The Information System: An Accountant’s Perspective
Data Sources
Data sources are financial transactions that enter the information system from both internal and external sources. External financial transactions are the most
common source of data for most organizations. These are economic exchanges with other business entities and individuals outside the firm. Examples include the
sale of goods and services, the purchase of inventory, the receipt of cash, and the disbursement of cash (including payroll). Internal financial transactions involve
the exchange or movement of resources within the organization. Examples include the movement of raw materials into work-in-process (WIP), the application of
labor and overhead to WIP, the transfer of WIP into finished goods inventory, and the depreciation of plant and equipment.
Data Collection
Data collection is the first operational stage in the information system. The objective is to ensure that event data entering the system are valid, complete, and free
from material errors. In many respects, this is the most important stage in the system. Should transaction errors pass through data collection undetected, the
system may process the errors and generate erroneous and unreliable output. This, in turn, could lead to incorrect actions and poor decisions by the users. Two
rules govern the design of data collection procedures: relevance and efficiency. The information system should capture only relevant data. A fundamental task of
the system designer is to determine what is and what is not relevant. He or she does so by analyzing the user’s needs. Only data that ultimately contribute to
information (as defined previously) are relevant. The data collection stage should be designed to filter irrelevant facts from the system. Efficient data collection
procedures are designed to collect data only once. These data can then be made available to multiple users. Capturing the same data more than once leads to data
redundancy and inconsistency. Information systems have limited collection, processing, and data storage capacity. Data redundancy overloads facilities and
reduces the overall efficiency of the system. Inconsistency among redundant data elements can result in inappropriate actions and bad decisions.
Data Processing
Once collected, data usually require processing to produce information. Tasks in the data processing stage range from simple to complex. Examples include
mathematical algorithms (such as linear programming models) used for production scheduling applications, statistical techniques for sales forecasting, and posting
and summarizing procedures used for accounting applications.
Database Management
The organization’s database is its physical repository for financial and nonfinancial data. We use the term database in the generic sense. It can be a filing cabinet or
a computer disk. Regardless of the database’s physical form, we can represent its contents in a logical hierarchy. The levels in the data hierarchy—attribute, record,
and file—are illustrated in Figure 1-6.
Data Attribute. The data attribute is the most elemental piece of potentially useful data
in the database. An attribute is a logical and relevant characteristic of an entity about which the firm captures data. The attributes shown in Figure 1-6 are logical
because they all relate sensibly to a common entity—accounts receivable (AR). Each attribute is also
Part I
Overview of Accounting Information Systems
