14 Common Data Entry Errors and How to Prevent Them

A small error in the database may not seem severe, but can cause substantial damage.  According to IBM, inaccurate data costs U.S. companies a massive $3.1 trillion every year.

Data entry is an essential part of business where errors are inevitable. Data acts as a core influential character for financial projection, report generation to strategic decision making.

The process looks easy at the surface but consists of some complex layers like raw data collection, data sorting, data processing, and lastly data input into the system.

This article breaks down 14 common data entry errors, their effects and strategies to prevent these errors.

What Are Data Entry Errors and Why They are Important to Prevent?

Data entry errors happen when a human or machine inputs wrong information into a system. They can occur during manual input by a bookkeeper or automation bots like robotic process automation (RPA), optical character recognition (OCR), etc.

As businesses heavily depend on data for decision making, small data entry mistakes can affect the overall operation.

These errors happen mainly due to two reasons: human and machine factors. Human factors include typographical errors, duplicate entries or simply misreading the information. On the other hand, Machine factors are software issues, problems in automation tools’ settings, and the software’s limitations to process.

Data entry errors are significant because they hamper business operations. Incorrect data creates false reports, customer issues and bad decision making.  These errors damage customers’ trust, brand’s image and reliability. Hiring an expert data entry service provider is a better option because they can reduce your database inaccuracies significantly.

14 Common Data Entry Errors and Their Effect on Your Business

Even with the utmost care, the possibility of data entry errors still remains. When information is migrated or merged among systems, the chance of error typically increases.

1. Typographical Errors

Typographical errors refer to the typing mistakes that happen in the data entry process. These errors happen mainly due to fatigue and lack of focus. Typographical errors are the root cause of bad data quality.

These mistakes are the most common errors in data entry but can create complications. For example, you have a customer named Dave. But the data inputter mistakenly input the name Cave in your database. This will be considered as a typographical error.

The operation department who are responsible for data entry works under constant pressure. They need to manage the operation along with entry related works which becomes painful sometimes. Outsourcing

Common typos or  typographical errors:

• Spelling Mistakes: Instead of ‘New’, the data inputter enters ‘Few’ in the system.
• Number Swapped: Upon entering, the numbers got swapped. For example, entering 9991 instead of 1999.
• Adding Extra Figures or Numbers: The inputter entered 19990, where it should be just 1999.

Common reasons behind typographical errors

• Poor lighting
• Intense work pressure, like overload and deadlines
• Keyboard issue
• Uncomfortable working environment

Effects of typographical error

• Disrupts automated workflow
• Can lead to bad customer relationship management, damaging the business’s brand image
• In health care, it can damage patients’ treatment process
• Can provide an incorrect calculation, which leads to insufficient production
• Miscalculated reports and tax returns can lead to penalties

2. Transposition Errors

A transposition error happens when a bookkeeper accidently reverses two adjacent digits while recording the data into the system. Transposition errors may seem silly but can also cause significant damage to businesses. They can lead to wrong financial assessment and faulty tax reports and wrong decision making.

Sometimes business doesn’t operate dedicated data entry teams. Different departments handles this tasks combinedly. But in some cases all the departments face tremendous workload and the additional data entry tasks become a headache for them and they tends to make errors in the database.

That’s why a dedicated data entry team is important for business to tackle simple mistakes like transpositional errors. Even a virtual data entry assistant can increase accuracy to 99.95%.

Transposition errors are a niche of typographical errors. Thus, they occur for the same reasons, like insufficient lighting, intense work pressure, device issues, uncomfortable working environment.

Consequences of Transposition Errors :

• Financial inaccuracies: Transpositional errors lead to financial inaccuracies and false reporting. For example, wrong sales data, inaccurate total expense numbers or total revenue.
• Costly corrections: Time and resources needed to examine the books to identify and resolve these errors are high.

3.  Duplicate Entries

Duplicate entries or data duplication occur when identical information gets recorded more than once in a system. This error can occur due to copy paste error, formatting and wrong document data entry.

Duplicate entry is also a niche of typographical error and occurs for almost similar reasons. Besides that, dirty data and complex source information can also lead to duplicate entries.

Reliable data plays a core foundation of business intelligence, assisting a business with effective analysis and reliable reporting.

Some Reason Behind Duplicate Entries

• Human Error: Manual Data entry and processing are prone to inconsistency, inaccuracy and insufficiency.
• Flawed System: Without consistent procedures and a unified approach to data handling and processing, duplicate records are more likely to proliferate.
• Multiple Data Sources: Absence of synchronization in the system while entering multiple entries.
• Dirty and Complex Source: Sourcing information from complex source is a complicated and tough task to accomplish. Often, processing complex and unsorted data becomes annoying. It reduces concentration and thus errors are generated.

 Effects of Duplicate Entries

• Operational inefficiencies: Inappropriate data reduces operational efficiencies.
• Inaccurate reports: Duplicate data creates inaccurate reports.
• Reduces decision accuracy: As some decisions are highly influenced by the numbers from the books, inaccurate reports lead to wrong decisions.

4. Omitted Data

Omitted data refers to the missing information in a data set. This can be caused by both human and machine factors. Often, data entry personnel simply forget to update the database, which leads to omitting data.

Omitting data can cause inconsistent units too. Often, businesses require merging different databases or migrating information to a different location. During this process of change, the possibility of missing data increases.

In Excel an empty cell can break down the calculation formula. To avoid this 0, N/A are used to identify the values that have been missing. Thus, omitted data can lead to incomplete datasets, affecting the accuracy of analysis.

Resolving Omitted or Missing Data

• Removal: If information is missing in some cells and the outcomes aren’t matching with the end result then an effective way to tackle omitted information is to delete the whole row and fill up the values again.
• Imputation: Replace missing values with estimated ones by using common values like mean, median or a predicted value based on other variables in the dataset.
• Flagging: Add a specific marker in the data set to find out the value missing. This is useful for applying an analysis to understand the extent and impact of the missing data.
• Using formulas: In spreadsheets, you can use formulas like “XLOOKUP” and “VLOOKUP” to identify missing IDs and functions like IF and IFERROR to highlight mismatches and errors.

Missing data handle is necessary for smooth business operation. Handling missing data enables a business with accuracy, reliability and quality.

Accurate data means your business can generate and estimate more effective reports. These reports further help generate better strategies for specific departments.

Furthermore, reliable and valid statistics also give management confidence that the business possesses accurate data and they can make decisions depending on it.

5. Incorrect Formatting

Data formatting refers to organizing, processing and categorizing your data. Incorrect formatting happens when the data inputter fails to process and organize the data and enters a wrong information in a record. For example, entering delivery date in order volume section.

Entering duplicate data, copy paste errors, invalid characteristics can cause incorrect formatting too. It is a significant contributor to insufficient data quality.

Incorrect formatting can occur due to two main reasons: human error and machine factors. Every year, on average, a business needs to update 30-40% of its customers’ information. This upgradation process needs database merging and migration, which is prone to incorrect formatting errors.

Some of the common examples of incorrect formatting:

• Using different data formats in the same dataset. For example using “1/1/2025,” “Jan 1, 2025,” and “2025-01-01”  in the same data set.
• Entering different data formats, like entering “seven” instead of numerical “7” or using an “O” instead of a “0”.
• Variation in the address format like putting street, city, and zip code in different orders or fields across records.
• Entering data for an email address in a phone number field, or vice versa.

Businesses often face incorrect formatting when automation bots are updated. It’s better to outsource data entry tasks for business especially copy paste data entry services. Outsourcing is inexpensive and experts process raw data more accurately.

6. Misclassified Data

Similar to incorrect formatting, misclassified data happens when an inappropriate value  gets into a wrong record.  For example, inputting customer’s order volume 531 into his business postal code number.

This can occur due to human errors like typos, misreading information or systemic issues like ambiguous formatting or outdated data sources. Misclassifying data can occur at any level of the data entry process.

Some common examples of misclassified data:

• Entering a customer’s data to a wrong category.
• Putting a sales report to a different group.
• Entering wrong date format. For Example, entering MM/DD/YY formatted date in a DD/MM/YY format.
• Different typing errors like entering 18 instead of 81.
• Using a backdated or unrecognized code into the database.

Effects of misclassified data

In business, misclassified data can disrupt the relationship among variables, leading to an inaccurate analysis. This can hamper operational efficiency and inaccurate billing.

7. Inconsistent Units

Businesses keep the same records across different databases. Usually a synchronization is applied to automatically update any changes across the database. When an information fails to auto update it creates inconsistent units.

Business tends to keep these same data across different departments or databases due to security purposes, backups and usage efficiency. But without any master record, a systematic or automatic synchronization can lead to inconsistency.

Often, inconsistent units are viewed as data redundancy while there is a significant difference between redundant data and inconsistent units. Businesses store these data copies for optimizing performance. For example, one customer group’s information is kept by both the sales and outreach teams.

An example of an inconsistent unit: The customer has requested to change the contact information to the sales team. The sales team kept this data to themselves and the outreach team is unaware of this updated data. This creates inconsistent units.

Common causes for inconsistent units:

• Absence of a centralized data management system
• Poor data governance and processing
• Dependency on manual processing and lack of automation among internal sources
• Latency issues
• Inconsistent software updates and synchronization
• Formula related issues

Inconsistent units are responsible for filling databases with inaccurate information and outdated data. These are similar to other errors mentioned earlier that cause misleading results.

8. Copy-Paste Errors and Invalid Characters

Copy-paste and invalid characteristics are mostly human errors. When copied information or data is pasted into a wrong location, database or system then these two errors are created.

 Common reasons behind these errors

• Lack of focus by the bookkeeper while data processing and entering data into the system
• Mistaken outdated or wrong data with actual ones
• Common formatting mishaps, for example, using Roman numerals where one needs to use English numerals
• Technical issues
• Misreading data
• Copying a part of the data rather than the whole portion
• Poor offline data entry

Common invalid character and copy paste related errors:

• Inputting Roman numerals instead of English.
• Symbol inconsistency while recording. For example, using $ and € in the same field.
• Inconsistent formatting. For example: “1/1/2025,” “Jan 1, 2025,” and “2025-01-01” are two different formats, but using this same field creates invalid characters.
• Replacing 0 with the English letter ‘O’ or I instead of 1.
• Copying a file from a wrong cell and paste it to required field
• Ignoring formula applied cells or rows

Effects of these errors:

• Causes inaccurate reports
• Leads to financial losses
• Causes an inconsistent unit in the dataset

9. Outdated Information

Outdated information in data entry simply refers to the data that is not relevant anymore. It belongs to an inaccurate dataset. For example, a customer has changed his or her email address, thus the old email address belongs to outdated data.

Outdated data causes inconsistent units in a business operation when automation and data synchronization fail to act timely. On average, 30-40 % of customers’ information needs updates every year. Maintaining this update is a data hygiene process that keeps data from getting complicated.

Businesses often need to utilize data from diverse sources. This use of multiple sources can create outdated information within the business operation.

Similarly, while transferring data from one source to another or from one department to another department can introduce outdated information. When businesses don’t possess a synchronized data management system and proper auditing, the chance of generating outdated information increases.

10. Data Overwrites

Data overwrites is a process where new data replaces already stored or formerly saved data in a database. When data is overwritten into a database, it will most likely delete the old data permanently unless a backup exists.

This human error can have a domino effect of negative outcomes. For example, false reporting, wrong assessment and taxation problems which cause financial damage. Once the new data is set, it becomes complicated to recover, especially if the data is stored in modern storage systems like SSD.

Some examples of how data overwrite happens:

• While sorting scratched and unprocessed data
• Misreading and lack of caution
• Merging cells with functions to an unwanted field not related to a certain category
• Saving a new file with the same name that exists in the storage makes the system delete the old file automatically. This can entirely delete a database from the system

11. Misread Source Documents

Misreading source documents causes different data entry errors like transpositions, formatting and misinterpretation errors. It’s entirely a human error that leads to generating faults in the database.

Unsorted and unprocessed data are complicated to work with. It increases the chances of getting inappropriate results. Poor handwriting, lighting issues, and an uncomfortable environment can cause data entry personnel to lose focus and misread information.

Overload and a short deadline pushes employees to work beyond their capabilities. This excess pressure causes panic and stress in the employee’s mind. This eventually leads the employee to lose focus and misread the source documents.

Another common misread happens when the intensity of similar types of data makes it complicated to sort out which has been in use and which has not.  For example, ID numbers 6462413937, 6462419337 and 64624189387 are pretty similar and manual input of these IDs can cause faulty inputs by the data handler.

12. Incorrect Field Mapping

The field mapping involves the development of a direct link between individual data fields of two or more systems or databases. It is important in data migration, integration and automation because it automates the data flow and avoids human errors. Just like other data entry errors, incorrect field mapping also causes substantial damages in the database.

Field mapping involves a source and target destination and requires a smooth link between these two. Absence of an appropriate link causes the incorrect field mapping.

How Incorrect field mapping occurs :

• Ignoring the target system’s specifications. For example, if the target database has less space than the source, eventually some data will be missed.
• Data migration requires a strong and smooth link between the source and the target destination. A broken link can lead to incorrect field mapping.

The data migration, integration and transformation projects rely on field mapping because this guarantees that every data fragment in a system maintains its meaning and integrity at the destination system. But incorrect field mapping corrupts the database with outdated data.

13. System Interface Errors

System interface errors occur when the system or database becomes unable to process the information the bookkeeper is inputting. This happens due to a bug in the system, false or faulty links between the source and target field, functional errors, software navigation problems to simple poor internet connection.

As automation software or bots are massively doing complicated tasks, a simple prompt issue or programming error can mislead the system to a faulty response.

Common scenarios of system interface errors:

• Auto redundancy elimination is not working, generating inconsistent units
• Auto update of data across different departments isn’t working automatically
• Prohibited data jammed the system’s response

System interface errors can cause significant damage by stalling the whole system or corrupting the database with inaccurate or outdated data.

14. Calculation Entry Errors

Calculation errors commonly occur when the data inputter miscalculates data’s value before entering it in the database. System interface-related issues in data automation bots and poor management of offline data entry sometimes cause this too.

Data assists sales forecast development. Sales forecast is directly linked to the production process. Miscalculated entries disrupt inventory control, production scheduling, supply chain management and product freshness. These errors can be seen through other forms of errors like omitted data, misclassified data or transposition errors.

Common calculation entry errors are:

• Calculation mistake in a certain data field and a wrong entry
• Developing a faulty function for data calculation by the bookkeeper
• Generic mistakes by the automation bots of ICRs, OPRs and RPAs.
• Misreading the source document by the data inputter or the automation software.

Effects of calculation error:

• Wrong financial estimation
• Disruption in CRM
• Insufficient production
• Increase in expense

6 Strategies and Know How to Prevent Data Entry Errors Effectively?

As described earlier, data are the backbone of a business and errors in entering data into a system or database cause substantial damage to the business. Thus, maintaining a seamless data processing system is necessary.

There are two main aspects that lead to these data entry errors: one is human factor, and the other is system or machine factor. Human factors or errors are the errors that solely occur through human mistakes.  On the other hand, machine errors or system errors occur through flaws in the system.

To prevent these data entry errors a wide range of strategies can be implemented throughout the system. But the strategies should cover both human and machine factors. Some of the strategies to prevent data entry errors are:

1. Sound working environment

A suitable working environment for data entry personnel is crucial for getting the best performance from them. Bookkeepers are the most important assets of data entry. They possess the most influential factor for maintaining a neat database.

Excess pressure and workload, discomfort, unfriendly working environment demotivate employees. This causes the employees to lose consciousness.

To cope with this, your company needs to

• Establish a friendly and stress-free working environment
• Provide ergonomics and wrist rest chairs to assist in curbing fatigue of muscles
• Create calm and soothing lighting arrangements
• Provide refreshments and breaks during working hours

2. Appropriate training arrangements

Data entry may seem as an easy task to accomplish on the surface, but there are diverse technical aspects that need careful implementation. Especially data entry software gets constant updates, familiarity with the application and its changes is crucial.

• Your company needs to make sure that all the data entry-related personnel will go through a basic training session, despite their experience
• Provide the new joinee with appropriate onboarding
• Construct performance evaluation and individual support
• Provide a detailed guideline

The goal of training is simply to keep the workforce adequate with the work requirement.

3. Workload management

Workload management directly affects employee motivation. It also plays a crucial part to develop a work environment sound and employee-friendly.

Key aspects of workload management:

• Make sure that your company has sufficient staff to handle the current and future tasks. Employee numbers should be balanced, neither excess nor less.
• Fix a plausible target for the employee and beneficiary for the company.
• Appoint doable deadlines

The right workload balance is when the employee feels a constant urge to accomplish the task they are assigned. But at the same time, they won’t feel over-pressured and doubt that the target might be impossible to achieve.

The target must be set while considering the fact that in data entry accuracy is more important than speed. When targets exceed employees’ capacity, errors are most likey to happen. Thus, adequate and balanced workload management is crucial for data entry-related positions.

4.Filtration and double-checking

Data goes through several processes from its extraction to finally get into the database. This whole process’s aim is to turn the raw data into organized usable data. You can use a standard procedure to ensure the data your team is processing is good data, not outdated and inaccurate data.

• A specific team should be responsible for the entry process; scattered responsibility can cause delay or insufficiency
• Establish a standard validation procedure into the system. This eliminates the leftover inaccuracies by double-checking and reviewing
• Apply access control to the system, specific individuals should work in particular stages of the process. This enhances the quality and keeps the sensitive data secure.
• Keep a copy of the record. If the data accidentally got deleted in the entry process, this record acts as backup.

5. Introduce automation for data entry

Manual data entry is time consuming, labor intense and to some extent possesses risk of inaccuracy. Automation tools for example, optical character recognition and robotic process automation eliminates the possibility of faulty data entry.

OCR technology is created to transform scanned pages and images of printed text into digital forms which can be read and edited by machines. Simply this is a combination of hardware and software capable of recognizing text by scanning printed or visual documents.

OCR’s benefits and capabilities

• Has a high accuracy rate from 99.5% to 99.99% for detecting scanned data.
• Better for structured data like bank documents, invoices, forms, and documents like financial statements.
• Can be integrated with AI tools, for example, google’s cloud AI. This integration makes OCR’s more intelligent and capable.

An advanced form of OCR is ICR or intelligent character recognition. It has the capability of recognizing handwritings from documents that OCR lacks. ICR’s accuracy can exceed up to 97%, making it more efficient than OCR’s basic recognition for handwriting.

Even though ICRs have high accuracy with normal handwriting but it lacks the capability to recognize cursive writing properly. That’s why outsourcing handwritten data entry service is a better option for your organization.

Barcode, QR code scanners also use OCR technology, which is not just fast to detect information, they are accurate and cost less  to operate.

While OCRs are used for just extracting data from visual files, robotic process automation (RPA) does a variety of tasks across databases that an employee would do. In simple terms robotic process automation uses software robots or software bots that are designed to behave as humans in their interaction with computer systems and computer software applications.

Range of tasks RPA is capable of doing:

• RPA software can automatically update data across databases. For example, if a customer has updated the order amount, it can automatically update it across different departments seamlessly.
• Highly capable of extracting data across different platforms, systems, websites and processing it to develop leads. RPA can also works as an automatic validation entity.
• It can intelligently evaluate customer interactions and update leads to different CRM.
• Can identify redundant data and eliminate data duplications.
• Has the capability to generate and gather required data for developing reports.

Eventually, RPAs bring automation into the system by doing several tasks. Common errors like typos, formatting, inconsistency and invalid units can be avoided through RPA applications.

6. Constant monitoring

From workload management, sound environment to automation, all these processes need constant monitoring to get a suitable end result.

Benefits of constant monitoring

• Helps evaluate the data entry process, recover faulty systems and system development for more efficient data entry procedures.
• Identity latency, least performing employees and software issues and updates.
• Identify error types occurring in the system and develop strategies to overcome specific issues

A Combination of these suggested prevention measures is enough to reduce your data entry team’s errors to near zero.

In conclusion, data entry errors are common but cause damage to businesses. Both humans and machines can be responsible for data entry errors.

Correction of these errors can become time-consuming and expensive. Taking proper preventive measures is a better option than waiting to sort out the faults after they occur.