Lecture 17 - Data Quality and Integration_CTMod (1).pdf

Data entry poor data capture controls lack of

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§ Data entry: Poor data capture controls. § Lack of organizational commitment: Not recognizing poor data quality as an organizational issue. Lee-Tsai-758Y-10 6
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Steps in Data Quality Improvement § Get business buy-in. § Perform data quality audit. § Establish data stewardship program. § Improve data capture processes. § Apply modern data management principles and technology. § Apply total quality management practices. Lee-Tsai-758Y-10 7
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Business Buy-In § Executive sponsorship. § Building a business case. § Prove a return on investment (ROI). § Avoidance of cost. § Avoidance of opportunity loss. Lee-Tsai-758Y-10 8
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Data Quality Audit § Statistically profile all data files. § Document the set of values for all fields. § Analyze data patterns (distribution, outliers, frequencies). § Verify whether controls and business rules are enforced. § Use specialized data profiling tools. Lee-Tsai-758Y-10 9
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Data Stewardship Program § Roles: Oversight of data stewardship program. Manage data subject area. Oversee data definitions. Oversee production of data. Oversee use of data. § Report to: business unit vs. IT organization? Lee-Tsai-758Y-10 10
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Improving Data Capture Processes § Automate data entry as much as possible. § Manual data entry should be selected from preset options. § Use trained operators when possible. § Follow good user interface design principles. § Immediate data validation for entered data. Lee-Tsai-758Y-10 11
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Apply Modern Data Management Principles and Technology § Software tools for analyzing and correcting data quality problems: Pattern matching. Fuzzy logic. Expert systems. § Sound data modeling and database design. Lee-Tsai-758Y-10 12
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Total Quality Management (TQM) Principles and Practices § TQM Principles: Defect prevention. Continuous improvement. Use of enterprise data standards. Strong foundation of measurement. § Balanced focus: Customer. Product/Service. Lee-Tsai-758Y-10 13
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Master Data Management (MDM) § Disciplines, technologies, and methods to ensure the currency, meaning, and quality of reference data within and across various subject areas.
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