Learnings from Pursuing High Data Quality: A Reflective Piece

Cover Photo by Claudio Schwarz on Unsplash Before diving in, let us discuss what data quality entails. IBM puts it so well - Data quality measures how well a dataset meets criteria for accuracy, completeness, validity, consistency, uniqueness, timeliness and fitness for purpose, and it is critical to all data governance initiatives within an organisation. Data quality does not only care about the data being pristine, but also being fit for intended use, meaning that data quality is context-specific. The domain in which the data is collected and used is as important as the other checks. In fact, in many situations, it provides the foundation to define checks for accuracy, validity, consistency, timeliness and uniqueness. Furthermore, data quality can build or destroy trust within the team. This is a reflective piece that encapsulates my experience setting up a roadmap for maintaining high-quality data. When asked previously about data quality enforcement and implementation, I always res