Data Quality and Checks

Data Validation

1. Purpose:

   • Ensure that the data uploaded to the platform is accurate, complete, and reliable.

2. Validation Procedures:

   • Schema Validation: Verify that the data conforms to the predefined schema, including field types, required fields, and data formats.
   • Range Checks: Ensure that numerical data falls within expected ranges.
   • Consistency Checks: Check for logical consistency within the data, such as matching totals or coherent dates.
   • Uniqueness Checks: Verify that unique fields, such as IDs, do not contain duplicate values.
   • Referential Integrity: Ensure that relationships between different datasets are consistent and valid.

3. Automated Tools:

   • Utilize automated validation tools to quickly assess the accuracy and integrity of the data.
   • Set up automated alerts for any detected validation errors.

Data Cleaning Tools

1. Purpose:

   • Prepare the data for analysis by removing errors, inconsistencies, and irrelevant information.

2. Available Tools:

   • OpenRefine: A powerful tool for cleaning messy data, transforming it from one format into another, and extending it with web services.
   • Trifacta: A data wrangling tool that helps in cleaning and transforming data through an intuitive interface.
   • Pandas (Python Library): Provides data structures and functions needed to clean and manipulate data effectively.
   • Talend Data Preparation: An intuitive tool for cleaning, enriching, and sharing data.

3. Cleaning Procedures:

   • Removing Duplicates: Identify and remove duplicate records.
   • Handling Missing Values: Fill in missing values or exclude incomplete records as necessary.
   • Standardizing Formats: Ensure that all data follows a consistent format, such as date formats or capitalization.
   • Correcting Errors: Detects and corrects errors in the data, such as typos or mislabeling.

Quality Metrics

1. Purpose:

   • Assess the overall quality of the data to ensure it meets the required standards for analysis and decision-making.

2. Metrics:

   • Accuracy: Measure the degree to which data correctly describes the real-world object or event.
   • Completeness: Assess whether all required data is present and accounted for.
   • Consistency: Ensure that data is consistent across different datasets and throughout time.
   • Timeliness: Evaluate if the data is up-to-date and relevant for the current analysis.
   • Validity: Check if the data complies with the required format, type, and range constraints.
   • Uniqueness: Measure the degree to which all distinct records are unique and free of duplicates.
   • Integrity: Ensure that all data is correct and reliable, with strong referential integrity between datasets.

3. Implementing Quality Checks:

   • Regularly perform quality assessments using these metrics.
   • Generate reports to identify areas of improvement.
   • Implement corrective actions based on the findings to enhance data quality continuously.