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Data Cleansing - What is it?

Data cleansing has become a popular topic with the recent focus on cybersecurity and information security. With organizations leveraging data analytics to enhance business performance and obtain competitive advantages over competitors, business operations and decision-making are becoming increasingly data-driven. As a result, clean data is essential for BI and data science teams, as well as business executives, marketing managers, sales reps, and operational employees. This is especially true in retail, financial services, and other data-intensive industries, but applies to businesses of all sizes.

The core of any customer management approach is data quality. Good quality data allows for analytics, campaign management, customer experience, and reporting; get it right, and it can have a long-term positive impact on your company's efficiency and reputation. After all, Your data-driven insights and analyses are only as good as the data you're working with.

What is Data Cleansing?
What is Data Cleansing?

Customer records and other company data may be inaccurate if data isn't properly cleaned, and analytics programs may deliver erroneous information. This can lead to poor business judgments, mistaken strategies, missed opportunities, and operational issues, all of which can lead to higher costs and lower revenue and profits.

The amount of data we have access to has grown in tandem with the amount of potential for error. As a result, we rely on data cleansing to improve the efficiency of our data management systems. Data cleansing enhances the integrity and usefulness of our data.


What is Data Cleaning?

Data cleansing is a type of data management. Data cleaning, also known as scrubbing or data cleaning, identifies and corrects errors, duplicates, and unnecessary data in a raw dataset. Businesses amass a lot of data on their customers and prospects over time. Information can quickly become outdated, from the basics, like contact names and addresses to financial facts and product portfolios.

Missing numbers, misplaced entries, and typographical errors are common data flaws. Data cleansing sometimes necessitates filling in or correcting particular values, while entries must be eliminated in others.

The term "dirty data" refers to data that contains these types of flaws and inconsistencies. According to estimates, only 3% of data fulfills essential quality criteria, and dirty data costs corporations in the United States about $3 trillion yearly.

Thus, data cleansing is the act of going through all of the data in a database and eliminating or updating any information that is incomplete, erroneous, incorrectly structured, duplicated, or irrelevant.

Though data cleansing can and does include deleting information, it is mainly concerned with updating, correcting, and combining data to make your system as efficient as possible.

Data Cleansing Steps

Now that we know what is a data cleanse we must also understand that it is usually threefold: maintaining information for existing customers to enable relevant communication, maintaining information that supports business functions such as collecting payments and making deliveries, and finally, data cleansing helps many industries' compliance requirements, including data protection legislation such as GDPR.


Data cleansing is removing data from your dataset that does not belong there. The process of changing data from one format or structure to another is known as data transformation. Data transformation, often known as data wrangling or data munging, is the process of changing and mapping data from one "raw" data type into another for warehousing and analysis.

Data transformation facilitates data processing. It should be cleaned and transformed before importing the data into the warehouse. Data transformation can be simple or complex depending on the changes that must be made to the data. Data transformation responsibilities include standardizing data, character set conversion, encoding handling, separating or merging fields, converting units of measurement into a standard format, aggregating, consolidating, and deleting duplicate data.


Organizations believe that roughly 30% of their data is inaccurate on average. Companies lose 12% of their overall sales due to incorrect data, losing more than just money. Cleansing data generates consistent, structured, and correct data, allowing for informed and intelligent decisions. It also identifies areas where upstream data entry and storage environments might be improved, saving time and money today and in the future.

Why is Data Cleansing Important?

Besides, you'll get the most out of your marketing efforts if you have the most up-to-date, correct information.

  • Better employee productivity and efficiency: Data that has been cleansed does not require employees to spend time fixing it. Employees may execute their responsibilities with complete faith that the data they are using is current and accurate to the fullest extent possible. When adequately cleansed, clean data gives valuable insights into internal needs and processes. It can also boost productivity and efficiency inside the company.

  • Higher sales and lower costs: According to studies, organizations lose an average of 27 percent of their revenue owing to erroneous data. Data cleaning is the most effective way to avoid the costs that arise when organizations are busy processing errors, correcting incorrect data or troubleshooting. Putting in the time and effort to clean up your data will pay off handsomely and improve your bottom line.

  • Improved Decision Making: Data quality is crucial since it directly affects your company's ability to make intelligent judgments and create effective strategies. No organization can afford to waste time and resources rectifying errors caused by inaccurate data.

  • More satisfied customers: Higher-quality data enables you to understand better how to improve the customer experience at every stage of the process, from prospecting to customer support and retention.

  • Competitive Advantage: The more effectively a company serves the needs of its customers, the faster it will rise above its competitors. Tools for data cleansing can help you discover growing client needs and keep up with emerging trends by providing accurate, complete information. Data cleansing can result in higher response rates, better leads, and a better customer experience.

The Data Cleansing Steps

Removing duplicate data: Duplicate data consumes server or processing resources while providing no value. Duplicates typically appear when data from multiple sources (e.g., spreadsheets, websites, and databases) is combined or when a customer has various contact points with a company or has submitted redundant forms. Duplicate records may also skew your insights into your customers. As a result, removing duplicate data from your warehouse is an essential step in the data cleansing process.

The Steps of Data Cleansing
  • Removing irrelevant data: Identify and remove unnecessary data from your database or warehouse. Complete data unconnected to the problem at hand can slow down processing time. These unrelated observations are not deleted from the source but excluded from the current analysis.

  • Manage incomplete data: Data may be missing values for various reasons (for example, customers failing to provide certain information). Addressing it is critical to analysis because it prevents bias and miscalculations. Discretion is required when deciding whether to drop, impute, or flag missing data. What you do with the missing data affects the accuracy of your analytics.

  • Identify Outliers: Data points far apart from the rest of a population can significantly distort the data's reality. Outliers can be identified using visual or numerical techniques such as box plots, histograms, scatterplots, or z-scores; when used as part of an automated process, it allows for quick assumptions, testing of those assumptions, and confidently resolving data issues. Outliers can be included or excluded from an analysis depending on how extreme they are and what statistical methods are used.

  • Correct Structural Errors: It is critical to correct typographical, capitalization, abbreviation, and formatting errors. The data type for each column is examined, and it is ensured that the entries are correct and consistent, which may include standardizing fields and removing unwanted characters like extra whitespace.

  • Standardizing data: Cleaning data entails standardizing it so that each value has a consistent format. You can begin by making all strings the same case (upper or lower). When standardizing measurements, metric conversion may be required. Make sure that all other units of measurement in your database are standard.

  • Validate Validation guarantees that your data is accurate and ready to be analyzed. It's a chance to check that data is correct, complete, consistent, and uniform. Although this occurs as part of an automatic data cleansing process, it's still necessary to run a sample to confirm everything is in order. This is also an excellent time to record which equipment and procedures were employed during the cleansing process.


Data cleansing is necessary for an accurate, robust analysis, but it is a time-consuming, fragmented process that wastes time and resources. Many businesses have decided to automate and standardize their data cleansing process because it is time-consuming and prone to errors. Using tools for data cleansing is a simple method to increase your company's data efficiency and consistency and your ability to make educated decisions.

Zantaz will help you evaluate and improve the quality of your data. It links to hundreds of various data sources, ensuring that all of your data, no matter where it comes from, is reliable, readable, and enriched. Say goodbye to a manual, siloed process that wastes time and resources, and learn about how Zantaz is redefining enterprise data management.

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