What Is Data Matching?
Data matching is the process of resolving duplicate data, which is a common issue in customer information management. We explain its necessity, benefits, and more in detail below.
Data Matching Is the Consolidation of Identical Customer Information from Multiple Databases
Data matching 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 account management in 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 separate entries due to minor differences, such as "Taro Yamada" versus "TaroYamada" with or without a space. Even for company names, official names, abbreviations, and former names may coexist, preventing the system from recognizing them as the same entity.
Data matching unifies these variations in notation and differences in input rules to accurately consolidate data that should belong to a single record.
Reasons and Benefits of Data Matching
The reasons for and benefits of data cleansing are as follows:
- To Effectively Utilize Data
If customer data is left without being cleansed, duplicates and inconsistent formatting will hinder analysis and marketing efforts, preventing the acquisition of accurate insights.
- Preventing Inefficient Duplicate Approaches
Sending direct mail repeatedly to the same recipient or having multiple representatives call the same contact can lead to customer distrust and complaints. Data cleansing enables a consistent approach.
- Improving Customer Satisfaction and Marketing Precision
By utilizing centralized 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 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 duplicate direct mail, phone calls, or email distributions not only increases printing and communication costs but also gives customers the impression that your management is sloppy or overly persistent.
- Risks of Misdirected Personal Information and Data Leaks
If different individuals with the same name are merged into one record due to inconsistent formatting, there is a risk that information may be sent to the wrong recipient.
- Decreased 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, 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. Every time departments attempt to cross-reference information, reconciliation tasks arise, placing a strain on the staff's man-hours.
In particular, if you feel that your SFA or CRM is not being fully utilized by the team 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 Cleansing Case Studies" mentioned later in this document.
Organizations with multiple locations, such as franchises or group companies
In companies where sales activities are conducted across multiple locations, such as headquarters, branch offices, and franchises, data held by each location tends to become siloed, making it difficult to grasp the transaction status of the entire group. If sales approaches are made without knowing whether another branch is already doing business with that company, it can lead to duplicate approaches, resulting in customer distrust or complaints.
* For actual case studies, please refer to the "Data Cleansing Case Studies" mentioned later in this document.
Companies that have implemented SFA, CRM, or MA tools
Marketing automation and sales support tools only perform at their full potential when backed by accurate customer data.
If data containing duplicates or inconsistent notations is imported, segmentation becomes distorted, and tools may send multiple emails to the same individual, effectively halving the tool's impact. If you have implemented tools but are not seeing the expected results, reviewing your data quality should be your first priority.
Companies that have experienced 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 data.
If left unaddressed after integration, duplicate data will continue to snowball, increasing the cost and man-hours required for future maintenance. Implementing data cleansing at the time of migration is essential for maintaining long-term data quality.
Companies that accumulate data but fail to utilize it for analysis or strategic initiatives
Many companies find themselves in a situation where they have data but cannot utilize it. The primary cause is low data accuracy resulting from duplication, missing information, and inconsistent formatting.
Analysis based on inaccurate data leads to overestimation of customer counts and misconfiguration of target segments, resulting in wasted marketing investment. Data matching is an essential preprocessing step to transform data from a stagnant asset into a powerful, actionable weapon.
A 4-Step Guide to Data Matching
Below, we explain the specific workflow for data matching.
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 clarify the goals for data matching.
Define the sources of duplication and the desired level of consistency for the final database.
2. Extract Data
Next, extract the fields 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, consolidate terms like "Client Name," "Company Name," and "Corporate 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:
- (KK) vs. Kabushiki Kaisha
- Full-width vs. Half-width numbers
- Yamada Taro vs. Yamada Taro (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 process.
4. Match Data
Based on the information unified through data cleansing, determine whether records are identical by combining multiple fields (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 use multiple combined keys to identify 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, or they are registered under abbreviations rather than official names.
Using a dedicated data matching tool is effective for achieving higher precision.
For more detailed data matching procedures, click here:
A 5-Level Guide to Data Matching: How to Eliminate Data Duplication in SFA and CRM Systems? ▶︎
Points to Consider and Countermeasures for Successful Data Matching
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 matching 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 cannot be integrated accurately if inconsistencies and omissions are left unaddressed.
It is important to implement measures to improve the quality of data cleansing, such as creating a formatting unification manual and establishing regular audits and double-check systems.
- Creating an Environment Where Data Matching Is Unnecessary
To reduce the effort required for data matching, it is important to build a system where duplication is less likely to occur in the first place.
- Unify input rules (utilize company ID codes that serve as keys for matching)
- Introduce mechanisms to automate duplicate checks during data registration
- Develop a foundation that facilitates easy integration between departments and systems
Data Cleansing and Consolidation Case Studies
Consolidating 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 managed separately by the Corporate Sales Division, regional headquarters, and individual franchisees. This resulted in approximately 5 million corporate data records becoming siloed across the entire group.
Under these conditions, it was impossible to verify whether a company was already an existing client of the group, leading to inefficient sales activities. Furthermore, 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, the group was able to centrally manage corporate data at the business location level. This enabled the visualization of market share by location, such as identifying that 'only 3 out of 12 locations of a client in a specific region are currently utilizing our services.' This clarified where sales representatives should focus their efforts and improved the accuracy of their proposals.
Click here for details on this case study: Consolidating Approximately 5 Million Siloed Data Records and Visualizing Group-Wide Transaction Share ▶︎
Driving SFA Adoption Through Customer Data Cleansing (Financial Industry)
Mitsubishi UFJ NICOS Co., Ltd., a core company of the Mitsubishi UFJ Financial Group providing corporate payment services, aimed to centralize sales information using Salesforce but struggled with adoption for many years. The primary cause was the duplication and inconsistency of customer data.
Because the company allowed corporate names to be registered in inconsistent formats—such as Kanji, Katakana, or alphabet characters—depending on the individual representative, the same company was frequently registered as multiple separate 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 uSonar enabled data cleansing and unique identification at the corporate entity level. As the project manager reflected, 'Without uSonar, this project would not have been a success,' data cleansing became the decisive factor in driving SFA adoption. For companies struggling to leverage CRM and SFA tools due to data quality issues, data cleansing is an essential step that cannot be overlooked.
For Details on Case Studies: Achieving Salesforce Adoption with uSonar: LBC Powers Customer Data Matching and Unification ▶
