Quality in the Era of Industry 4.0

6Data Quality

6.1 Introduction

As we traverse the landscape of data‐driven decision‐making, the old adage “Garbage In, Garbage Out” rings more true than ever. The caliber of data leveraged to guide decisions, sculpt strategies, and drive operations remains a prime consideration for contemporary organizations. Hence, the understanding and enhancement of data quality form the bedrock of successful data management, emerging as a focal point for quality professionals.

Fundamentally, data quality gauges the state of data based on criteria such as accuracy, completeness, consistency, reliability, and relevancy. High‐quality data should not only be precise but also pertinent for its intended purpose, and it needs to be delivered promptly and in an intelligible format.

For quality professionals, safeguarding data quality is central to their duties. Superior quality data lays a robust foundation for processes and systems, curtails operational inefficiencies, and bolsters the reliability of outcomes. These professionals shoulder crucial responsibilities in defining data governance policies, setting standards for data collection, and deploying practices to monitor and enhance data quality. Armed with high‐quality data, quality professionals can produce more accurate analyses, make well‐informed decisions, and heighten overall operational efficacy.

The dawn of the digital age has amplified the significance of data quality exponentially. The rise of big data, artificial intelligence, and ...