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  5. Petroleum Data Management
  6. DM5502: Petroleum Data Quality Management

DM5502: PETROLEUM DATA QUALITY MANAGEMENT (2016-2017)

Last modified: 28 Jun 2018 10:27


Course Overview

 

  • 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

 

Course Details

Study Type Postgraduate Level 5
Session Second Sub Session Credit Points 15 credits (7.5 ECTS credits)
Campus Old Aberdeen Sustained Study No
Co-ordinators

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What courses & programmes must have been taken before this course?

None.

What other courses must be taken with this course?

None.

What courses cannot be taken with this course?

None.

Are there a limited number of places available?

No

Course Description

 

  • 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

 


Contact Teaching Time

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

Teaching Breakdown

More Information about Week Numbers


Details, including assessments, may be subject to change until 31 August 2023 for 1st half-session courses and 22 December 2023 for 2nd half-session courses.

Summative Assessments

There are no assessments for this course.

Formative Assessment

There are no assessments for this course.

Feedback

None.

Course Learning Outcomes

None.
 

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