What Is Data Cleansing?
Data cleansing is the process of resolving duplicate data issues that often occur when managing customer information. Below, we explain its necessity, benefits, and more in detail.
What Is Data Cleansing? Consolidating Duplicate Customer Information from Multiple Databases
Data cleansing is the process of identifying overlapping customer information registered across various databases and integrating data related to the same entity into a single record.
While it originated from managing accounts at failed financial institutions, it is now widely used by companies to organize and integrate their customer data.
For example, it is common for the same individual to be registered as duplicate entries due to minor differences, such as the presence or absence of a space between a first and last name. Even with company names, official names, abbreviations, and former names may coexist, preventing the system from recognizing them as the same entity.
Data cleansing standardizes these variations in notation and differences in input rules, effectively merging data that should belong together.
Reasons and Benefits of Data Cleansing
The following points outline the reasons and benefits of performing data cleansing.
- To Utilize Data Effectively
If customer data is left without cleansing, duplicates and variations in notation become obstacles when attempting to use the data for analysis or marketing, preventing the acquisition of accurate insights.
- To Prevent Inefficient Duplicate Approaches
Sending direct mail to the same recipient multiple times or having multiple representatives call the same client can lead to customer distrust or complaints. Data cleansing enables consistent and professional engagement.
- To Improve Customer Satisfaction and Marketing Accuracy
By leveraging unified information, companies can provide appropriate approaches tailored to customer needs, ultimately resulting in improved customer satisfaction and more efficient sales operations.
Risks and Examples of Failure When Data Consolidation Is Not Performed
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- Increased Costs Due to Duplicate Data
Performing duplicate direct mail, phone, or email outreach not only increases printing and communication costs but also gives customers the impression that your company is persistent or poorly managed.
- Risks of Misdirected Personal Information and Data Leaks
If you consolidate different individuals with the same name into a single record due to inconsistencies in notation, you may inadvertently send sensitive information to the wrong recipient.
- Reduced Accuracy in Data Analysis
Creating reports based on customer data that contains duplicates can lead to errors in measuring the effectiveness of initiatives or selecting target audiences, potentially resulting in wasted marketing expenditures.
A 4-Step Guide to Data Consolidation
Below, we explain the specific workflow for data consolidation.
It is broadly divided into four steps: (1) Data Investigation, (2) Data Extraction, (3) Data Cleansing, and (4) Data Matching.
1. Investigate Data
The first step is to understand the current situation.
Identify which departments, systems, and tools contain customer data and define the goals for your data consolidation.
Define the source of the duplicates and the level of consistency you aim to achieve in your final database.
2. Extract Data
Next, extract the items necessary to identify customers from each database.
- Company Name, Contact Name, Address, Phone Number
- Email Address, Department, Job Title, etc.
If item names differ between databases, it is important to unify them under the same attribute. For example, consolidate terms like "Client Name," "Enterprise Name," and "Company Name" into a single attribute.
3. Perform Data Cleansing
Data Cleansing is the process of correcting or deleting inconsistencies and errors to ensure data integrity. Specific examples include:
- Co., Ltd.
- Full-width numbers ⇔ Half-width numbers
- Taro Yamada ⇔ Yamada Taro (presence or absence of spaces)
- Old Company Name ⇔ New Company Name
If you do not establish clear notation rules here, the data will not be integrated correctly in the subsequent matching phase.
4. Match Data
Based on the information unified through data cleansing, determine whether records are identical by combining multiple items (keys) such as company name, phone number, and address.
- If company name, address, and phone number match → Treat as the same company
- Check company names including variations (old names, abbreviations, etc.)
The key is to identify as many duplicates as possible by cross-referencing multiple keys.
A point to note is company name matching. There are many cases where company names have changed due to mergers, office locations have changed due to relocation, the same company is registered with different prefixes or suffixes, or they are registered under abbreviations rather than official names.
Using a dedicated data consolidation tool is effective for achieving higher matching accuracy.
For more detailed data consolidation procedures, click here:
A 5-Level Guide to Data Consolidation! How to Eliminate Duplicate Data in SFA and CRM Systems? ▶︎
Points to Consider and Countermeasures for Successful Data Consolidation
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- Consideration for Personal Information Protection
Since data consolidation involves handling personal information, the risk of misdirected mail or data leaks increases.
Proceed with caution by adhering to standards such as the Act on the Protection of Personal Information and the Privacy Mark (P-Mark) system, for example, by ensuring that different individuals with the same name are not incorrectly merged and by strengthening the security of your data storage environment.
- Thorough Data Cleansing
Matching data while leaving inconsistencies or omissions unaddressed will not result in accurate integration.
It is important to implement measures to improve the quality of data cleansing, such as creating a notation unification manual and establishing regular audits or double-check systems.
- Creating an Environment That Does Not Require Data Consolidation
To reduce the operational burden of data consolidation, it is essential to build a system that prevents duplicates from occurring in the first place.
・Standardize Input Rules (Utilize Company ID Codes as Keys for Consolidation)
・Implement a System That Automates Duplicate Checks During Data Entry
・Develop a Foundation That Facilitates Seamless Integration Between Departments and Systems
