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 records due to minor differences, such as the presence or absence of a space between names. 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, ensuring that data that should be unified is accurately consolidated.
Reasons and Benefits of Data Cleansing
The following points outline the reasons and benefits of performing data cleansing.
- To Effectively Utilize Data
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 contact can lead to customer distrust or complaints. Data cleansing enables consistent and professional engagement.
- To Improve Customer Satisfaction and Marketing Precision
By leveraging unified information, you can provide appropriate approaches tailored to customer needs, ultimately resulting in improved customer satisfaction and more efficient sales operations.
Risks and Failure Examples Caused by Not Performing Data Cleansing
名寄せを怠ると、コストの増大・個人情報の漏えいリスク・データ分析の精度低下という3つの問題が起こります。それぞれの具体的な失敗例を見ていきましょう。
- Increased Costs Due to Duplicate Data
Sending duplicate direct mail, calls, or emails 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 variations 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 inaccurate measurement of campaign effectiveness and flawed target selection, resulting in wasted marketing expenditures.
Characteristics of Companies That Should Implement Data Cleansing
名寄せは全ての企業に緊急度が高いわけではありません。しかし、以下のような状況に当てはまる場合は、データの問題がすでに営業・マーケティング・経営判断に悪影響を及ぼしている可能性が高く、早急な対応が求められます。
Managing Customer Data Across Multiple Departments and Systems
In companies where sales, marketing, and customer support departments manage customer data in separate systems, information on the same customer becomes fragmented, making it difficult to grasp the overall picture. The need to reconcile data every time information is referenced across departments places a significant burden on staff hours.
In particular, if you have implemented SFA or CRM systems but feel that the team is not utilizing them effectively or that the entered data is unreliable, the root cause is often duplicate data or variations in notation.
*For actual case studies, please refer to the Data Cleansing Use Cases section below.
Organizations with Multiple Locations, Such as Franchises and Group Companies
In companies where sales activities are conducted across multiple locations, such as headquarters, branches, and franchisees, data held by each location tends to become siloed, making it difficult to understand the transaction status across the entire group. Approaching a company without knowing if another branch is already doing business with them can lead to duplicate outreach, resulting in customer distrust or complaints.
* For actual case studies, please refer to the "Data Cleansing Case Studies" mentioned later in this article: Data Cleansing Case Studies.
You Have Implemented Tools Such as SFA, CRM, or MA
Marketing automation and sales support tools can only perform at their full potential when supported by accurate customer data.
If you input data that contains duplicates or inconsistent formatting, your segmentation will be skewed, and you may experience issues such as sending multiple emails to the same individual, which diminishes the effectiveness of your tools. If you have implemented these tools but are not seeing the expected results, reviewing your data quality should be your first priority.
You Have Experienced Corporate Mergers, Acquisitions, or System Migrations
When databases are consolidated due to M&A or system replacements, a large volume of duplicates is generated as different coding systems and formats are merged.
If left unaddressed after integration, duplicate data will continue to snowball, leading to higher costs and more man-hours required for future cleanup. Performing data cleansing at the time of migration is essential for maintaining long-term data quality.
You Are Accumulating Data but Not Utilizing It for Analysis or Strategy
Many companies find themselves in a situation where they have data but are unable to use it effectively. This is often caused by low data accuracy due to duplicates, missing information, or inconsistent formatting.
Analysis based on inaccurate data leads to overestimating the number of customers or misconfiguring target segments, resulting in wasted marketing investment. To transform your data from "accumulated assets" into "actionable weapons," data cleansing is an essential preparatory step.
A 4-Step Guide to Data Cleansing
Below, we explain the specific workflow for data cleansing.
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 your current situation.
Identify which departments, systems, and tools hold customer data and clarify the goals of your data cleansing project.
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 field names differ between databases, it is important to unify them into the same attributes. For example, "Client Name," "Company Name," and "Corporate Name" should all be consolidated 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. (Incorporated)
- Full-width numbers ⇔ Half-width numbers
- Taro Yamada ⇔ Yamada Taro (Handling of spaces)
- Old Company Name ⇔ New Company Name
If you do not establish clear formatting rules here, the data will not be integrated correctly during the subsequent matching phase.
4. Match Data
Based on the information unified during the data cleansing phase, determine if records are identical by combining multiple 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.)
Combining multiple keys is the key to identifying as many duplicates as possible.
A point to note is company name matching. This is because 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 (e.g., Kabushiki Kaisha at the beginning vs. end), or the company is registered under an abbreviation rather than its formal name.
To achieve higher matching accuracy, it is effective to use a dedicated data cleansing tool.
For more detailed data cleansing procedures, click here:
A 5-Level Guide to Data Cleansing: How to Eliminate Data Duplicates in SFA and CRM? ▶︎
Points to Consider and Countermeasures for Successful Data Cleansing
名寄せを成功させるための注意点は、個人情報保護への配慮・データクレンジングの徹底・重複が発生しない環境づくりの3点です。それぞれ詳しく解説します。
- Consideration for Personal Information Protection
Since data cleansing 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, ensuring that individuals with the same name are not incorrectly merged and that security for data storage environments is strengthened.
- Thorough Data Cleansing
Matching data without addressing inconsistencies or omissions will not result in accurate integration.
It is essential to implement measures that enhance data cleansing quality, such as creating style guides for data standardization, conducting regular audits, and establishing double-check systems.
- Creating an Environment That Eliminates the Need for Data Deduplication
To reduce the manual effort required for data deduplication, it is crucial to build a system that prevents duplicates from occurring in the first place.
• Standardize input rules (utilize corporate ID codes that serve as keys for deduplication).
• Implement automated duplicate checks during data registration.
• Develop a foundation that facilitates seamless integration between departments and systems.
Data Deduplication Case Studies
Centralizing Approximately 5 Million Data Records [Service Industry]
At Duskin Co., Ltd., which operates a nationwide rental service for cleaning and hygiene products, corporate data was siloed across the Corporate Sales Division, regional headquarters, and individual franchisees, resulting in approximately 5 million fragmented corporate records across the group.
In this state, it was impossible to even confirm whether the group already had an existing relationship with a target company, leading to inefficient sales activities. Additionally, there was a lack of foundational data, such as corporate affiliation information, which is essential for sales strategy, making it difficult to develop high-precision account plans.
Following the implementation of uSonar, we are now able to centrally manage corporate data for the entire group at the business location level. This has enabled the visualization of market share by location, such as identifying that 'only 3 out of 12 branches of a client in a specific region are utilizing our services.' This clarity has helped sales representatives determine exactly where to focus their efforts, thereby increasing the accuracy of our proposals.
Click here for details on this case study: Centralizing Approximately 5 Million Siloed Data Records: Visualizing Group-Wide Transaction Share ▶︎
Driving SFA Adoption Through Customer Data Consolidation [Financial Industry]
As a core company of the Mitsubishi UFJ Financial Group providing corporate payment services, Mitsubishi UFJ NICOS Co., Ltd. aimed to centrally manage sales information using Salesforce, but struggled with adoption for many years. The primary cause was the duplication and inconsistency in customer data formatting.
Because corporate names were registered in inconsistent formats—such as Kanji, Katakana, or alphabet characters—depending on the individual representative, the same corporation was frequently registered as multiple separate customer records. This made searching and centralized management impossible, leading to a vicious cycle where the field team could not effectively utilize Salesforce despite its implementation.
The situation changed dramatically once uSonar was implemented, enabling data consolidation and unique identification at the corporate level. As the project manager reflected, 'Without uSonar, this project would not have been a success,' data consolidation became the decisive factor in SFA adoption. For companies struggling to leverage CRM and SFA tools due to data quality issues, data consolidation is an essential process that cannot be avoided.
Click here for details on this case study: Achieving Salesforce Adoption with uSonar: LBC Proves Powerful for Customer Data Consolidation and Centralization ▶︎
