Which activity is focused on developing and maintaining data quality?

Which activity is focused on developing and maintaining data quality?

• A. Data Asset Valuation
• B. Communication Mechanisms
• C. Data Quality Improvement Priorities
• D. Change Management Engagement

Answer: (C)

The activity that focuses on developing and maintaining data quality is centered around establishing priorities for improving data quality. This encompasses identifying specific areas where data quality issues exist, setting goals for enhancements, and creating actionable plans to address these issues. By focusing on data quality improvement priorities, organizations can systematically address the root causes of data quality problems and implement processes that lead to sustained improvements. This proactive approach helps ensure that data is accurate, consistent, and fit for use in decision-making and operational processes. In contrast, while data asset valuation involves recognizing the worth of data sets, it does not directly tackle the quality of the data itself. Communication mechanisms are essential for ensuring that stakeholders are informed and can collaborate effectively, but they do not inherently focus on data quality. Change management engagement deals with managing the transitions and transformations within an organization, which can impact data processes but is not specifically dedicated to enhancing the quality of data. Thus, the emphasis on setting improvement priorities clearly aligns with the goal of advancing data quality within an organization.

The activity that focuses on developing and maintaining data quality is centered around establishing priorities for improving data quality. This encompasses identifying specific areas where data quality issues exist, setting goals for enhancements, and creating actionable plans to address these issues. By focusing on data quality improvement priorities, organizations can systematically address the root causes of data quality problems and implement processes that lead to sustained improvements. This proactive approach helps ensure that data is accurate, consistent, and fit for use in decision-making and operational processes.

In contrast, while data asset valuation involves recognizing the worth of data sets, it does not directly tackle the quality of the data itself. Communication mechanisms are essential for ensuring that stakeholders are informed and can collaborate effectively, but they do not inherently focus on data quality. Change management engagement deals with managing the transitions and transformations within an organization, which can impact data processes but is not specifically dedicated to enhancing the quality of data. Thus, the emphasis on setting improvement priorities clearly aligns with the goal of advancing data quality within an organization.