What Is Data Cleansing?
Data cleansing is the process of resolving duplicate data, which often becomes an issue when managing customer information. We explain its necessity, benefits, and more in detail below.
What Is Data Cleansing? Consolidating Duplicate Customer Information from Multiple Databases
Data cleansing is the process of identifying overlapping customer information across various databases and integrating data regarding the same entity into a single record.
Although 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 duplicates due to minor differences, such as "Taro Yamada" versus "TaroYamada" with or without a space. 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 input rules, ensuring that data that should be unified is properly merged.
Reasons and Benefits of Data Cleansing
The following points outline the reasons for and benefits of data cleansing.
- To Utilize Data Effectively
If customer data is left without cleansing, duplicates and variations in notation become obstacles when attempting to use it for analysis or marketing, preventing the acquisition of accurate insights.
- Preventing Inefficient Duplicate Approaches
Sending direct mail to the same recipient multiple times or having multiple representatives call the same person can lead to customer distrust or complaints. Data cleansing enables consistent and professional engagement.
- Improving Customer Satisfaction and Marketing Precision
By basing activities on unified information, you can provide appropriate approaches tailored to customer needs, ultimately achieving higher customer satisfaction and more efficient sales operations.
Risks and Failure Examples of 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
Performing redundant direct mail, phone calls, or email campaigns not only increases printing and communication costs but also leaves customers with a negative impression of being pestered or poorly managed.
- Risk of Personal Information Misdelivery or Leakage
If data for different individuals with the same name are merged due to inconsistencies in notation, there is a risk that sensitive information may be sent to the wrong recipient.
- Reduced Accuracy in Data Analysis
Creating reports based on customer data that contains duplicates can lead to miscalculations in campaign effectiveness and incorrect target selection, often resulting in wasted marketing expenditures.
Characteristics of Companies That Should Implement Data Consolidation
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 full picture. The need to verify data every time information is referenced across departments places a significant burden on staff hours.
In particular, if you have already 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 data duplication or inconsistencies in notation.
*For actual case studies, please refer to the Data Consolidation 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 engaged in business can lead to duplicate outreach, resulting in customer distrust or complaints.
*For actual case studies, please refer to the Data Consolidation Use Cases section below.
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, different coding systems and formats often coexist, leading to a massive influx of duplicate records.
If left unaddressed after integration, these duplicate records will continue to snowball, increasing the cost and man-hours required for future cleanup. Performing data consolidation (nayose) during the migration phase 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. The primary cause is often low accuracy due to duplicate, missing, or inconsistently formatted data.
Analysis based on inaccurate data leads to overestimation of customer counts and incorrect target segmentation, resulting in wasted marketing investment. To transform your data from a stagnant asset into a powerful, actionable tool, data consolidation is an indispensable preprocessing step.
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 the Data
The first step is to understand your current situation.
Identify which departments, systems, and tools contain customer data and clarify the goals of your consolidation project.
Define the sources of duplication and determine the level of consistency you aim to achieve in your final database.
2. Extract the 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 under the same attributes. For example, "Client Name," "Corporate Name," and "Company Name" should all be integrated 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:
- (KK) vs. Kabushiki Kaisha
- Full-width numbers vs. Half-width numbers
- Taro Yamada vs. Taro Yamada (presence or absence of spaces)
- Old company name vs. 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 the 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.)
Combining multiple keys is the key to identifying duplicates as comprehensively 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., front-loaded vs. back-loaded corporate designations), or they are registered under abbreviations rather than official names.
Using a dedicated data consolidation tool is effective for achieving higher-precision matching.
For more detailed procedures on data consolidation:
A 5-Level Guide to Data Consolidation: How to Eliminate Data Duplication in SFA and CRM Systems? ▶︎
Precautions and Countermeasures for Successful Data Consolidation
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 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, 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 first addressing inconsistencies and omissions will not result in accurate integration.
It is important to implement measures to improve the quality of data cleansing, such as creating a manual for unifying notation, conducting regular audits, and establishing a double-check system.
- Creating an Environment That Eliminates the Need for Data Cleansing
To reduce the operational burden of data cleansing, it is essential 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 data cleansing)
• Implement a System That Automates Duplicate Checks During Data Registration
• Establish a Foundation That Facilitates Seamless Integration Between Departments and Systems
Data Cleansing Case Studies
Centralizing Approximately 5 Million Data Records [Service Industry]
At Duskin Co., Ltd., which operates a nationwide rental service for cleaning and hygiene supplies, corporate data was siloed across the Corporate Sales Division, regional headquarters, and individual franchisees, resulting in approximately 5 million fragmented corporate records across the entire 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 fundamental data, such as corporate affiliation information, which serves as the foundation for sales strategies, making it difficult to develop high-precision account plans.
After implementing uSonar, the group was able to centrally manage corporate data by office location. This enabled the visualization of market share at the branch level, such as identifying that 'out of 12 locations of a client in a specific region, only 3 are currently using our services.' This clarified where sales representatives should focus their efforts, thereby increasing the success rate of their proposals.
For More Details on This Case Study: Centralizing Approximately 5 Million Siloed Data Records to Visualize 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 centralize 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—by different staff members, the same entity was frequently registered as multiple customer records. This made searching and centralized management impossible, creating a vicious cycle where the field team could not effectively utilize Salesforce despite its implementation.
The situation changed dramatically once corporate-level data consolidation and unification were achieved through the implementation of uSonar. As the person in charge reflects, "The project would not have succeeded without uSonar products," making data consolidation the decisive factor in SFA adoption. For companies struggling to leverage CRM and SFA tools due to data quality issues, data consolidation is an unavoidable and essential process.
For More Details on This Case Study: Gaining Traction in Salesforce Adoption with uSonar: LBC Powers Customer Data Consolidation and Centralization ▶︎
