Build Your Data Quality Program

Quality data drives quality business decisions.

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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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Module 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.
  • Data quality problem statements.
1.2 Develop data quality problem statements.
1.3 Identify critical data elements involved.
  • Critical data elements identified.
1.4 Determine the value and impact drivers of those data elements.
  • Value and impact drivers of those data elements.

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.
  • Data quality program scope.
2.2 Perform scenario-based data lineage.
  • Data lineage scenario diagram.
2.3 Conduct fishbone root cause analyses.
  • Root cause issue identification.

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.
  • Initial data quality improvement opportunities.
3.2 Define data quality improvement working groups.
  • Initial data quality improvement working groups.
3.3 Develop the data quality improvement roadmap.
  • Data quality improvement initiative roadmap.

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
  • Data management capabilities for sustained improvement.
4.2 Identify data management capabilities for sustained data quality improvement.
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