The Importance and Challenges of Core System Modernization
Core systems refer to the information systems that support the foundation of a company's operations and management.
Specifically, these include systems that handle 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 strategic management 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 lead to not only operational disruptions but also significant impacts on the company, such as lost opportunities and loss of credibility.
The Background Behind the Need for Core System Modernization: The 2025 Cliff Problem
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, the complex, aging, and opaque core systems were described as the "2025 Cliff," serving as a warning.
It suggests that if companies enter 2025 while still relying on legacy systems, it could result in an economic loss of up to 12 trillion yen.
To promote digital transformation and transform business models, transitioning to new core systems that facilitate data utilization is essential.
Companies must urgently address the digitalization and modernization of their core systems.
Adverse Effects of Failing to Modernize Legacy Systems
If legacy core systems are not modernized, the following adverse effects will occur:
- Decline in Operational Efficiency
- Increased Operational and Maintenance Costs Due to Accumulated Technical Debt
Each of these points is explained below.
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 slower 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 be unable to support them, forcing companies to build separate, additional systems.
If such ad-hoc system development continues, business processes themselves become inefficient, hindering business growth and transformation.
Furthermore, complex legacy systems require significant time and effort to identify and address the root causes of issues when they occur.
Because the overall system architecture is not transparent, it is difficult to pinpoint where problems lie, creating a risk that business operations will stall in the meantime.
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, they become prone to frequent failures, necessitating reactive, stopgap measures each time.
The departure of personnel who possess deep knowledge of the system can lead to a loss of maintenance expertise, resulting in a state where significant time and effort are consumed by routine upkeep.
In addition, maintaining and managing black-boxed legacy systems incurs substantial costs.
Continuous expenses are generated, including the need to secure engineers for operations and maintenance, the procurement of hardware and middleware, and the payment of licensing fees.
The problem is that rising operational and maintenance costs squeeze budgets for new IT investments.
While resources should ideally be allocated to promoting digital transformation and adopting new digital technologies, they are instead consumed by the maintenance of legacy systems, making it difficult to move forward.
This state, known as 'technical debt,' becomes a major factor that significantly undermines a company's digital competitiveness.
Requirements for the Modernized Core System
So, when considering future sustainability, what requirements are necessary for a modernized core system?
Specifically, they are as follows.
- Flexibility to Adapt to Business Changes
- Realization of a Data-Driven Structure
We will explain in detail below.
Flexibility to Adapt to Business Changes
In the digital age, core systems require 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, and 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.
Realization of a Data-Driven Structure
To promote digital transformation, it is essential to establish 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 across all departments within the company in real time.
For example, by aggregating all data, such as customer and market data, and visualizing it on a dashboard, both management and frontline staff can utilize data and make decisions rapidly.
To fulfill the role of such a data foundation, 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 through demand forecasting using machine learning.
Successful Core System Modernization Requires a Review of Data Utilization
Modernizing 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 effectively the data accumulated in the core system is organized and utilized becomes the deciding factor in the success or failure of the modernization project.
Data Preparation Before Modernization
When modernizing core systems, it is necessary to migrate data accumulated in existing systems to the new platform.
However, simply migrating the data as-is will not lead to effective utilization.
This is because, over years of operation, data quality often degrades, frequently resulting in 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 tasks such as data standardization and unification, entity resolution, deduplication, and filling in missing values.
Low-quality data prevents accurate analysis and forecasting, which may lead to erroneous management decisions.
Data maintenance is a highly cost-effective initiative to ensure the original objectives of a system overhaul are achieved.
The Necessity of Continuous Data Maintenance After a System Overhaul
Core system data is constantly updated through daily operations.
Therefore, cleaning data only once during a system overhaul is insufficient.
Even after migrating to a new system, it is essential to continuously maintain data to preserve and improve its quality.
To achieve this, regular data checks, cleansing, and master data updates are required.
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 functions can prevent the entry of inappropriate data.
Data maintenance is not the responsibility of a few individuals alone.
From management to frontline employees, the entire organization must recognize the importance of data utilization and engage in data management.
It is essential to establish a data governance framework and embed continuous data maintenance into the corporate culture.
How to Proceed with a Core System Overhaul Centered on Data
As described above, a core system overhaul 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 Status 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.
Evaluate the importance, quality, and relevance of data by clarifying the types, formats, volumes, and update frequencies of data accumulated in each system.
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 you have gained a comprehensive overview of your data through current state analysis, the next step is to address data cleansing and integration.
Data accumulated over many 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 essential 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 perform data matching on identical records dispersed across multiple systems—such as customer or product data—and manage them as integrated master data.
By centralizing master data, data synchronization between systems becomes smoother, and the efficiency of data utilization will be significantly improved.
[Understand in 5 Minutes] What Is Data Cleansing? An Easy-to-Understand Guide to Objectives and Practical Examples! ▶︎
Step 3. Defining Requirements for New Systems Based on Data Utilization
As data visualization and cleansing progress and data quality improves, the next step is to begin defining requirements for the new system.
At this stage, it is crucial to maintain a perspective on how to leverage accumulated data for business growth, rather than focusing solely on operational efficiency.
Align your 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 Construction and Migration
Once the requirements for the new system are defined with data utilization in mind, you enter the system construction phase.
However, rather than implementing all functions at once, proceed with a phased modernization approach.
Rather than migrating all functions at once, prioritize them from the perspective of data utilization and gradually expand the scope of the migration.
By doing so, you can steadily advance the system modernization while minimizing the impact on your business operations.
Summary
When modernizing core systems in the digital age, it is essential to proceed with a focus on Data Utilization.
It is important to maintain a consistent perspective on data utilization, from data preparation before modernization to data management after the transition.
Steadily advancing through a series of steps—Data Visualization and Current Status Analysis, Cleansing and Integration, Data-Driven Requirements Definition, and Phased/Iterative System Construction and Migration—will lead to the success of your core system modernization.
As the business environment changes dramatically, there is a demand for achieving results from data utilization quickly.
Without being constrained by legacy systems, let us approach the modernization of core systems with a Data-First mindset.
