Build Your Data Quality Program

Quality data drives quality business decisions.

Onsite Workshop

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.

Module 1: Define Your Organization’s Data Environment and Business Landscape

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
  • Data Quality Management Primer
1.2 Detail business vision, objectives, and drivers
  • Business Capability Map Template
1.3 Discuss data quality barriers, needs, and principles
  • Data Culture Diagnostic
1.4 Assess current enterprise-wide data quality capabilities
  • Data Quality Diagnostic
1.5 Identify data quality practice future state
  • Data Quality Problem Statement Template
1.6 Analyze gaps in data quality practice

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
  • Business Unit Prioritization Roadmap
2.2 Develop subject areas project scope
  • Subject area scope
2.3 By subject area 1 data lineage analysis, root cause analysis, impact assessment, and business analysis
  • Data Lineage Diagram

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 Understand how data quality management fits in with the organization’s data governance and data management programs
3.2 By subject area 2 data lineage analysis, root cause analysis, impact assessment, and business analysis
  • Data Lineage Diagram
  • Root Cause Analysis
  • Impact Analysis

Module 4: Create a Strategy for Data Quality Project 3

The Purpose

Determine a strategy for fixing data quality issues for the highest priority business unit

Key Benefits Achieved

Strategy defined for fixing data quality issues for highest priority business unit

Activities: Outputs:
4.1 Formulate strategies and actions to achieve data quality practice future state
4.2 Formulate a data quality resolution plan for the defined subject area
  • Data Quality Improvement Plan
4.3 By subject area 3 data lineage analysis, root cause analysis, impact assessment, and business analysis
  • Data Lineage Diagram

Module 5: 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:
5.1 Formulate metrics for continuous tracking of data quality and monitoring the success of the data quality improvement initiative
  • Data Quality Practice Improvement Roadmap
5.2 Workshop Debrief with Project Sponsor
  • Data Quality Improvement Plan (for defined subject areas)
5.3 Meet with project sponsor/manager to discuss results and action items
5.4 Wrap up outstanding items from the workshop, deliverables expectations, GIs

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