Data cleansing is a very important aspect of improving data quality, but it’s not the only one. In order to have a sustainable data quality program you can’t just fix the data. You need to understand what needs to be fixed and why; analyze the root cause of the issues and address any findings; understand your data environment and inter-dependencies; identify the data owners, stewards, and custodians. You also have to profile the data<\/a> and not just understand the business logic by which data gets created, maintained, or consumed, but also how do these conflict with your technical constraints. Prevention methods in the form of data entry validations, regular data quality audits, clear ownership and definitions, understood business and technical processes are also needed to sustain the needed level of your data quality. For details on all of these, please read “The trifecta of the best data quality management<\/a>“.<\/p>\n