2026 Edition: How to Perform Data Cleansing? Methods, Procedures, and Key Considerations Explained

The Meaning of Data Cleansing

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

Incorrect data entry or inconsistent formatting can hinder accurate data analysis and information management. 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, inconsistently formatted, or mixed-character data within a company's database.

Examples of data cleansing include the following:

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

Data Matching, a term similar to data cleansing, refers to the process of integrating duplicate items within a database. Data matching is sometimes considered a subset of data cleansing.

Benefits of Understanding Data Cleansing Methods

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

Understanding data cleansing methods improves operational efficiency. Without data cleansing, inconsistent formatting can make it impossible to find relevant data even when searching.

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

By performing data cleansing to eliminate inconsistencies and discrepancies, you can find the relevant data immediately upon searching, 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 tasks, as well as for necessary breaks, thereby promoting operational efficiency and ultimately leading to higher 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.

Using well-maintained, high-quality data enables accurate data analysis. The results of such analysis serve as reliable evidence, supporting the determination of 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 Trust

Analyzing old or incorrect data will not yield accurate results. Data errors and deficiencies are the root cause of various troubles and problems.

For example, sales activities based on incorrect customer data will not be effective, and presenting inaccurate information can lead to a loss of customer trust. You might also inadvertently send emails 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. A decline in customer satisfaction and service quality, compounded by significant errors, 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 functions such as spreadsheets or Excel.

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

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

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 accelerates, the number of companies providing tools and services dedicated to data cleansing has increased. Engaging such external services is highly effective.

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 process. While there is a cost to 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 several options to introduce one that aligns with your company's objectives.

Points for Considering 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 verification can be. It is also important to look beyond volume and consider quality, such as the freshness of the data.

Check the information items provided as well. For corporate information, if you can supplement basic data such as industry, company name, and address with information like 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 company's database. Clearly define your budget and the expected return on investment to 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.

Identify Data

First, check your company's database to identify any missing or duplicate information. It is necessary to correctly grasp the current situation and identify the targets for data cleansing.

Decide on the data format to be cleansed and extract only the necessary data from multiple databases. Performing cleansing on data containing unnecessary items is inefficient and increases the workload.

Import Data

Once you have gathered the necessary data, move to the import stage. After determining the scope of the 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.

Format Data and Execute Data Cleansing

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

Based on your company's established standards, correct inconsistencies in formatting or character usage and delete unnecessary data. It is recommended to set standards based on the specific objectives of the data cleansing.

Examples are shown below:

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

Clarifying standards and rules allows for efficient cleansing.

Classify 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.

Manage the data in a way that allows for immediate retrieval when needed. Classified data can be utilized for marketing and sales activities.

Points to Note for Data Cleansing

What points should be kept in mind during data cleansing?

Perform Visual Confirmation

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, visually confirm whether the cleansing has been carried out according to 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 objectives of the data cleansing remain aligned.

Combining the use of tools with visual confirmation 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 errors or inconsistent formatting, data quality will decline.

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

Set the frequency of implementation as high as possible and schedule it regularly, such as once a month. In addition to regular implementation, it should also be performed as needed when new data analysis is required due to business expansion or when the volume of data held by the company increases significantly.

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

When performing data cleansing in-house, follow a process of data identification, ingestion, formatting, and classification. 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 ensure that data remains in an optimized state.