DM5502: PETROLEUM DATA QUALITY MANAGEMENT (2016-2017)

• The value of data quality and data quality management, and business consequences of poor data quality (lost value, increased costs, increased risk, reputational damage)
• Inherent uncertainty in data values and the implications for data use
• The contextual nature of data quality management which also depends on how data is used and context of use (not all applications require great precision or completeness, which incur costs)
• The difference between standards and rules deriving from the nature of the data, and those deriving from the business purpose the data meets; how standards and rules may differ by country and context
• Addressing data quality issues (e.g. through data clean-up projects and application of data analytics) • Auditing and assessing the business’ data quality processes and adherence to standards and business rules
• The relation between data quality management and data governance.
• Using business rules for loading and cleansing different data types and data sets
• Identifying, monitoring for, handling, and reporting data quality issues
• The role of, value of and need for data quality standards, business rules, policies, and procedures, and how these are used to lead compliance activities
• Tracking data uncertainty and quality; the importance of data users documenting assumptions and precision
• Common data quality issues (including issues related to data completeness, consistency and precision) and how to deal with these

Information on contact teaching time is available from the course guide.

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There are no assessments for this course.

There are no assessments for this course.