Introduction

Data cleansing is the bane of many data scientists’ existence: according to a 2016 survey, it occupies 60 percent of their time, yet 57 percent of data scientists still say that data cleansing is the “least enjoyable” part of their job.

Yet data cleansing occupies a vital role in the ETL (extract, transform, load) process, helping to ensure that information is consistent, accurate, and high-quality. What’s more, you can make data cleansing significantly less painful by following a few simple best practices. In this article, we’ll discuss what data cleansing is and how to do it right.

Table of Contents:

 Data Cleansing: Definition

 4 Benefits of Data Cleansing

 Data Cleansing: Best Methods & Practices 

 Conclusion

Data Cleansing: Definition

Simply put, data cleansing is the act of cleaning up a data set by finding and removing errors, either by hand or with automated software tools. Data cleansing is also referred to as “data cleaning” or “data scrubbing”—alternate terms for the same metaphor.

Enterprise data sets can decay in quality at an alarming rate. For example, most analysts estimate that B2B customer data decays at a rate of at least 30 percent per year, and as high as 70 percent annually for industries with high turnover.

Information that is out-of-date, corrupt, duplicated, missing, or incorrect can dramatically skew the results of your analysis and reporting processes. The goal of data cleansing is to repair the holes and inconsistencies present in your data set, so that organizations dependent on accurate information can continue to enjoy the benefits of high-quality data.

4 Benefits of Data Cleansing

  • More accurate insights and predictions: The well-known software development principle of “garbage in, garbage out” (GIGO) asserts that poor input data will necessarily result in bad outputs. The more accurate your enterprise data, the better performance from the algorithms and models that use this data. 
  • Better employee productivity and efficiency: Data that is cleansed is data that people don’t have to spend their time correcting themselves. Employees can do their jobs with full confidence that the information they use is up-to-date and correct to the greatest extent possible.
  • Higher revenues and lower costs: According to a study by Experian, companies estimate that they lose 12 percent of their revenue, on average, due to inaccurate data. Investing time and effort into data cleansing will reap major rewards and significantly improve your bottom line.
  • More satisfied customers: Higher-quality data helps you understand how to improve the customer experience at every stage of the process, from the initial contacts with prospects to customer support and retention.

Data Cleansing: Methods and Best Practices

Not all data cleansing techniques and practices are created equal. Data workers sometimes use spell checkers and histograms to find outliers in the data that are likely errors. A variety of software vendors offer complete data cleansing tools that can easily check the accuracy of your records and scrub them for duplicates. Even with these automated tools, however, some degree of manual intervention is usually required to provide a sanity check.

There are also a few best practices for data cleansing that nearly every organization should follow. When cleaning your enterprise data, consider making the following adjustments:

  • Names: A person’s full name may be written in a variety of formats. For example, the family name may be placed before or after the given name, and middle initials may be included or excluded. Data cleansing should recognize that all of these formats refer to the same person.
  • Dates and times: There may be even more ways to write a date than to write a name: MM/DD/YYYY, YYYY-MM-DD, and DD MM YYYY are just a few examples. In addition, times may be given in 12-hour or 24-hour format, and may or may not include seconds.
  • Locations: The same location may be referred to by several names, or by its latitude and longitude. For example, the United States may be written as “U.S.”, “US”, “USA”, or “United States of America.”
  • Numbers and currencies: Many European countries use periods rather than commas to separate groups of three digits, and use commas rather than periods as the decimal separator—exactly the opposite of the American system. Meanwhile, you may want to convert foreign currencies into their local equivalent.

Conclusion

Data cleansing is a vital yet often misunderstood part of data management, helping to sanitize and fact-check information as it moves down the data pipeline. The Xplenty data integration platform enables users to cleanse and reformat your data during ETL, transforming it to the required target format. Get in touch with our team of data experts today to set up a free trial of Xplenty.