Data Analytics
Data Quality Checks to Implement on a new dataset
Performing data quality checks on a new dataset typically involves several key steps. Here are the key steps for conducting data quality checks:
- Identify the variables: Determine the variables or columns present in the dataset and understand their meaning and significance.
- Examine data types: Check the data types assigned to each variable (e.g., numeric, categorical, date/time) and ensure they are correctly assigned. Incorrect data types can lead to data integrity issues and hinder proper analysis.
- Check for missing values: Identify and handle missing values in the dataset. This includes checking for empty cells, placeholders, or inconsistent representations of missing values. Decide on an appropriate strategy to handle missing data, such as imputation or deletion.
- Validate data formats: Verify if the data adheres to the expected format for each variable. For example, ensure that date variables are in the correct date format, numeric variables have consistent decimal places, and categorical variables have predefined values.
- Assess data integrity: Look for any inconsistencies or anomalies within the dataset. Check for duplicates, inconsistent entries, outliers, or data that does not conform to defined business rules or constraints.
- Evaluate data range and distribution: Analyze the range and distribution of numeric variables to identify any unexpected values or patterns. This helps to detect potential errors or outliers that might require further investigation.
- Conduct data cross-referencing: Compare the data in the new dataset with external sources or other existing datasets to validate its accuracy and consistency. This step can help identify discrepancies or inconsistencies that need to be resolved.
- Perform statistical checks: Utilize statistical techniques to assess the data quality, such as calculating summary statistics, checking correlations, or conducting hypothesis tests. These tests can provide insights into the overall quality and reliability of the dataset.
- Document findings: Record the results of the data quality checks, including any issues or concerns identified, the actions taken to address them, and any transformations or modifications made to the dataset.
