Let us look at examples of data that require cleansing.
The image below displays 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.
These inconsistencies are caused by the following four factors:
1. Inconsistencies between "Kabushiki Kaisha" and "(KK)" or errors in prefix/suffix placement
2. Presence or absence of spaces and differences in character styles
3. Mixing of formal/abbreviated forms and full-width/half-width characters
4. Variations in hyphens, parentheses, and the presence or absence of area codes or missing fields
These data issues often arise when multiple sales or marketing staff members 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 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 specific departments 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, thereby increasing productivity within the same working hours. Additionally, employees can work more comfortably without the stress of dealing with flawed data, which may also improve 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 heavily on the consistency and accuracy of the underlying data. Marketing initiatives that leverage customer data require highly accurate analysis. By correcting missing or erroneous information and standardizing data formats through cleansing, you enable more precise analysis. Performing this regularly allows for effective marketing strategies, such as identifying high-value customers.
Furthermore, data cleansing provides benefits when quantifying marketing results for performance measurement. If your company aims to conduct high-precision analysis using internal data, it is essential to foster a shared understanding within the organization 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 impacts sound decision-making and the formulation of effective marketing strategies. For example, if referenced data contains errors, missing fields, or outdated information that has not been corrected, it may lead to incorrect decisions or flawed strategies. If left unaddressed, you may only realize the errors after significant time and labor have been lost. 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 precision 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, eliminating the need for expensive tools for data retrieval.
Furthermore, you can avoid wasteful sales activities based on old or incorrect data, saving costs that would have otherwise been incurred. Additionally, deleting unnecessary data reduces server maintenance costs. Moreover, by reducing redundant tasks and improving operational efficiency, you can lower unnecessary labor costs, such as overtime pay, among many other benefits.
How to Proceed with Data Cleansing
The process for data cleansing varies by company and organization. Here, we explain the general steps.
For more detailed information, please refer to the article below.
Reference Article: [2024 Edition] What Is Data Cleansing? Methods, Procedures, and Key Considerations
1. Select and Collect Important Data
The first step in data cleansing is to define the data scope and collect important information. Gather necessary data from various file formats, such as CSV or XML, and consolidate them into a single database. This consolidation may reveal relationships between data points that were previously invisible.
A key point in selecting and collecting important data is to define the scope in advance. Collecting irrelevant or outdated data is counterproductive. In fact, it may create unnecessary work, so defining the scope beforehand allows for a smoother transition to the cleansing phase.
2. Execute Cleansing
Before moving to the actual cleansing, organize the data and remove unnecessary elements. This process, sometimes called "deduplication," is often considered part of data cleansing. Unnecessary elements primarily refer to duplicate data.
Next, perform the critical tasks of data correction and restoration. This includes fixing incorrect data, standardizing full-width/half-width characters, adding missing information, and updating outdated records. It is also important to create a system that makes future data management easier. Reviewing data entry methods and creating a manual for data collection and entry to ensure consistency regardless of who performs the input is highly recommended.
3. Reorganize the Data
Data that has been unified through cleansing can be utilized as lists for marketing activities and customer support. The next step is to extract and list this data based on specific rules.
This task is a post-cleansing process, and the method of organization will vary depending on the purpose of the data and the type of information. Define rules based on future usage and reorganize the data accordingly.
4. Standardize Processes and Perform Regular Data Cleansing
Data cleansing is not a one-time task. By performing regular cleansing as data grows or when launching new business initiatives, you can maintain high-precision data.
However, performing cleansing using different methods each time can negatively impact the data. To avoid this, it is important to standardize the process. Specifically, determine the timing and assign responsibility, and document these in a manual. By standardizing the process and sharing it across the company, you can perform data cleansing efficiently.
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