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Data quality has matured. In its early days the focus of efforts to improve the quality of data in organizations was predominantly tactical. Usually a specific data quality (DQ) problem was identified and a project initiated and delivered to resolve or ameliorate it. Examples include improving a customer marketing list, clearing redundant records of former customers, matching logical and physical inventory and so on. This approach was characterised by a heavy emphasis on data cleanse, a one off process where shortcomings were recognized, quantified and improvements made.
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