Taking Data Quality to the Enterprise through Data Governance
07/11/06
Data quality is difficult to comprehend in its entirety, because of the diverse aspirations and actions collected under its broad umbrella. This includes standard technology and business practices that improve data, like name-and-address cleansing, record matching and merging, house-holding, deduplication, standardization, and appending third-party data. Some of these tasks can be automated with software, while others—like entering data properly—are purely matters of business process.
March 2006
Data quality is difficult to comprehend in its entirety, because of the diverse aspirations and actionscollected under its broad umbrella. This includes standard technology and business practices thatimprove data, like name-and-address cleansing, record matching and merging, house-holding, deduplication,standardization, and appending third-party data. Some of these tasks can be automatedwith software, while others—like entering data properly—are purely matters of business process.
Given this complexity, it’s no wonder misconceptions abound, like thinking data quality is aone-time action that results in perfection. To the contrary, data quality is a complex concept thatencompasses many data-management techniques and business-quality practices, applied repeatedlyover time as the state of quality evolves, to achieve levels of quality that vary per data type andseldom aspire to perfection.
Of the organizations TDWI surveyed, 82.5% continue to perceive their data as good or okay.However, half of the practitioners surveyed warn that data quality is worse than their organizationrealizes, which explains why the number of organizations with a data-quality plan doubled between2001 and 2005.
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