Last modified: 05 Aug 2021 13:04
The course will provide an understanding of: the value of data quality, the importance of data quality management and the consequences of poor data quality management. It will cover common data quality issues, and inherent uncertainty in data values, and demonstrate the need for data quality standards, business rules, policies and procedures, and how these are used to lead compliance activities. It will also show the relation between data governance and data quality.
| Study Type | Postgraduate | Level | 5 |
|---|---|---|---|
| Session | Second Sub Session | Credit Points | 15 credits (7.5 ECTS credits) |
| Campus | Aberdeen | Sustained Study | No |
| Co-ordinators |
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The course will provide an understanding of: the value of data quality and data quality management; the consequences of poor data quality management; common data quality issues; inherent uncertainty in data values, implications for data use, tracking data quality and the importance of documenting assumptions and precision of data; 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; the difference between standards and rules deriving from the nature of the data, from the business purpose the data meets, how these change from country to country; monitoring, handling and reporting data quality issues; addressing data quality issues; use of business rules for loading and cleansing data and data sets; audit and assessment of business data quality processes, standards and business rules compliance; understanding of the relation between data governance and data quality.
Information on contact teaching time is available from the course guide.
| Assessment Type | Summative | Weighting | 10 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
Daily oral feedback on formative basis throughout course. Feedback on group presentations and written submission within 4 days of submissions. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
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| Assessment Type | Summative | Weighting | 10 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
Daily oral feedback on formative basis throughout course. Feedback on group presentations and written submission within 4 days of submissions. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
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| Assessment Type | Summative | Weighting | 15 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
The results of the online test are available immediately, and additional formative feedback will be provided in the following week. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
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| Assessment Type | Summative | Weighting | 30 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
Daily oral feedback on formative basis throughout course. Feedback on group presentations and written submission within 4 days of submissions. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
|
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| Assessment Type | Summative | Weighting | 25 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
Daily oral feedback on formative basis throughout course. Feedback on group presentations and written submission within 4 days of submissions. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
|
|
||
| Assessment Type | Summative | Weighting | 10 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
Detailed written feedback within 3 weeks of reflective diary. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
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There are no assessments for this course.
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Apply | Explain the key issues which can arise in data quality and data quality management |
| Procedural | Analyse | Analyse business protocols and workflows to identify data quality issues, assess the impact on the business of poor data quality, and prioritize remedial actions |
| Procedural | Create | Evaluate data uncertainty, data provenance, & data security, and create a risk report that prioritizes remedial actions |
| Procedural | Apply | Apply compliance measures by creating robust models to demonstrate conformity with standards, business rules, and proper audit protocols |
| Conceptual | Apply | Explain the importance of dealing with data throughout the data lifecycle |
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