Data cleansing is the process of identifying and correcting errors or inaccuracies in a dataset. When merging data from multiple sources, it’s easy to end up with incomplete, duplicated, or mislabeled information, making algorithms unreliable. But data cleansing can solve these issues, increasing the reliability and value of your data.
What is data cleansing?
Data cleansing, also known as data sanitization, is the process of identifying and correcting corrupted or inaccurate records from a dataset, table, or database. It involves detecting incomplete, incorrect, inaccurate, or irrelevant portions of the data, then replacing, modifying, or deleting them. Data cleansing can be done interactively using wrangling tools, or as a batch process using scripts or a data quality firewall.
Why is data cleansing important?
Data cleansing eliminates erroneous, inaccurate, or irrelevant data, increasing the consistency, reliability, and value of your organization’s data. Common inaccuracies are missing values, misplaced entries, and typos. Data scrubbing can be the correction or removal of incorrectly formatted or duplicate data or records. Such data removed in this process is often referred to as “dirty data”. Therefore, data cleansing is essential to maintain data quality.
What is the data cleansing process?
There is no universal process for data cleansing, as it varies depending on the dataset. However, creating a template that strictly follows a set of steps is crucial to ensure consistent data cleaning. The data cleansing process often involves these steps:
- Defining data cleansing requirements
- Performing data profiling and analysis
- Cleaning data, including standardization and normalization
- Validating data
- Conducting data quality checks and monitoring
FAQs
What are some common issues in data cleansing?
Common issues in data cleansing include missing data, duplicates, inconsistencies, formatting errors, and outdated information. Identifying and fixing these issues will ensure that your data is accurate and reliable.
How often should data cleansing be done?
Data cleansing depends on the dataset and its usage. However, it is recommended to perform data cleansing periodically, especially if your data undergoes continuous updates and changes. It’s also important to conduct data quality checks regularly.
What are the benefits of data cleansing?
Data cleansing has many benefits, such as:
- Improved data quality
- Reliable data for decision-making
- Reduced errors and inaccuracies
- Enhanced efficiency and productivity
- Cost savings on IT resources and storage
Final Thoughts
Data cleansing is critical for maintaining accurate and reliable data. By following a set process and addressing common issues, your data can become consistent and provide optimal results for your organization’s decision-making processes.
