Sources of Inaccurate Data in Data Entry Process
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Common Sources of Inaccurate Data in Data Entry Process

Richard Martinez
Richard Martinez
November 9, 2022
Last updated on:

November 9, 2022

|

Read time: 4 mins

There are several reasons why data entered into the system at an organization might be inaccurate. It might have been entered wrongly by mistake or it could have been entered incorrectly, deliberately. It could also be due to an error in the system. Whatever be the reason, the sources of such mistakes must be probed in order to rectify mistakes before they lead to catastrophes.

Listed below are some sources of inaccurate data.

Reasons for Errors in Data Entry Process

1. Human Errors:

In an extensive data entry process where hundreds of words and thousands of numbers are being entered every day, it is impossible to maintain absolute accuracy. Even though quality checks may be in place and the content is always reviewed and re-reviewed, it is still possible to encounter typographic mistakes and incorrect information incorporated due to a lack of concentration. An extra number here and a wrong word there could lead to serious consequences.

2. Flawed Process:

The data entry process itself, in many companies, might be flawed. For instance, paper documents that are given to operators might be filled incorrectly.

  • There may be scanned copies of paper documents lacking clarity, being circulated in the firm. Businesses have seen several instances where information is faxed on poor quality paper in a machine that lacks a good toner and which gives poor output.

  • The most common mistake is that of wrong information being entered in the wrong fields. Sometimes, the information is left open to interpretation by the operator and he or she enters data that is different from what the sender meant. There are even times when the type of English used causes trouble for the operators; the content might be written in US English while the operators are trained in UK English. The documents also arrive in different formats and styles. When the origin of the information itself is not correct, then every other step along the way also becomes error-prone.

3. System Errors:

These are a major cause for concern. Data in securities firms, brokerages, banks, and financial services companies are known to be error-prone because the data did not get transmitted accurately from one system to the other.

  • In a securities firm, front office systems and back-office systems are different. The traders enter the data in the front office systems, and as the information passes through the wires and codes and embeds itself in the back-office tools, some of it gets lost while other pieces of data are only half-filled. Accounting firms find that the entries do not settle in the correct ledger accounts.
  • Sometimes, there are so many codes that the accountants do not know which code to enter for which transaction. This can often lead to confusion when two or more codes refer to closely related transactions. The banks find that their systems incorrectly calculate interest payments on the wrong date or using the wrong percentage. Deposits are not renewed on the correct date because the system malfunctioned and sweep entries in savings accounts are not done on time. That is why Inter Systems reconciliation is a massive and continuously occurring activity in the world of finance.

4. Intentional Errors:

There are cases where incorrect data is entered deliberately in order to harm the business. There may be dishonest workers in every firm and they often look at finding transactions that could be used to their advantage. Providing employers with incorrect data is one of the ways in which corrupt workers try to take advantage of benefits that belong to others. Credit card frauds and loan scams are frequently the results of data manipulation.

Several departments, including finance and marketing, thrive on information and if the information is inaccurate, then their business might not survive in the long run. Regular reconciliation, frequent employee training, timely updating of systems, and outsourcing of activities to data entry specialists, are some ways in which inaccurate data can be minimized.

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