Example Dataset Quality Report.txt

Dataset Quality Report

Dataset Summary
- Dataset Name: Sales Data Q1 2024
- Number of Records: 10,000
- Number of Features: 12
- Date of Analysis: May 17, 2024

Data Integrity
- Unique Identifiers: Each record has a unique identifier (OrderID). No duplicates found.
- Data Types Consistency: All fields conform to their expected data types.
  - OrderID: Integer
  - CustomerID: Integer
  - OrderDate: Date
  - ProductID: Integer
  - Quantity: Integer
  - Price: Float
  - Total: Float
  - Region: String

Completeness
- Missing Values:
  - CustomerID: 0 missing values
  - OrderDate: 2 missing values
  - ProductID: 0 missing values
  - Quantity: 0 missing values
  - Price: 0 missing values
  - Total: 0 missing values
  - Region: 10 missing values

Anomalies
- Outliers:
  - Quantity: 5 outliers detected (values > 100)
  - Price: 3 outliers detected (values > $1000)
- Inconsistent Data:
  - Total: 50 records where Total ≠ Quantity * Price

Compliance Checks
- Date Range Validity: All OrderDate values are within the expected range (Jan 1, 2024 - Mar 31, 2024), except for 2 records with missing dates.
- Region Codes: 10 records missing Region values. All other values conform to expected region codes.

Recommendations
1. Address Missing Values:
   - Investigate and fill in the missing OrderDate and Region values.
   - Consider imputing missing values where appropriate, using methods such as mean imputation for numerical data or mode imputation for categorical data.

2. Handle Outliers:
   - Review records with outliers in Quantity and Price to determine if they are errors or valid high values.
   - Implement outlier treatment strategies such as capping or transformation if necessary.

3. Fix Inconsistent Data:
   - Correct the 50 records where Total does not equal Quantity * Price. This may involve recalculating the Total or correcting the input errors in Quantity or Price.

4. Enhance Data Validation:
   - Implement stricter data validation rules at the point of data entry to prevent missing and inconsistent values.
   - Regularly audit and clean the dataset to maintain high-quality data.

End of Report