[2026 Edition] What Is the Best Way to Perform Data Cleansing? A Guide to Methods, Procedures, and Key Considerations

The Meaning of Data Cleansing

Companies collect vast amounts of data, but not all of it is accurate or in a usable state.

Incorrect data registration or inconsistent formatting can prevent accurate data analysis and information management using databases. To effectively utilize data and information, it is necessary to organize it into a usable format.

Data Cleansing refers to the process of correcting or deleting duplicate data, inconsistent formatting, or mixed character types within a company's database.

The following are examples of data cleansing:

• Mixing of full-width and half-width characters
• Presence or absence of spaces and delimiters
• Corporate name formatting (e.g., abbreviations vs. full legal names)
• Errors in variant characters (for personal names)
• Formatting of addresses and phone numbers

"Data Consolidation," a term similar to data cleansing, refers to integrating duplicate items within a database. Data consolidation is sometimes considered a component of data cleansing.

Benefits of Understanding How to Perform Data Cleansing

What are the benefits for a company in understanding how to perform data cleansing? Below, we explain three key effects and benefits.

Improving Operational Efficiency

Knowing how to perform data cleansing improves operational efficiency. If data cleansing is not conducted, there is a risk that relevant data cannot be found during searches due to issues such as inconsistent formatting.

Data must be corrected to locate target information and save time on future searches. Identifying misspellings and inconsistencies within a massive database is time-consuming and interrupts ongoing operations. These tasks would be unnecessary if data were entered according to established rules from the start.

By performing data cleansing to eliminate inconsistencies and discrepancies, you can immediately locate relevant data through searches, saving the time and effort otherwise spent on manual corrections. Eliminating unnecessary tasks allows operations to proceed smoothly.

This increases the time available for marketing and other core business activities, or for necessary breaks, thereby driving operational efficiency. As a result, this leads to improved productivity.

Accelerating Decision-Making

Data cleansing accelerates corporate decision-making. Beyond the ease of finding data, the quality and accuracy of the data also play a significant role.

Utilizing well-maintained, high-quality data enables accurate data analysis. The results of such analysis serve as reliable evidence, which is instrumental in determining corporate strategy and direction.

By making critical decisions quickly, a company can implement effective strategies ahead of competitors. This increases the likelihood of improving performance and securing a solid position in the market.

Protecting Corporate Trust

Analyzing outdated or incorrect data will not yield accurate results. Data errors and deficiencies are the root causes of various troubles and issues.

For example, conducting sales activities based on incorrect customer data will not produce results, and presenting inaccurate information will cause a loss of customer trust. You may also inadvertently send emails or other communications to the wrong recipients due to errors in customer data.

Creating sales forecasts or reports based on inaccurate information will not lead to improved performance. If customer satisfaction and service quality decline, and serious errors accumulate, it can lead to a loss of the company's overall reputation.

Data cleansing is essential to protect the trust and credibility of your company.

Methods of Data Cleansing

Methods for data cleansing include manual execution or the use of specialized tools and services.

Manual Execution

Data cleansing can be performed manually, and no special qualifications are required. You can organize data manually using free tools and features such as spreadsheets or Excel.

It is also possible to process data by utilizing functions, programming languages, or database languages such as SQL. Advanced processing is best handled by engineers or programmers.

If no engineers are available, employees knowledgeable in data processing can handle the task to a certain extent. However, it is important to ensure that this does not interfere with their primary responsibilities.

Manual execution requires time, effort, and a certain level of skill and knowledge, making it increasingly complex and cumbersome as the volume of data grows. The likelihood of errors also increases.

Utilizing Specialized Tools and Services

As IT adoption rapidly advances, the number of companies providing tools and services dedicated to data cleansing has increased. Outsourcing to such external services is a highly effective option.

Even if your company lacks the necessary personnel or resources, introducing a tool allows for smooth data cleansing. Using tools or services enables rapid data integration and conversion, streamlining the workflow. While there is a cost associated with implementation, the simplification and automation of data processing allow for continuous, complex cleansing tasks.

There are various services and tools available, so it is important to compare and evaluate multiple options to introduce one that aligns with your company's objectives.

Key Considerations for Evaluating Data Cleansing Tools and Services

When introducing specialized tools or services, focus on the following points:

• Volume of Data Held
• Quality of Information Obtained
• Cost-Effectiveness

Companies providing external services and tools possess their own proprietary databases. The larger the volume of information they hold, the more comprehensive the data scrutiny can be. It is also important to look beyond volume and consider the quality, such as the freshness of the data.

Check the available information categories as well. For corporate information, if you can supplement basic details like industry, company name, and address with data such as capital, number of employees, and annual sales, you can create higher-quality data.

It is also important to understand the scale and volume of your own database. After clarifying how much budget can be allocated and what returns can be expected from the implementation, evaluate the appropriate features and plans.

How to Proceed with Data Cleansing

Data cleansing follows a flow of identifying, importing, formatting, and classifying data. Below, we explain the specific steps.

Identifying Data

First, check the databases your company handles to identify any missing or duplicate information. It is necessary to correctly grasp the current situation and identify the scope for data cleansing.

Determine the data format to be cleansed and extract only the necessary data from multiple databases. Performing cleansing on data containing unnecessary items is inefficient as it increases redundant work.

Importing Data

Once you have gathered the necessary data, move to the import stage. After determining the scope of data to be imported, consolidate various file formats—such as Excel, Word, log files, CSV, and PDF—into a single database. Converting inconsistent file formats into a uniform format during the import process will make subsequent processing smoother.

Consolidating into a single database reduces manual effort and may reveal new correlations or relationships between data points.

Formatting Data and Executing Data Cleansing

After importing the data, perform data formatting. Formatting is the most critical process in data cleansing.

Based on standards established by your company, correct inconsistencies in notation or character styles and delete unnecessary data. It is recommended to set these standards according to the specific purpose of the data cleansing.

Examples are provided below:

• Standardize full-width alphanumeric characters to half-width
• Remove hyphens from phone numbers and postal codes
• Standardize multiple entries of the same company name
• Update old addresses to new ones

By clarifying standards and rules, you can perform cleansing efficiently.

Classifying Data

After formatting, organize and classify the data. It is important to create lists based on the intended use of the data and store it in appropriate locations.

It is advisable to manage data in a format that allows for immediate retrieval when needed. Classified data can then be utilized for marketing and sales activities.

Points of Caution for Data Cleansing

What points should be kept in mind during data cleansing?

Perform Visual Checks

Even if you automate data cleansing using tools, if the tool settings are incorrect, the tool will not perform as intended. After performing data cleansing, be sure to visually verify whether the cleansing aligns with your objectives. If the intended results were not achieved, you must re-examine the tool settings, share them with relevant stakeholders, or go back to the upstream process to ensure the purpose of the data cleansing remains consistent.

Combining tool usage with visual verification further enhances the effectiveness of data cleansing.

Perform Regularly

Data cleansing is not a one-time task. Corporate data and information change daily. If new data entries contain incorrect registrations or inconsistent formatting, data quality will decline.

It is important to perform data cleansing frequently to maintain data quality. By repeating the process, areas for improvement will become clear, and the methodology can be made more efficient. Establishing and communicating rules for data entry to employees will save time and effort.

Set the frequency of implementation as high as possible and establish a regular schedule, such as once a month. In addition to regular execution, it is necessary to perform cleansing as needed if new data analysis is required due to business expansion or if the volume of data held by your company increases significantly.

Data cleansing is essential for the effective utilization of data. Properly implementing data cleansing improves operational efficiency and protects corporate credibility. Methods for implementation include manual execution as well as the use of services and tools.

When executing data cleansing in-house, follow the flow of identifying, importing, formatting, and classifying data. To maximize the value of the data obtained through cleansing, it is important not to treat it as a one-time task, but to perform data cleansing regularly to keep the data in a consistently organized state.