Exploring the Core Processes- What Happens During Data Cleansing-

Which of the following occurs during data cleansing?

Data cleansing is a critical process in the field of data management and analysis. It involves identifying and correcting or removing errors, inconsistencies, and inaccuracies in a dataset. This process is essential to ensure the quality and reliability of data used for decision-making and analysis. In this article, we will explore some of the key activities that occur during data cleansing.

1. Identifying and correcting errors

One of the primary tasks in data cleansing is to identify and correct errors in the dataset. These errors can be due to various reasons, such as data entry mistakes, system failures, or incorrect data transformations. During this phase, data analysts and data scientists use various techniques to detect errors, such as outlier detection, data profiling, and data validation.

2. Handling missing values

Missing values are a common issue in datasets, and they can significantly impact the quality of analysis. During data cleansing, it is crucial to handle missing values appropriately. This can involve imputation techniques, such as mean, median, or mode imputation, or more sophisticated methods like multiple imputation or k-nearest neighbors.

3. Standardizing data formats

Data cleansing also involves standardizing data formats to ensure consistency across the dataset. This includes converting data types, normalizing text, and ensuring that dates and times are in a consistent format. Standardization helps in making the data more uniform and easier to analyze.

4. Removing duplicates

Duplicate data can lead to skewed results and inaccurate analysis. During data cleansing, it is essential to identify and remove duplicate records. This can be done by comparing key fields or using advanced techniques like fuzzy matching to identify potential duplicates.

5. Data transformation and enrichment

Data cleansing may also involve transforming and enriching the data to make it more suitable for analysis. This can include aggregating data, creating new variables, or integrating data from external sources. Data transformation and enrichment help in enhancing the value of the dataset and making it more informative.

6. Data validation and verification

Once the data has been cleansed, it is important to validate and verify the results to ensure the accuracy and reliability of the cleaned data. This involves performing various checks, such as cross-validation, benchmarking against known datasets, or involving domain experts to review the cleaned data.

In conclusion, data cleansing is a multifaceted process that involves several key activities. By identifying and correcting errors, handling missing values, standardizing data formats, removing duplicates, transforming and enriching data, and validating the results, organizations can ensure the quality and reliability of their data for better decision-making and analysis.