Accounting Information Systems—A Definition
Figure 1-1 suggests that accounting information systems (AISs) stand at the crossroads of
two disciplines: ‘‘accounting’’ and ‘‘information systems.’’ Thus, the study of AISs is often
viewed as the study of computerized accounting systems. But because we cannot define FIGURE 1-1 Accounting information systems exists at the intersection of two important disciplines:
(1) accounting and (2) information systems.
an AIS by its size; it is better to define it by what it does. This latter approach leads us to
the following definition that we will use as a model in this book:
Definition: An accounting information system is a collection of data and processing
procedures that creates needed information for its users.
Let us examine in greater detail what this definition really means. For our discussion,
we’ll examine each of the words in the term ‘‘accounting information systems’’ separately.
Accounting. You probably have a pretty good understanding of accounting subjects
because you have already taken one or more courses in the area. Thus, you know that
the accounting field includes financial accounting, managerial accounting, and taxation.
Accounting information systems are used in all these areas—for example, to perform tasks
in such areas as payroll, accounts receivable, accounts payable, inventory, and budgeting.
In addition, AISs help accountants maintain general ledger information, create spreadsheets
for strategic planning, and distribute financial reports. Indeed, it is difficult to think of
an accounting task that is not integrated, in some way, with an accounting information
system.
The challenge for accountants is to determine how best to provide the information
required to support business and government processes. For example, in making a decision
to buy office equipment, an office manager may require information about the sources
of such equipment, the costs of alternate choices, and the purchasing terms for each
choice. Where can the manager obtain this information? That’s the job of the accounting
information system.
AISs don’t just support accounting and finance business processes. They often create
information that is useful to non-accountants—for example, individuals working in
marketing, production, or human relations. Figure 1-2 provides some examples. For this
information to be effective, the individuals working in these subsystems must help the
developers of an AIS identify what information they need for their planning, decision
making, and control functions. These examples illustrate why an AIS course is useful not
only for accounting majors, but also for many non-accounting majors.
Information (versus Data). Although the terms data and information are often
used interchangeably, it is useful to distinguish between them. Data (the plural of datum)
are raw facts about events that have little organization or meaning—for example, a set
of raw scores on a class examination. To be useful or meaningful, most data must be
processed into useful information—for example, by sorting, manipulating, aggregating, or FIGURE 1-2 Examples of useful information that an AIS can generate for selected
non-accounting functions of a business.
classifying them. An example might be by taking the raw scores of a class examination and
computing the class average.
Do raw data have to be processed in order to be meaningful? The answer is ‘‘not at
all.’’ Imagine, for example, that you take a test in a class. Which is more important to
you—the average score for the class as a whole (a processed value) or your score (a raw
data value)? Similarly, suppose you own shares of stock in a particular company. Which of
these values would be least important to you: (1) the average price of a stock that was
traded during a given day (a processed value), (2) the price you paid for the shares of stock
(an unprocessed value), or (3) the last price trade of the day (another unprocessed value)?
Raw data are also important because they mark the starting point of an audit trail—
i.e., the path that data follow as they flow through an AIS. In a payroll system, for example,
an employee’s time card for a given pay period indicates how many hours he worked, and
therefore (when combined with his hourly pay rate), his gross pay. An auditor can verify
the information on a paycheck by following the audit trail backwards—for example, to
make sure that the final value reflects the correct payment for the number of hours worked.
Case-in-Point 1.1 At one American university, an employee in the payroll department was
able to steal thousands of dollars by manipulating the payroll records of student workers.
When students quit their jobs, she would delay inputting their termination dates in her
computer, continue to submit time cards in their behalf, and cash the subsequent payroll
checks generated by the system. She was caught when one student complained that his W-2
tax form showed he had earned more money than he had in fact been paid. Auditors then
examined his payroll records and were able to uncover the fraud.1
Despite the potential usefulness of some unprocessed data, most end users need
financial totals, summary statistics, or exception values—i.e., processed data—for
decision-making purposes. Figure 1-3 illustrates a model for this—a three stage process
in which (1) raw and/or stored data serve as the primary inputs, (2) processing tasks
process the data, and (3) meaningful information is the primary output. Modern AISs, of FIGURE 1-3 An information system’s components. Data or information is input, processed, and
output as information for planning, decision-making, and control purposes. course, harness information technology to perform the necessary tasks in each step of
the process. For example, a catalog retailer might use some web pages on the Internet to
gather customer purchase data, then use central file servers and disk storage to process
and store the purchase transactions, and finally employ other web pages and printed
outputs to confirm and distribute information about the order to appropriate parties.
Although computers are wonderfully efficient and useful tools, they also create problems.
One is their ability to output vast amounts of information quickly. Too much
information, and especially too much trivial information, can overwhelm its users, possibly
causing relevant information to be lost or overlooked. This situation is known as
information overload. It is up to the accounting profession to determine the nature and
timing of the outputs created and distributed by an AIS to its end users.
Another problem with computerized data processing is that computers do not automatically
catch the simple input errors that humans make. For example, if you were performing
payroll processing, you would probably know that a value of ‘‘-40’’ hours for the number
of hours worked was probably a mistake—the value should be ‘‘40.’’ A computer can be
programmed to look for (and reject) bad input, but it is difficult to anticipate all possible
problems.
Yet a third problem created by computers is that they make audit trails more difficult
to follow. This is because the path that data follow through computerized systems is
electronic, not recorded on paper. However, a well-designed AIS can still document its
audit trail with listings of transactions and account balances both before and after the
transactions update the accounts. A major focus of this book is on developing effective
internal control systems for companies, of which audit trails are important elements.
Chapters 11, 12, and 14 discuss these topics in detail.
In addition to collecting and distributing large amounts of data and information,
modern AISs must also organize and store data for future uses. In a payroll application,
for example, the system must maintain running totals for the earnings, tax withholdings,
and retirement contributions of each employee in order to prepare end-of-year tax forms.
These data-organization and storage tasks are major challenges, and one of the reasons why
this book contains three chapters on the subject (see Chapters 4, 5, and 6).
Besides deciding what data to store, businesses must also worry about how best to
integrate the stored data for end users. An older approach to this problem was to maintain
independently the data for each of its traditional organization functions—e.g. finance,
marketing, human resources, and production. A problem with this approach is that even
if all the applications are maintained internally by the same IT department, there will be
separate data-gathering and reporting responsibilities within each subsystem, and each
application will store its data independently of the others. This often leads to a duplication
of data-collecting and processing efforts, as well as conflicting data values when specific
information (e.g., a customer’s address) is changed in one application but not another.
Organizations today recognize the need to integrate the data associated with their
functions into large, seamless data warehouses. This integration allows internal managers
and possibly external parties to obtain the information needed for planning, decision
making, and control, whether or not that information is for marketing, accounting, or some
other functional area in the organization. To accomplish this task, many companies are now
using large (and expensive) enterprise resource planning (ERP) software packages to
integrate their information subsystems into one application. An example of such a software
product is SAP R/3, which combines accounting, manufacturing, and human resource
subsystems into an enterprise-wide information system—i.e., a system that focuses on
the business processes of the organization as a whole. (We discuss these systems in Case-in-Point 1.2 Accountants and other managers are using predictive analytics, a
technique that takes adv