Page 41 - Accounting Information Systems

12 PART I Overview of Accounting Information Systems

application of labor and overhead to WIP, the transfer of WIP into finished goods inventory, and the depre-
ciation 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 program-
ming 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 relevant because it contributes to the information content
of the entire set. As proof of this, the absence of any single relevant attribute diminishes or destroys the
information content of the set. The addition of irrelevant or illogical data would not enhance the informa-
tion content of the set.

RECORD. A record is a complete set of attributes for a single occurrence within an entity class. For
example, a particular customer’s name, address, and account balance is one occurrence (or record) within
the AR class. To find a particular record within the database, we must be able to identify it uniquely.
1
Therefore, every record in the database must be unique in at least one attribute. This unique identifier at-
tribute is the primary key. Because no natural attribute (such as customer name) can guarantee unique-
ness, we typically assign artificial keys to records. The key for the AR records in Figure 1-6 is the
customer account number. This is the only unique identifier in this record class. The other attributes pos-
sess values that may also exist in other records. For instance, multiple customers may have the same
name, sales amounts, credit limits, and balances. Using any one of these as a key to find a record in a

1 When we get into more advanced topics, we will see how a combination of nonunique attributes can be used as a unique
identifier.