ETL & Data Quality Issues

Data Accuracy

• Incorrect or erroneous data can lead to misleading insights.
• Issues include typos, calculation errors, and data entry mistakes.
• Examples: Incorrect customer addresses, wrong product prices, or inaccurate sales figures.

Data Completeness

• Missing data can hinder analysis and reporting.
• Issues include empty fields, partial records, or missing attributes.
• Examples: Missing customer contact information, incomplete order details, or absent product descriptions.

Data Consistency

• Inconsistent data formats or standards can create challenges in data integration.
• Issues include varying data types, different date formats, or conflicting data values.
• Examples: Using different date formats for birthdates, inconsistent product naming conventions, or multiple ways to represent customer status.

Data Duplication

• Duplicate records can lead to inflated metrics and inaccurate analysis.
• Issues include identical or similar records with slight variations.
• Examples: Duplicate customer records, repeated order entries, or redundant product information.

Data Timeliness

• Outdated data can provide an inaccurate picture of current conditions.
• Issues include delayed data updates or historical data being used for current analysis.
• Examples: Using last year's sales data for current forecasting, outdated customer contact information, or stale inventory levels.

Data Validity

• Adherence to business rules and constraints.
• Correct data types and formats.
• Compliance with industry standards and regulations.

Data Uniqueness

• Ensuring that each record represents a distinct entity.
• Identifying and handling duplicate or near-duplicate records.

Data Consistency

• Maintaining uniformity across different data sources and systems.
• Aligning data definitions and formats.
• Resolving conflicts in data values.

Data Integrity

• Ensuring data accuracy, completeness, consistency, and validity.
• Preventing data corruption and loss.

Data Relevance

• Ensuring data is meaningful and useful for its intended purpose.
• Aligning data with business objectives.

Data Accessibility

• Ensuring data is readily available to authorized users.
• Implementing appropriate security measures.

Data Understandability

• Clear and concise data definitions and metadata.
• Effective data documentation.

Data Security

• Protecting data from unauthorized access, modification, or disclosure.
• Implementing encryption and access controls.