4.1 Data Quality Dimensions and Assessment

Data quality is crucial for effective business intelligence. It encompasses accuracy, completeness, consistency, timeliness, and validity. These dimensions ensure that data accurately represents real-world entities, supports decision-making, and maintains uniformity across systems.

Assessing data quality involves techniques like profiling, metrics, and rules. These methods help identify patterns, measure quality dimensions, and validate data against business constraints. Understanding the impact of quality issues and planning assessments are key steps in maintaining high-quality data for informed decision-making.

Data Quality Dimensions

Dimensions of data quality

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• Accuracy ensures data correctly represents real-world entities and events free from errors (incorrect values, duplicates, inconsistencies)
• Completeness requires all necessary data is present without missing values or records to sufficiently support business processes and decision-making
• Consistency maintains data uniformity across different systems and sources adhering to defined formats, data types, domains, and integrity through referential integrity and data lineage
• Timeliness provides data availability when needed, up-to-date reflecting the most current information with minimized latency for real-time or near-real-time decision-making
• Validity confirms data conforms to defined business rules and constraints, values fall within acceptable ranges or domains, and relationships and dependencies are maintained

Data Quality Assessment

Techniques for quality assessment

• Data profiling analyzes data to identify patterns, distributions, anomalies, generates summary statistics (minimum, maximum, mean, standard deviation), identifies data types, formats, domains, and detects missing values, duplicates, inconsistencies
• Data quality metrics define quantitative measures to assess data quality dimensions (accuracy rate, completeness rate, consistency rate, timeliness rate, validity rate), calculate metrics based on data profiling results and business requirements, track metrics over time to monitor trends and improvements
• Data quality rules define business rules and constraints to validate data quality (data type checks, range checks, format checks, referential integrity checks), implement rules in data validation and cleansing processes, automate rule-based checks in ETL workflows and data pipelines

Impact of data quality issues

• Missing or incomplete data impairs analysis and reporting leading to inaccurate or incomplete insights and decisions
• Inconsistent data formats and values hinder integration and interoperability requiring transformation and standardization efforts
• Duplicate or redundant data increases storage and processing costs confusing users and leading to inconsistent reporting
• Inaccurate or invalid data misleads business users and stakeholders resulting in incorrect decisions and actions
• Outdated or untimely data hinders real-time decision-making and responsiveness leading to missed opportunities or suboptimal outcomes

Data quality assessment planning

1. Define data quality objectives and scope by identifying critical data assets and business processes, prioritizing data quality dimensions based on business impact and feasibility
2. Select data quality assessment techniques and tools determining appropriate data profiling, metrics, rules for each data asset, evaluating and selecting data quality software and platforms
3. Establish data quality assessment timeline and resources defining project phases, milestones, deliverables, assigning roles and responsibilities for assessment activities
4. Execute data quality assessment activities performing data profiling, calculating metrics, applying data quality rules, documenting issues and root causes
5. Analyze and communicate data quality assessment results summarizing key findings and insights, visualizing metrics and trends using charts, dashboards, reports, presenting results to stakeholders and recommending improvement actions
6. Develop and implement data quality improvement plan prioritizing issues based on business impact and feasibility, defining improvement initiatives and projects, establishing data governance processes and policies to maintain quality over time