The Importance and Challenges of Core System Modernization
A core system refers to an information system that supports the foundation of a company's operations and management.
Specifically, this includes systems that process operations such as sales management, inventory management, accounting, and human resources and payroll.
These systems play a vital role in achieving operational efficiency and automation, while supporting corporate decision-making.
Core systems are involved in every aspect of corporate activity and significantly influence a company's competitiveness.
Therefore, if a core system experiences downtime or failure, it can cause not only operational delays but also significant impacts on the company, such as lost opportunities and loss of credibility.
Background Behind the Need for Core System Modernization: The 2025 Digital Cliff
Many companies are facing the aging of their core systems. Because they continue to use legacy mainframes or client-server systems, they are encountering issues such as increasing maintenance costs and technological obsolescence.
Cases where data integration becomes complex, making company-wide data utilization difficult, are also frequently observed.
In the "DX Report" published by the Ministry of Economy, Trade and Industry in 2018, these complex, aging, and black-boxed core systems were described as the "2025 Digital Cliff," serving as a warning.
It is estimated that if companies continue to rely on legacy systems past 2025, it could result in an economic loss of up to 12 trillion yen.
To promote digital transformation and transform business models, transitioning to a new core system that leverages data is essential.
Companies must urgently address the digitalization and modernization of their core systems.
Negative Impacts of Failing to Modernize Legacy Systems
Failing to modernize legacy core systems leads to the following negative impacts:
- Decline in Operational Efficiency
- Increased Operational and Maintenance Costs Due to Accumulated Technical Debt
Each of these will be explained individually.
Decline in Operational Efficiency
Continuing to use legacy systems over many years leads to increased system complexity, which significantly reduces operational efficiency.
As a result of repeated partial modifications and customizations, the entire system becomes bloated, leading to frequent issues such as reduced processing speeds and data inconsistencies.
Legacy systems often fail to meet current business requirements.
For example, when introducing new products or services, legacy systems may struggle to adapt, often necessitating the construction of separate, additional systems.
If such ad-hoc system construction continues, business processes themselves become inefficient, hindering business growth and transformation.
Furthermore, complex legacy systems require significant time and effort to identify and resolve issues when they occur.
Because the overall system architecture is not transparent, pinpointing the source of problems is difficult, creating a risk of operational downtime in the interim.
Increased Operational and Maintenance Costs Due to Accumulated Technical Debt
Legacy systems tend to see their operational and maintenance costs increase year after year.
As systems age, failures occur more frequently, forcing organizations to rely on reactive, stopgap measures.
The departure of personnel with deep system knowledge often leads to a loss of maintenance expertise, resulting in a state where excessive time and effort are required for routine upkeep.
In addition, maintaining black-boxed legacy systems incurs substantial costs.
Continuous expenses are generated, including the need to secure specialized engineers for operations and maintenance, the procurement of hardware and middleware, and ongoing licensing fees.
The core issue is that rising operational and maintenance costs constrain new IT investments.
While budgets should ideally be allocated toward driving digital transformation and adopting new digital technologies, resources are instead consumed by maintaining legacy systems, hindering progress.
This state, known as technical debt, serves as a major factor that significantly undermines a company's digital competitiveness.
Requirements for the Modernized Core System
What requirements should a modernized core system meet to ensure future sustainability?
Specifically, they are as follows:
- Flexibility to Adapt to Business Changes
- Realizing a Data-Driven Organization
The details are explained below.
Flexibility to Adapt to Business Changes
In the digital age, core systems are required to have greater flexibility than ever before.
This is because it is necessary to respond rapidly to market changes and the diversification of user needs, and to evolve business models accordingly.
Therefore, core systems must be equipped with a design and architecture that can flexibly accommodate new business requirements.
For example, it is important that core systems can be speedily modified and expanded in response to changes in the business environment, such as the release of new products, the introduction of new services, or the development of new customer channels.
Even when changing business direction, such as through global expansion or M&A, it is essential to have a system foundation that can flexibly adapt to global business processes so that the core system does not become a bottleneck.
Realizing a Data-Driven Organization
To promote DX, it is essential to build a framework for data-driven decision-making and business execution.
To achieve this, it is important to have a mechanism in place that can collect, integrate, visualize, and analyze various data generated in each department of the company in real time.
For example, by aggregating all data, such as customer data and market data, and visualizing it on a dashboard, everyone from management to frontline staff can perform rapid data utilization and decision-making.
To fulfill the role of such a data infrastructure, core systems require an architecture suitable for data utilization, including ease of data integration, high scalability, and support for real-time processing.
Beyond simple data visualization and analysis, it becomes possible to generate new business value from data, such as demand forecasting using machine learning.
Successful Core System Modernization Requires a Review of Data Utilization
The modernization of core systems is not merely about replacing old systems with new ones. It is the first step toward transforming your business and achieving data-driven management.
Furthermore, it is essential to review the very approach to data utilization in parallel with system modernization.
In other words, how you organize and utilize the data accumulated in your core system becomes the deciding factor in the success or failure of your core system modernization.
Data Preparation Before Modernization
When modernizing core systems, it is necessary to migrate data accumulated in existing systems to the new system.
However, simply migrating the data as-is will not allow for effective utilization.
This is because, over years of operation, data quality often degrades, frequently leading to issues such as inconsistencies, duplicates, and missing information.
Therefore, thorough data preparation is required before system modernization.
Specifically, it is necessary to improve data quality by performing data standardization and unification, record linkage, deduplication, and the completion of missing values.
Low-quality data can lead to inaccurate analysis and forecasting, which may result in erroneous management decisions.
To achieve the original objectives of system modernization, data preparation can be considered a high-return investment.
The Necessity of Continuous Data Maintenance After System Modernization
Data within core systems is constantly updated throughout daily operations.
Therefore, simply cleaning the data once during a system modernization project is insufficient.
Even after migrating to a new system, it is essential to perform continuous data maintenance to maintain and improve data quality.
To achieve this, it is necessary to conduct regular data checks, cleansing, and master data updates.
It is also important to review operational workflows and integrate mechanisms that enhance data quality.
For example, establishing data entry rules and strengthening input validation features can prevent the entry of inaccurate or inappropriate data.
Data maintenance is not the responsibility of a few select individuals.
From management to frontline employees, the entire organization must recognize the importance of data utilization and commit to data management.
It is essential to establish a data governance framework and embed continuous data maintenance into the corporate culture.
How to Proceed with Core System Modernization Centered on Data
As described above, core system modernization requires a simultaneous review of how data is utilized.
The specific procedure can be divided into the following four steps.
- Step 1. Visualization and Current State Analysis of Existing Data
- Step 2. Data Cleansing and Integration
- Step 3. Requirement Definition for New Systems Based on Data Utilization
- Step 4. Phased and Iterative System Construction and Migration
Each step will be explained individually.
Step 1. Visualization and Current Status Analysis of Existing Data
When preparing data for core system modernization, it is essential to first conduct an inventory of information assets and create a data map.
Clarify the types, formats, volumes, and update frequencies of data accumulated in each system, and evaluate the importance, quality, and relevance of the data.
It is also important to visualize how data is generated, processed, and utilized, and to clarify the data flow.
This allows for the identification of issues such as data dependencies, redundancies, and inconsistencies between systems.
Step 2. Data Cleansing and Integration
Once the overall picture of the data is clear through current status analysis, the next step is to work on data cleansing and integration.
Data accumulated over years of operation may contain "dirty" data that is inaccurate or inconsistent.
Utilizing such data as-is will not yield accurate analytical results.
Therefore, data cleansing is necessary to improve data quality.
We ensure data consistency and integrity by performing tasks such as "filling in missing values," "deleting unnecessary data," and "standardizing inconsistent notations."
It is also necessary to consolidate and manage identical data scattered across multiple systems, such as customer or product data, into a single master data set.
By centralizing master data, data integration between systems becomes smoother, and the efficiency of data utilization will be significantly improved.
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Step 3. Defining Requirements for New Systems Based on Data Utilization
Once data visualization and cleansing have progressed and data quality has improved, the next step is to begin defining requirements for the new system.
It is important at this stage not only to aim for operational efficiency but also to maintain a perspective on how to effectively leverage accumulated data for business purposes.
Align business processes with data flows to clarify which data is generated and used in each operation.
Based on this, define requirements that incorporate features to support data-driven decision-making and integration with data analysis platforms.
Step 4. Phased and Iterative System Development and Migration
Once the requirements for the new system are defined with data utilization in mind, the project enters the system development phase.
However, rather than implementing all functions at once, proceed with a phased modernization.
Instead of migrating all functions simultaneously, prioritize them from the perspective of data utilization and gradually expand the scope of migration.
By doing so, you can steadily advance the system modernization while minimizing the impact on business operations.
Summary
In the digital era, the modernization of core systems must be driven by data utilization.
It is essential to maintain a consistent perspective on data utilization, from data preparation before modernization to data management after implementation.
Steadily executing a series of steps—Data Visualization and Current State Analysis, Cleansing and Integration, Data-Driven Requirement Definition, and Phased/Iterative System Construction and Migration—leads to the success of core system modernization.
As the business environment changes dramatically, there is a demand for rapid results from data utilization.
Let us approach the modernization of core systems with a data-first mindset, unconstrained by legacy systems.
