January 29, 2022


Accounting + Finance Blog

Meaning of Data Collection in an Accounting Information System

Meaning of Data Collection in an Accounting Information System

An Accounting Information system (AIS) begins with data collection. In every entity facts must be collected. Then, it is processed and made available to as many as those who need the information. The collection here is different from that collected during a research. What really is it? What do system designers have in mind when programming an AIS? Let’s begin!

Definition of Data Collection

It can be defined as the gathering of valid and complete events or facts that will go through an Accounting Information System. It also means the first operational stage where scrutinise events or transactions collected by an entity’s employees are processed by its AIS.

Key Explanations

First Operational Stage. Data collection is the first stage in data processing. Other states include data processing, database management and output (information).

Read: Five key differences between AIS and MIS

Valid and complete events. The data collected must be valid and complete. Events are valid if it’s exactly what is recognized by the entity. Must events are entered in a source document. For example, sales events must pass through sales documents. Such invoices are valid if they are recognized by the entity. Note that an invoice accepted by Dangote Cement may be totally different from what is valid for Ashaka Cement.

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The events collected must also be complete. If it misses any vital data, it becomes impossible to enter the events in the AIS. For example, If a sale invoice does not include the amount of the goods sold, it may be impossible for the responsible employee to post it in the AIS.

Scrutinise events. The transaction must be scrutinised and free from material error. Where data with error ensues undetected and passes through the processing stage. The output provided may mislead users. In turn, the users will make wrong actions.

Rules Guiding the Design for Data Collection

There are two rules which must be abided by the designers of an Accounting Information System. These are efficiency and relevance.

Data collection is efficient if and only if it collects data once and makes it available to multiple users. This is the problem with Nigeria’s data collection system. Multiple data are collected in Nigeria. This makes the system inefficient and a waste of taxpayer money.

When the same data is collected more than once the problem is more than the huge costs involved. It might lead to poor decision making. And of course, can lead to data redundancy. When this is the case, the data will have to be processed more than once. And will affect storage space making the entity need more space to store its data.

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Data relevance. Whatever events the AIS should capture must be relevant to the users needs. If unnecessary aspects of fields of a transaction are entered during collection, it will result in information overload. And in turn poor decisions and actions on the part of the users.

Read: Meaning of Non Convertible Preference Shares and Key Explanations

Therefore, system designers ensure that the system captured fields of an event that are relevant to the users. To achieve this, the system designers must analyse users’ needs. Furthermore, the AIS should have the capacity to filter irrelevant facts.


In conclusion, data collection involves gathering of facts required for processing through a business accounting information system. For it to be successful, the system designer must consider efficiency and relevance. If a data set contains material errors, it will poorly affect users’ decision making powers.