Conquer Data Quality Challenges in 4 Steps

A manifesto for strategic data quality improvement.

Onsite Workshop

Immature or non-existent data quality causes:

  • Lack of data trust.
  • Important decisions based on intuition and weak analysis.
  • Wrong decisions based on wrong data.
  • Business units bypassing IT to establish their own data silos to manage data quality. Data silos are not governed, propagating further issues.
  • Inability to launch other data initiatives such as business intelligence, master data management, and big data due to suboptimal data quality.
  • Leads to significant exposure to risks, non-compliance, and privacy issues.

Effective data quality management results in:

  • Increased trust in data.
  • The ability to leverage data to effectively control risks.
  • Data usability increasing over time, making data highly relevant to business purposes.
  • More informed decisions made based on quality data.
  • A solid foundation for other data management initiatives.

Module 1: Understand the Business Context

The Purpose

  • Understand the corporate strategy and goals.
  • Identify the most valuable subject areas to work on.
  • Define data quality principles to guide the project.
  • Scope, structure, and staff the project.

Key Benefits Achieved

  • Understanding of the business’s needs for data quality.
  • Knowledge of which subject areas are the most valuable to the business and how data quality interacts with those subject areas.
  • Establishment of overarching data quality principles to provide guidance.
  • The data quality initiative is tackled in chunks, with a defined and manageable scope.
  • Roles and responsibilities for the project team are defined. Agility best practices are used in the project.

Activities: Outputs:
1.1 Establish business context
  • Business vision, mission, and goals clarified
  • Business drivers and differentiators identified
1.2 Review and identify the most valuable subject areas
  • Key subject areas are identified based on business values
1.3 Define data quality principles
  • Data quality principles are defined
1.4 Define data quality roles and responsibilities
  • Project organization structure is defined
1.5 Establish project governance and ensure agility in data quality
  • Project governance is designed with built-in agility

Module 2: Data Inventory and Data Quality Assessment

The Purpose

  • Inventory data assets for the subject area(s) of interest.
  • Perform a detailed technical assessment of the data's quality.
  • Interview data users and owners to uncover issues around data.
  • Identify root causes and impact of the data issues.

Key Benefits Achieved

  • An understanding of data assets.
  • Data quality is quantified through technical assessment of the data.
  • Gathered insights on the data from interviews.
  • Root causes of the data issues are identified.
  • The business impact of the data issues are quantified.

Activities: Outputs:
2.1 Perform data inventory
2.2 Conduct user interviews
  • Data quality profiles for data assets
2.3 Assess data quality via technical assessment
  • Insights on the data quality issues
2.4 Perform root cause analysis
  • Root causes of the data issues
2.5 Quantify business impact
  • Quantified business impacts

Module 3: Data Quality Practice Assessment

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:
3.1 Evaluate data quality maturity
  • Current maturity of the data quality practice
3.2 Assess how data quality is integrated with other data management practices
  • Data quality maturity in the other related data management practices
3.3 Define the target state for the data quality practice
  • Future state maturity

Module 4: Initiatives Roadmap for Continual Improvement

The Purpose

  • Gather and rationalize issues and opportunities from the earlier phases.
  • Define improvement initiatives.
  • Define a data quality improvement strategy and roadmap.
  • Plan for continuous improvement in data quality.

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:
4.1 Take inputs in terms of data issues, gaps, and opportunities from the earlier phases
  • Data improvement initiatives are defined
4.2 Define and develop specifications for the improvement initiatives
  • Data quality improvement strategy is defined
4.3 Develop a strategy and roadmap for data quality
  • Roadmap to achieve the strategy is developed

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