Data Quality and Data Cleaning Techniques

"In a world of more data, the companies with more data-literate people are the ones that are going to win.” — Miro Kazakoff, senior lecturer, MIT Sloan

1. Understanding Data Quality Issues

Data quality encompasses the accuracy, completeness, consistency, and reliability of data. Here are common data quality issues to consider:

- Missing Data: Incomplete or missing data points can undermine analysis validity.

- Outliers: Extreme values that deviate significantly from the norm may skew analysis results.

- Inconsistent Data: Errors, duplications, or variations in data entry can lead to inconsistencies.

- Data Accuracy: Incorrect or erroneous data collection or recording can compromise accuracy.

- Data Relevancy: Ensuring data aligns with research or analysis objectives is essential for relevance.

2. Data Validation and Verification

Data validation involves checking the integrity and correctness of data by performing various checks, such as format validation, range validation, or logical validation. Verification ensures that data has been entered or recorded accurately by comparing it against original sources or other reliable references.

3. Effective Data Cleaning Techniques

Data cleaning, or data scrubbing, identifies and corrects errors, inconsistencies, or inaccuracies in collected data. Common techniques include:

- Handling Missing Data: Imputation or deletion addresses missing values.

- Outlier Treatment: Assessing validity and deciding on exclusion, transformation, or imputation.

- Data Standardization: Ensuring consistent formats, units, and representations.

- Removing Duplicates: Eliminating redundant entries for accuracy.

- Data Normalization: Transforming data to a common scale or format for meaningful comparisons.