented data science, analytics, and process engineering to achieve their business goals.

By integrating data science, analytics, and process engineering concepts, this book offers a comprehensive guide to achieving higher quality levels and optimizing performance. It is an invaluable resource for professionals, researchers, and students interested in leveraging data and technology to drive business success.

Weight: 263g
Dimension: 234 x 156 (mm)
ISBN-13: 9780367780975