* Section - ONE: Introduction; ** Chapter - 01: Why data quality matters; ** Chapter - 02: Communication data requirements with the dimensions of data quality; ** Chapter - 03: Trends in the adoption of the dimensions of data quality; * Section - TWO: Techniques to manage and improve data quality; ** Chapter - 04: Introduction to techniques for management and improvements; ** Chapter - 05: Top-down and bottom-up approaches; ** Chapter - 06: Validating your data quality; ** Chapter - 07: Completeness and consistency techniques; ** Chapter - 08: Methods of profiling data; ** Chapter - 09: Human-directed auditing; ** Chapter - 10: Conducting objective and subjective surveys; ** Chapter - 11: Data contracts and the role of data governance; * Section - THREE: Conformed dimensions - a standard set of dimensions of depth; ** Chapter - 12: Completeness - where to start when you don't have the data; ** Chapter - 13: Accuracy - when your data isn't correct; ** Chapter - 14: Precision- how granular does your data need to be; ** Chapter - 15: Consistency - comparing your data to other sources; ** Chapter - 16: Validity - when data isn't a valid combination; ** Chapter - 17: Timeliness, currency and accessibility - which measure to use; ** Chapter - 18: Integrity - ensuring correct connectivity; ** Chapter - 19: Lineage - building confidence; ** Chapter - 20: Representation - provide more context; * Section - FOUR: Preparing for the future of data quality; ** Chapter - 21: Current versus future improvements with AI; ** Chapter - 22: Improved transparency and privacy with blockchain;.