Data cleansing is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in data. It involves detecting and removing duplicate records, correcting spelling errors, standardizing data formats, and removing irrelevant or incomplete data. 

The benefits of data cleansing include: 

1. Improved accuracy: Data cleansing can improve the accuracy of your data by eliminating errors, inconsistencies, and duplications. Clean data can lead to better insights, analysis, and decision-making.
2. Increased efficiency: With clean data, you can reduce the time and resources needed to analyze and process your data. This can lead to increased efficiency and productivity.
3. Better decision-making: Clean data can provide a clearer picture of your business operations, customers, and market trends. This can help you make better-informed decisions and develop more effective strategies.
4. Improved customer satisfaction: Clean data can help you better understand your customers and their needs, allowing you to provide more personalized and targeted marketing campaigns and customer service.
5. Reduced cascading costs: Significant reduced costs such as the processing of returned postal, bounced emails, time to search for missing information, etc.
6. Cost savings: By identifying and eliminating inaccurate or irrelevant data, you can reduce storage costs and avoid making costly business decisions based on inaccurate information. 

In summary, data cleansing is a crucial process that can help organizations to improve data accuracy, increase efficiency, make better decisions, improve customer satisfaction, and reduce admin as well as cascading costs. 

In general, 'data cleaning' and 'data cleansing' refer to the same process of identifying and correcting errors, inconsistencies, and inaccuracies in data. However, some people may use the terms slightly differently, depending on their specific context or industry. In some cases, "data cleaning" may be used more broadly to refer to any process that involves preparing data for analysis, which can include tasks like formatting, transforming, and structuring data in a way that makes it more usable. On the other hand, "data cleansing" may be used more specifically to refer to the process of identifying and correcting errors in data, such as removing duplicates, filling in missing values, and correcting formatting or spelling mistakes. 

Data cleansing and data scrubbing are terms that are often used interchangeably, but they actually refer to slightly different processes.  

1. Data cleansing: also known as data cleaning, is the process of identifying and correcting or removing errors and inconsistencies in data. This includes tasks such as removing duplicates, correcting misspellings, filling in missing values, and formatting data to ensure consistency. The ultimate goal of data cleansing is to improve the accuracy and completeness of the data, which in turn helps to improve the quality of any analysis or decision-making that relies on that data.
2. Data scrubbing: on the other hand, is a more comprehensive process that involves identifying and removing any sensitive or confidential information from a dataset. This can include personal information such as names, addresses, and Social Security numbers, as well as financial information or other types of confidential data. The goal of data scrubbing is to protect the privacy and security of individuals or organizations whose data is being used, while still allowing the data to be used for analysis or other purposes. 

In summary, data cleansing is focused on improving the accuracy and completeness of data, while data scrubbing is focused on removing sensitive or confidential information from a dataset. Only Once focusses on data cleansing.