Let us look at examples of data that actually 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 data, resulting in a database that is difficult to use for analysis.
Notation discrepancies occur due to the following four causes:
1. Inconsistency between "Kabushiki Kaisha" and "(KK)" or errors in the placement of the company type (prefix vs. suffix).
2. Differences in the presence of spaces or character styles.
3. A mixture of formal and abbreviated forms, as well as full-width and half-width characters.
4. The presence or absence of hyphens, parentheses, area codes, or missing fields.
These types of data issues often arise when multiple sales or marketing staff enter data without unified input rules. The role of data cleansing is to correct these inconsistencies and errors to improve data integrity and accuracy.
Benefits of Data Cleansing
What benefits can be gained from performing data cleansing? Here, we explain four representative benefits.
1. Improved Productivity
The first benefit is improved productivity.
By optimizing data through data cleansing, you can expect productivity gains not only within a specific department but across the entire company. Conversely, if 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 business operations. Reducing unnecessary tasks allows employees to focus on core 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. Consequently, this leads to overall improvements in corporate productivity.
2. Improved Data Analysis Accuracy
The second benefit is improved data analysis accuracy.
High-precision data analysis relies heavily on the consistency and accuracy of the underlying data. To conduct marketing using customer data, high-precision analysis is essential. By correcting missing or erroneous information and standardizing data formats through data cleansing, more accurate data analysis becomes possible. Performing this regularly enables highly effective marketing initiatives, such as identifying profitable customers.
Furthermore, data cleansing provides benefits when measuring the effectiveness of marketing results. If you wish to perform high-precision data analysis using your company's data, it is also important to foster a shared understanding within the company that data cleansing is a critical measure.
3. Enhanced Decision-Making Capability
The third benefit is enhanced 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, it may lead to incorrect decisions or strategies. If left unaddressed, you may realize the information was incorrect only after significant time and effort have been lost. Even without errors, data loses its freshness over time, and its quality declines.
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 also effective for cost reduction.
First, by consolidating inconsistent data formats through data cleansing, data extraction becomes easier. As a result, there is no need to use expensive tools for data extraction.
Furthermore, you can avoid wasteful sales activities based on old or incorrect data, saving the costs that would have been incurred. Additionally, by deleting unnecessary data, you can reduce server maintenance costs. Moreover, by reducing redundant tasks and improving work efficiency through data cleansing, 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 from company to company. Here, we briefly explain the general steps.
For more detailed information, please refer to the article below.
Reference Article: [2024 Latest Edition] What Is Data Cleansing? Explaining Methods, Procedures, and Precautions
1. Select and Collect Important Data
The first step in data cleansing is to select the data domain and collect important data from 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.
The key to selecting and collecting important data is to define the scope of the data to be collected in advance. Collecting irrelevant or outdated data that has not been updated 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 moving to the actual cleansing, organize the data and delete unnecessary parts. This process is sometimes called "deduplication" and is 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 erroneous data, full-width/half-width characters, adding missing data, and updating old information. It is also important to create a system that makes it easier to manage data collected in the future. It is advisable to review data entry methods and create a manual for data collection and entry so that consistency is maintained regardless of who enters the data.
3. Reorganize the Data
Data that has been unified through cleansing can be utilized as lists for marketing activities and customer support. The task is to extract and list the data based on specific rules.
This task is a post-cleansing data processing step, and the method of organization changes depending on the purpose of data utilization and the type of data. Define rules with future use in mind and reorganize accordingly.
4. Standardize the Process and Perform Data Cleansing Regularly
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 recommended to determine the timing of implementation and the person in charge, and to create a manual. By standardizing the process and sharing it across the company, you can perform data cleansing efficiently.
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