How to eliminate duplicate data entry? Causes, solutions, and key points for system selection.

What is double input (transcription)?

By correctly defining duplicate data entry (transcription) and understanding specific examples of situations where it is likely to occur, it becomes easier to identify areas for improvement.
Duplicate data entry (transcription) refers to the process of repeatedly entering the same information manually into multiple locations, such as paper, Excel spreadsheets, or business systems. The issue isn't the "number" of entries, but rather the fact that it occurs solely to transfer the same information to different containers.

This problem is most likely to occur in tasks where the "place of input" and the "place of use" are far apart, such as between the field and the office, between departments, or between internal and external sources. Typical examples include recording information on a paper inspection sheet at the field and then re-entering it into Excel at the office, re-entering an estimate created in Excel into sales management, or entering expense reimbursement details into both the application system and the accounting system.
While duplicate data entry is often overlooked due to familiarity, the more instances it occurs, the more the accuracy of the data is compromised. This can easily lead to situations where it's unclear which version is the latest, or where corrections are only reflected in one version, weakening the foundation for data utilization beyond mere inconvenience.

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Three problems caused by dual input

Duplicate data entry is not just a "hassle"; it can lead to increased costs, decreased quality, and delays in information sharing, ultimately lowering overall business performance.

The first consequence is an increase in time costs. Because the same information is handled twice, the workload simply increases, and during busy periods, transcription becomes a trigger for overtime. Furthermore, if verification work is required before and after transcription, invisible incidental work accumulates, gradually reducing productivity on the ground.

The next most serious issues are input errors and quality degradation. Misreading handwritten entries, incorrect digit counts, missing entries, and incorrect item selections are all potential entry points for errors in duplicate data entry. Moreover, when an error occurs, it is difficult to track at what stage the mistake happened, leading to time-consuming troubleshooting and rework.

Furthermore, information sharing is delayed, slowing down the decision-making process. Data entry is often done "later in batches," and the time lag in input directly translates into delays in management and decision-making. As a result, figures such as inventory, costs, and sales forecasts are not available in real time, preparing meeting materials becomes a chore, and both frontline staff and management become exhausted.

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Why does double input occur?

Much of the problem lies not in a lack of individual effort, but in the "system," such as the fragmentation of information storage locations and the lack of coordination between systems.
The main problem is that data is stored in multiple locations. When department-specific optimized Excel spreadsheets, paper copies, and department-specific SaaS applications coexist, the need arises to have the same information in a way that suits each department's needs, leading to the normalization of duplicate data entry.

The next most common issue is a lack of integration between systems. Even if accounting, sales, attendance, and CRM systems operate separately, data can be linked to eliminate the need for manual data entry. However, due to reasons such as the lack of APIs, incompatible data fields, and inadequate error handling during integration, data ultimately has to be transferred manually.

Another cause is the "rigidification of operational rules." If rules remain based on past assumptions, such as supervisors requiring Excel submissions, keeping paper copies for audits, or limited input permissions preventing on-site registration, no matter how many tools are added, duplicate data entry will not disappear. Duplicate data entry is not a matter of on-site habits, but a problem that arises from a combination of rules and system design.

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Four steps to eliminate duplicate input

Eliminating duplicate data entry can be minimized and disruptive to the field by proceeding in stages: understanding the current situation → centralization → automated integration → operational stabilization.
Duplicate data entry is often not resolved simply by introducing a new system. Unless you decide which data will be considered correct, where to collect it, and how to link and handle exceptions, you will end up reverting to using Excel or manual work. Therefore, first, based on the concept of SSOT (Single Source of Truth), you must clearly define which data will be considered correct. After determining the SSOT and centralizing the correct data and input sources, you should then link the data to the necessary systems.

The basic approach is to visualize the source of the problem, start with the areas where the impact will be greatest, consolidate the input source into one, and automatically route the data to the necessary systems. Finally, input quality is ensured through the system, and operations are solidified, including Education and permission design.

Identify the locations where duplicate input is occurring.

The first step is to create an overview of the duplicate data entry. Specifically, write down which tasks involve transferring data, what items are being transferred, and from where to where. Examples include transferring data from paper to Excel, from Excel to mission-critical system, core system, from SaaS to an internal ledger, and from a PDF attached to an email to accounting data entry.

In the identification process, we evaluate not only the frequency of occurrence and the time spent on each task, but also the impact of the error. For example, tasks that "everyone does for 5 minutes every day" have a large total volume, while tasks that involve transferring data that affects monetary amounts, even if only at the end of the month, have a large loss due to errors, so the prioritization criteria change accordingly.
Once you've defined the scope, set KPIs first. Defining metrics such as the time you want to reduce, the number of errors, the lead time for closing processes, and the number of rework requests will allow you to measure the effectiveness after implementation and make it easier to gain acceptance from the team.

Determine the SSOT and centralize the input source.

Next, we clarify "which data should be considered true" as the Single Source of Truth (SSOT). If the SSOT remains ambiguous, even if we add more systems, duplicate management will persist, and the latest version will remain unknown.
Once the SSOT (Solution to Single Data) is decided, the data and input sources are centralized, such as by having a single location where the correct data is entered. First, the policy of eliminating the use of Excel and departmental ledgers is clearly defined, and if there are exceptions, the conditions for those exceptions are also defined.

Tasks that involve on-site data entry can often be effectively replaced with input via smartphones or online forms. Using features such as photo attachment, multiple-choice input, barcode scanning, and retrieving previous values not only digitizes paper data but also reduces the overall input burden.
Centralization goes hand in hand with operational design. If you don't define rules such as who can make modifications, how to record modification history, and what logs are necessary for auditing, you'll end up reverting to the old ways and thinking, "It's actually safer to manage it with Excel." Centralization is not simply about collecting data; it's a design based on SSOT (Solid State Authority) to manage data in a way that includes responsibility and authority.

Automate data integration between systems.

Once the SSOT and centralization policy is decided, we will create integrations to automatically reflect that data in accounting, sales, attendance, CRM, etc. Possible methods include API integration, iPaaS, RPA, and file transfer such as CSV, and it is practical to choose the appropriate method depending on the characteristics of the system and business operations. The key point is not the trend of the method, but whether it "directly leads to a reduction in manual data entry."​

When designing data integration, you need to decide on the level of granularity: real-time or batch. Real-time is effective for inventory and deal status where immediate information is required, but data for closing processes can often be handled by nightly batches. Avoiding excessive real-time processing, tailored to the decision-making points of the business, improves maintainability.
Furthermore, without a design for handling errors, there's a false sense of "integration," ultimately leading to a return to a situation where humans have to check and re-enter data. We need to establish rules for retries, failure notifications, resubmission procedures, and master data synchronization for customers, items, departments, etc., to create an operation that ensures the data flow never stops.

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Automate input validation and establish a consistent operational process.

Finally, we automate the input quality process, moving away from human oversight. By incorporating essential checks, format checks, range checks, master data references, and duplicate detection, we can prevent not only transcription errors but also "incorrect input" from the outset. Establishing a return workflow also clarifies responsibility for corrections.

Transition periods often involve parallel operation, and failures here can lead to a return to paper or Excel. It's crucial to decide in advance how long the parallel period will last, which system will be considered correct, and how to handle exceptions. Minimum Education and manuals should also be prepared. The key to successful adoption isn't perfect documentation, but rather a readily accessible point of contact for inquiries and a continuous improvement cycle.
The design of permissions is directly linked to successful implementation. By designing input, approval, and viewing with the minimum necessary permissions and maintaining audit logs, administrator concerns are reduced, and it becomes easier to eliminate redundant processes such as Excel submissions. We will realistically create a system that can continue to operate, including exceptions such as offline situations that may cause problems for the field staff.

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How to choose a system that suits your solution

When choosing a tool to prevent duplicate data entry, don't select it based on its name. Instead, select it based on which data should be included in SSOT (Single Sign-Off Time) and which data paths should be used for integration and control.
A common mistake in system selection is choosing a "seemingly convenient tool" first, and then considering the input points and data accuracy at the operational level afterward. As a result, it doesn't mesh well with existing systems, leaving users with the need for Excel assistance and manual coordination.

The order of selection should be as follows: first, determine the data and business scope you want to integrate into SSOT; second, identify the necessary integration partners and methods; and finally, apply the control and audit requirements. This will help clarify whether you should solve the problem through integration like ERP, by connecting with iPaaS, or by supplementing with RPA.
Furthermore, we evaluate the system's scalability and operational maintenance, taking into account that business processes may change after implementation. Duplicate data entry requires a design that not only reduces existing tasks but also can accommodate future increases in systems and data.

How to choose between ERP, iPaaS, RPA, AI-OCR, and workflows.

ERP is an option that integrates core business functions and centralizes data entry sources to aim for overall optimization. When sales, accounting, inventory, etc., are integrated, the amount of data entry work between systems is reduced, but because the scope of impact is large, business standardization and a phased implementation plan are necessary.

iPaaS's strength lies in its standardization of API integration between SaaS applications, providing a built-in system for connection and operation. Even with system additions or new fields added, the burden of rebuilding integrations is reduced, contributing to long-term maintainability. On the other hand, if the data models to be integrated and error handling procedures aren't defined from the outset, the complexity increases as the number of integrations grows.

​HULFT Square, an iPaaS provided by Saison Technology, is a cloud-based data integration platform that supports data integration across various systems and SaaS applications. By linking data dispersed across multiple systems to the necessary locations, it reduces the need for manual data transfer via Excel or CSV files, thus eliminating duplicate data entry.

Furthermore, instead of integrating all systems from the start, it's possible to gradually integrate them, beginning with tasks that involve frequent duplicate data entry or those that are heavily reliant on manual processes. It's also necessary to consider whether the integration can be continuously expanded and maintained in anticipation of system additions or business process changes.

RPA can replace screen operations in areas without APIs and is a complementary solution that tends to show results quickly. However, it is vulnerable to screen changes, and maintenance costs increase as the number of exceptions increases. AI-OCR is suitable for improving the entry point of digitizing paper and PDFs, and can change the starting point of tasks that still involve paper. Workflows control applications and approvals, and by systematizing the normalization of input fields and rejection, it becomes easier to eliminate the breeding ground for duplicate entry in approvals, expenses, and purchases.

Points to note during implementation (integration, scalability, permissions, personal information)

We prioritize checking compatibility. This includes not only the presence or absence of an API, but also the integration method (Webhook, batch, file), data item compatibility, retransmission and notification mechanisms in case of errors, and monitoring mechanisms, all to determine if it is "operational." Since manual data entry will resume the moment the integration stops, a design that is less prone to downtime is necessary.

Scalability assesses whether the system can accommodate future departmental expansion, addition of items, and system expansion. While narrowing requirements during initial implementation is effective, it's important to consider factors such as the ease of configuration changes, the ease of adding external integrations, and the licensing structure, assuming that requirements will increase later.

Designing permissions and personal information management is crucial for balancing ease of use for on-site staff with audit and legal security. Input, approval, and viewing are separated by minimum privileges, audit logs are maintained, and job responsibilities are preserved. For personal information, the scope of handling is clearly defined, and procedures for encryption, storage location, outsourcing management, deletion, and masking are all established. Additionally, during migration, master data preparation and inventory of existing Excel files are conducted, and establishing a contact point and rule update system beforehand facilitates smoother adoption.

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summary

Duplicate data entry is the process of transferring the same information to different media or systems, resulting in time costs, errors, and delays in information sharing. It's important to understand this as a structural problem caused by information fragmentation and lack of coordination, rather than something that can be solved through individual effort alone.

The basic approach to resolving this issue is to start by identifying and prioritizing the locations where data needs to be transcribed, and then clearly defining "which data should be considered correct" as an SSOT (Single Source of Truth). Furthermore, the input source should be unified, and data integration with surrounding systems should be automated. By implementing input checks and operational design, it becomes easier to prevent reverting to duplicate data entry.

When choosing a system, instead of looking at ERP, iPaaS, RPA, AI-OCR, and workflows in isolation, the criteria should be based on which data is considered correct and how and where it will be routed. If you want to reduce duplicate data entry while leveraging existing systems, you can also utilize data integration platform such as iPaaS. HULFT Square is one such option that supports data integration between multiple systems and SaaS applications.
Eliminating duplicate data entry is not merely about efficiency; it's about laying the foundation for faster decision-making and increased data reliability.

The person who wrote the article

Affiliation: Marketing Department

Yoko Tsushima

After joining Appresso (now Saison Technology), he worked as a technical sales representative, in charge of technical sales, training, and technical events. After leaving the company to return to his hometown, he rejoined the company in April 2023 under the remote work system. After gaining experience in the product planning department, he is currently in charge of creating digital content in the marketing department.
(Affiliations are as of the time of publication)