There are various ways to address data silos in B2B companies, and their effectiveness depends on the specific situation. For many companies, it is common to start with Data Cleansing to correct immediate data inaccuracies or Data Matching to consolidate duplicate records.
In this article, we will categorize these two representative methods for resolving data silos—Data Cleansing and Data Matching—and then explain Master Data Management (MDM) as a third approach that elevates these processes from one-time tasks into a system for continuous, unified data management.
The Data Cleansing Approach to Organizing Siloed Data
Generally, data cleansing refers to the process of detecting, correcting, and formatting inaccuracies in a database—such as inconsistent notations, missing values, typos, and outdated information—to improve overall data quality.
Starting with cleansing to address data silos is considered effective because it can rapidly improve the quality of individual databases. Since integrating systems while data remains "dirty" only propagates inaccurate information throughout the company, cleansing is considered a foundational task that serves as a prerequisite for all data integration.
Specific examples include standardizing company name prefixes and suffixes (e.g., converting between full and abbreviated forms of "Kabushiki Kaisha"), unifying address and phone number formats, and reflecting changes such as relocations, name changes, or business closures.
On the other hand, cleansing is often performed as a one-time task, meaning data will inevitably degrade again over time. Furthermore, because it is a process of cleaning individual records, it has the limitation of being unable to resolve duplicates that span across multiple databases.
The Data Matching Approach to Integrating Distributed Customer Information
Generally, data matching refers to the process of identifying and consolidating records of the same company or individual that are scattered across multiple databases or within a single database into a single, unified record.
By implementing data matching to address data silos, customer information dispersed across CRM, Marketing Automation (MA), and SFA systems can be linked at the corporate level, enabling a so-called "360-degree view of the customer." This method directly contributes to preventing redundant approaches by sales and marketing teams and ensures an accurate understanding of transaction history.
Specific examples include cross-referencing leads acquired by the marketing department with companies currently in negotiations with the sales department, and linking group companies, including parent companies, subsidiaries, and branch offices. To match the same company with different notations with high precision, it is considered effective to use multiple data points such as company name, address, and phone number, as well as utilizing a unique identifier (integration key) to identify each company.
However, data cleansing is not a one-time task. As new data continuously flows into various systems daily, a single, isolated data cleansing project leaves the lingering challenge of data siloing and duplication recurring over time.
The Third Approach Essential for Unified Data Management: Master Data Management (MDM)
Master Data Management (MDM) refers to the initiative of centrally managing core business data—such as corporate, customer, and product information—across the entire organization and continuously maintaining its quality. It is a systematic concept in the field of data management, known as a framework for establishing a 'Single Source of Truth' (SSOT) within an organization.
The fundamental difference is that while cleansing and matching are 'tasks,' MDM is the 'system and structure' designed to execute those tasks continuously. Its purpose is not merely to clean data through a one-off project, but to create a structure where data remains in a clean state indefinitely.
MDM is considered a concept that provides particular value in the current climate, where DX promotion and data-driven management are essential. Specifically, it is said to follow the processes outlined below.
- Assessment of Current State and Formulation of Integration Policy
Inventory the data sources existing within the company and define which data will be managed as the 'Master.' Simultaneously, establish the ownership of management responsibilities and quality standards.
- Design of Integration Rules and Coding Systems
Design the matching rules for data cleansing and the system for integration keys (corporate codes) that uniquely identify companies. The quality of this design is considered to determine the accuracy of all subsequent integrations.
- Execution of Cleansing and Matching, and Infrastructure Development
Execute cleansing and matching based on the designed rules and build the infrastructure to maintain the integrated master data. Integrate with peripheral systems such as CRM and MA via APIs or ETL.
- Continuous Operation and Governance
Automatically apply integration rules when new data flows in and regularly monitor quality metrics. The core of MDM lies in operations that continuously reflect changes such as corporate relocations, name changes, and organizational restructuring.
In this way, MDM is an approach that repositions cleansing and matching not as conflicting methods, but as components of continuous unified management, serving as a powerful framework that includes the 'prevention of recurrence' of data siloing.
What Does Implementing MDM Using Corporate Data Entail?
It is said that MDM provides practical value not only by organizing internal data but also by combining it with external corporate data. In B2B business, the following applications can be expected.
MDM Use Cases
- Promoting ABM (Account-Based Marketing)
With a centrally managed corporate master, you can comprehensively grasp the history of touchpoints, negotiations, and transactions for each target company. By managing the relationships between group companies and business offices, it becomes possible to formulate account-based strategies and execute organizational approaches.
- Eliminating Duplication and Managing Leads Between Sales and Marketing Departments
By matching leads acquired by marketing with the corporate master, you can prevent duplicate approaches to existing customers or companies currently in negotiations. Establishing a system that automatically performs data consolidation when new leads arrive ensures that the accuracy of interdepartmental collaboration is maintained continuously.
- Credit Management and Compliance
Accurately grasping business partners at the corporate level serves as the foundation for proper management of credit limits and compliance tasks such as anti-social force checks. It also leads to a better understanding of transaction status across the entire group, contributing to improved accuracy in risk management.
- Management Analysis and Visualization of LTV (Life Time Value)
By integrating sales, contract, and support data that were previously siloed by department at the corporate level, it is possible to accurately calculate profitability and LTV for each customer. Management can make resource allocation decisions based on the same reliable data.
With MDM, the results of cleansing and data consolidation are not merely temporary; they transform into assets that continue to support corporate activities. However, the challenge remains that data held internally cannot keep up with changes such as corporate relocations, mergers, and closures. Therefore, using it in conjunction with an external corporate information database that offers freshness and comprehensiveness can be expected to yield higher accuracy.
Conclusion
We summarize the key points of this article in three items: (1) Methods for resolving data silos can be organized into cleansing, data consolidation, and MDM, with MDM serving as the framework to continuously execute the former two; (2) Cleansing and data consolidation lose their effectiveness over time if performed only once; (3) Operating MDM in combination with external corporate data is the key to sustaining unified management.
All the approaches introduced in this article are useful. While data cleansing can improve individual data quality in the short term, it does not resolve duplicates. Data matching integrates dispersed customer information to achieve a 360-degree view, but when performed as a one-off task, data silos will inevitably re-emerge. It is essential to use both as a starting point and elevate them into an MDM framework, thereby transforming centralized data management into a sustainable competitive advantage.
Our customer data integration solution, uSonar, utilizes our proprietary LBC code system as an integration key to achieve high-precision data matching and centralized management of corporate master data. With one of Japan's largest corporate information databases, it allows for the continuous acquisition of up-to-date external information and supports real-time integration via API, enabling the sustainable operation of MDM.
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