Let us look at examples of data that require cleansing.
The image below shows basic information in a customer database, such as company names, contact names, addresses, and phone numbers.
As shown here, differences in notation and formatting can cause the same information to be identified as separate records, resulting in a database that is difficult to use for analysis.
Inconsistencies are caused by the following four factors:
1. Inconsistencies between full company names and abbreviations (e.g., Kabushiki Kaisha vs. (KK)) or errors in prefix/suffix placement
2. Differences in spacing or character styles
3. Mixing of formal/abbreviated forms and full-width/half-width characters
4. Presence or absence of hyphens, parentheses, area codes, or missing fields
These data issues often arise when multiple sales or marketing staff enter data without unified entry rules. The role of data cleansing is to correct these inconsistencies and errors to improve data integrity and accuracy.
Benefits of Data Cleansing
What are the benefits of performing data cleansing? Here, we explain four representative advantages.
1. Improved Productivity
The first benefit is improved productivity.
By optimizing data through cleansing, you can expect productivity gains not only within a specific department but across the entire company. Conversely, when data is flawed, it becomes difficult to extract necessary information, and corrections must be made every time an error is discovered.
By organizing data in advance through data cleansing, there is no need to make corrections on a case-by-case basis during daily operations. Reducing unnecessary tasks allows employees to focus on core business activities, increasing productivity within the same working hours. Additionally, employees can work more comfortably without the stress of redundant tasks, which also improves job satisfaction. Ultimately, this leads to higher productivity for the entire organization.
2. Enhanced Data Analysis Accuracy
The second benefit is enhanced data analysis accuracy.
High-precision data analysis relies on the consistency and accuracy of the underlying data. Marketing initiatives that leverage customer data require highly accurate analysis. By correcting missing or incorrect information and standardizing data formats through cleansing, more precise analysis becomes possible. Performing this regularly enables effective marketing strategies, such as identifying high-value customers.
Furthermore, data cleansing provides benefits when measuring the effectiveness of marketing outcomes. If you wish to perform high-precision analysis using your company's data, it is important to foster a shared understanding within the company that data cleansing is a vital measure.
3. Improved Decision-Making Capability
The third benefit is improved decision-making capability.
The quality of your company's data influences sound decision-making and the formulation of effective marketing strategies. For example, if referenced data contains errors, missing parts, or outdated information that has not been corrected, there is a risk of making incorrect decisions or strategies. If such processes proceed, significant time and effort may be lost by the time the errors are discovered. Even without errors, data loses its freshness over time, leading to a decline in quality.
Accurate information is essential for seamless data utilization. Perform data cleansing regularly to maintain the accuracy required for effective decision-making.
4. Cost Reduction
Finally, data cleansing is effective for cost reduction.
First, by consolidating inconsistent data formats, data extraction becomes easier. Consequently, there is no need to use expensive tools for data extraction.
Furthermore, you can avoid wasteful sales activities based on outdated or incorrect data, saving costs that would have otherwise been incurred. Additionally, by deleting unnecessary data, you can reduce server maintenance costs. Moreover, by reducing redundant tasks and improving operational efficiency, you can curb unnecessary labor costs, such as overtime pay, among many other benefits.
How to Proceed with Data Cleansing
The process of data cleansing varies by company and organization. Here, we briefly explain the general steps.
For more detailed information, please refer to the article below.
Reference Article: [2024 Updated Edition] What Is the Method for Data Cleansing? Explaining Procedures, Approaches, and Precautions
1. Select and Collect Important Data
The first step in data cleansing is to define the data scope and collect important data from within it. Collect only the necessary data from various file formats, such as CSV or XML, and consolidate them into a single database. By consolidating, you may discover relationships between data that were previously invisible.
A key point in selecting and collecting important data is to determine the scope of the data to be collected in advance. Collecting irrelevant or outdated data is meaningless. Conversely, it may lead to unnecessary work, so defining the scope allows for a smoother transition to data cleansing.
2. Execute Cleansing
Before proceeding to the actual cleansing, organize the data and delete unnecessary parts. This process is sometimes called "Data Matching" and is often considered part of data cleansing. Unnecessary parts mainly refer to duplicate data.
Next is the correction and repair of data, which is a critical item in data cleansing. This involves correcting and standardizing incorrect data, full-width/half-width characters, adding missing data, and updating outdated information. It is also important to create a system that makes future data easier to manage. Reviewing data entry methods and creating manuals for data collection and entry to ensure consistency regardless of who enters the data is also recommended.
3. Reorganize the Data
Data that has been unified through cleansing can be utilized as lists for marketing activities and customer support. The task involves extracting and listing the data based on specific rules.
This task is a post-cleansing data process, and the method of organization changes depending on the purpose of data utilization and the type of data. Define rules based on future use cases and reorganize accordingly.
4. Standardize Processes and Perform Regular Data Cleansing
Data cleansing is not a one-time task. By performing regular cleansing when data increases or when launching new businesses, you can maintain high-precision data.
On the other hand, performing cleansing using different methods each time can negatively affect the data. To avoid this, it is important to standardize the process. Specifically, it is advisable to determine the timing of implementation and the person in charge, and document this in a manual. By standardizing the process and sharing it across the company, you can perform data cleansing efficiently.
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