As more organizations realize the critical importance of viewing data as a corporate asset, data quality is an increasingly prevalent topic of discussion, especially within the context of establishing a data governance program.
Data governance maturity models describe an organization's evolution through a series of stages intended to measure its capability and maturity, its tendency toward being reactive or proactive, and its inclination to be project-oriented or program-oriented. Historical approaches to data quality relied on reactive data cleansing projects focused on correcting existing data problems, but without resolving their root cause - and in some cases, without even identifying it.
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