Which of the following is NOT a stage of the Data Quality Management Cycle?
Answer(s): D
The stages of the Data Quality Management Cycle typically include Plan, Do, Check, and Act. These stages align with the Plan-Do-Check-Act (PDCA) cycle, a well-known approach for continuous improvement."Intervene" is not part of the standard cycle. The "Act" stage involves making adjustments based on the findings in the "Check" stage to improve data quality.
An enterprise data architect works with enterprise architecture to:
Answer(s): E
An enterprise data architect works with enterprise architecture to model the data to the overall architecture.This ensures that the data architecture aligns with the broader enterprise architecture, supporting the organization's strategic goals. The data architect ensures that data flows efficiently through the systems, is properly structured, and is accessible across different parts of the business.
What is the final step in the development of business-data-driven roadmap?
The final step in developing a business-data-driven roadmap is to establish the sequence of projects that will be used to establish the data value chain. This step involves determining the order in which data-related initiatives and projects will be executed to support the business capabilities and objectives, ensuring that the data strategy aligns with the overall business goals.
Enterprise Data architects effect change through:
Enterprise Data Architects drive change through an adopted data architecture roadmap. This roadmap outlines the strategic direction and steps for evolving the organization's data architecture in alignment with business goals. It provides a clear plan for implementing and managing data systems, tools, and processes, ensuring that data architecture supports the overall business strategy and operations.
Data replication is useful as it provides:
Data replication is useful because it allows the distribution of data across multiple databases or systems, which helps to spread the workload. This can improve performance, ensure availability, and enhance the scalability of data management. By replicating data, organizations can also ensure better disaster recovery and data redundancy.
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