Meaning ● Data Quality Principles, within the SMB framework of growth, automation, and implementation, denote the guidelines ensuring business data is fit for its intended uses in operations, decision-making, and strategic planning. These principles are critical for SMBs leveraging data-driven strategies to enhance competitiveness.
Data Quality Principles ● Accuracy is paramount; business data must be free from errors and reflect true values. Completeness dictates that essential data fields should never be vacant, ensuring a comprehensive understanding of business information. Consistency maintains uniformity across different datasets, preventing discrepancies that might undermine business analysis. Validity confirms business data conforms to predefined formats and rules, guaranteeing reliability in business applications. Timeliness means the business data should be available when needed and reflective of the present situation. ● Furthermore, uniqueness mandates that business records are distinctly identifiable, avoiding duplication. Reasonableness confirms data falls within acceptable parameters; ensuring it aligns with real-world possibilities, which helps maintain data credibility in the SMB. Accessibility guarantees authorized business users can readily access and utilize data for necessary operations and decisions. Relevance ensures business data is useful and applicable to the task, so it directly addresses business needs. Data quality principles are vital for SMB growth, particularly with increased automation, ultimately improving data integrity.
