Haphazardly implementing data quality practices in your organization without a balanced investment in people, process, and technology results in:
- Poor quality of insights and decision-making.
- Reduced productivity and satisfaction with increased time spent on data cleansing.
- Decreased value returns to the organization.
A robust data quality program allows you to:
- Restore trust in data quality.
- Improve mechanisms in profiling, rectifying, and monitoring data quality.
- Determine which data management capabilities will support the journey of becoming a data-driven organization.
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Onsite Workshops offer an easy way to accelerate your project. If you are unable to do the project yourself, and a Guided Implementation isn’t enough, we offer low-cost onsite delivery of our Project Workshops. We take you through every phase of your project and ensure that you have a road map in place to complete your project successfully.
Book NowModule 1: Assess the Scope of Data Quality
The Purpose
Identification of key data quality problems that when resolved, will improve the state of strategic priorities.
Key Benefits Achieved
Data quality program scope defined for data profiling, improvement, and monitoring.
| Activities: | Outputs: | |
|---|---|---|
| 1.1 | Identify symptoms of data quality problems. |
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| 1.2 | Develop data quality problem statements. |
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| 1.3 | Identify critical data elements involved. |
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| 1.4 | Determine the value and impact drivers of those data elements. |
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Module 2: Identify the Root Causes of Data Quality Issues
The Purpose
Profile data quality issues impacting strategic priorities.
Key Benefits Achieved
Identification of root cause issues hindering data value.
| Activities: | Outputs: | |
|---|---|---|
| 2.1 | Define the data quality program scope. |
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| 2.2 | Perform scenario-based data lineage. |
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| 2.3 | Conduct fishbone root cause analyses. |
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Module 3: Build Your Data Quality Improvement Plan
The Purpose
Definition of data quality improvement initiatives.
Key Benefits Achieved
Development of data quality improvement plan with assignment of resources and improvement roadmap .
| Activities: | Outputs: | |
|---|---|---|
| 3.1 | Identify data quality improvement opportunities. |
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| 3.2 | Define data quality improvement working groups. |
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| 3.3 | Develop the data quality improvement roadmap. |
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Module 4: Scale Your Data Quality Practice
The Purpose
Identification of data management capabilities that make data quality improvement sustainable.
Key Benefits Achieved
Alignment of most applicable data quality dimensions with data management capabilities as mechanisms for sustained improvement.
| Activities: | Outputs: | |
|---|---|---|
| 4.1 | Identify the most applicable data quality dimensions |
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| 4.2 | Identify data management capabilities for sustained data quality improvement. |
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