Restore Trust in Your Data With a Business-Aligned Data Quality Management Approach
Great data-driven insights start with great data quality.
RETIRED CONTENT
Please note that the content on this page is retired. This content is not maintained and may contain information or links that are out of date.Not having a business-aligned data quality management approach results in:
- Unclear definitions of what tolerance for data quality is and how it differs across business units according to their needs.
- Difficulties getting past a “band-aid” level of data quality solutions, as the issues continue to crop up even after an attempt has been made to repair data quality.
- Difficulty understanding the root causes of these issues.
Having a business-aligned data quality management approach results in:
- Good data quality, allowing you to generate insights into customer intimacy, operational excellence, and innovation leadership.
- Effective data quality improvement projects driven by business need for data.
- A corporate culture that holds data quality as a priority and data as a key asset.
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Module 1: Define and Assess Your Organization’s Data Quality Practice
The Purpose
- Evaluate the maturity of the existing data quality practice and activities.
- Assess how data quality is embedded into related data management practices.
- Envision a target state for the data quality practice.
Key Benefits Achieved
- Understanding of the current data quality landscape.
- Gaps, inefficiencies, and opportunities in the data quality practice are identified.
- Target state for the data quality practice is defined.
| Activities: | Outputs: | |
|---|---|---|
| 1.1 | Explain approach and value proposition. |
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| 1.2 | Detail business vision, objectives, and drivers. |
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| 1.3 | Discuss data quality barriers, needs, and principles. |
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| 1.4 | Assess current enterprise-wide data quality capabilities. |
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| 1.5 | Identify data quality practice future state. |
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| 1.6 | Analyze gaps in data quality practice. |
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Module 2: Create a Strategy for Data Quality Project 1
The Purpose
- Define improvement initiatives.
- Define a data quality improvement strategy and roadmap.
Key Benefits Achieved
- Improvement initiatives are defined.
- Improvement initiatives are evaluated and prioritized to develop an improvement strategy.
- A roadmap is defined to depict when and how to tackle the improvement initiatives.
| Activities: | Outputs: | |
|---|---|---|
| 2.1 | Create business unit prioritization roadmap. |
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| 2.2 | Develop subject areas project scope. |
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| 2.3 | Subject area 1: data lineage analysis, root cause analysis, impact assessment, business analysis. |
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Module 3: Create a Strategy for Data Quality Project 2
The Purpose
- Define improvement initiatives.
- Define a data quality improvement strategy and roadmap.
Key Benefits Achieved
- Improvement initiatives are defined.
- Improvement initiatives are evaluated and prioritized to develop an improvement strategy.
- A roadmap is defined to depict when and how to tackle the improvement initiatives.
| Activities: | Outputs: | |
|---|---|---|
| 3.1 | Create business unit prioritization roadmap. |
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| 3.2 | Develop subject areas project scope. |
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| 3.3 | Subject area 1: data lineage analysis, root cause analysis, impact assessment, business analysis. |
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Module 4: Create a Plan for Sustaining Data Quality
The Purpose
- Plan for continuous improvement in data quality.
- Incorporate data quality management into the organization’s existing data management and governance programs.
Key Benefits Achieved
- Sustained and communicated data quality program.
| Activities: | Outputs: | |
|---|---|---|
| 4.1 | Formulate metrics for continuous tracking of data quality and monitoring the success of the data quality improvement initiative. |
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| 4.2 | Workshop debrief with project sponsor. |
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| 4.3 | Meet with project sponsor/manager to discuss results and action items. |
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| 4.4 | Wrap up outstanding items from the workshop, deliverables expectations, GIs. |
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