Data Quality for Accounting Information System

INTRODUCTION

This report will introduce the Data Quality in Accounting Information System starting in data quality dimensions and the uses of data in database, e business and management. Also it will clarify what is Accounting Information System Data Quality and the possible factors that impact on data quality in accounting information systems .

Accounting information system is a very useful and meaningful which is capable of being used in decision making process by the various users of accounting information. One of the main objectives of financial accounting is to determine whether the business operations have been profitable or not. So Accounting enables us to find out whether a business has earned profits or suffered losses during the accounting period. Accounting data also consists of financial transactions and events relating to an entity supported by documentary evidence (vouchers). This information is presented mostly in the form of financial statements like Income statement (Trading and Profit & Loss account) Position statement (Balance sheet).

Today’s organizations are operating and competing in an information age. ssocieties. Indeed, an organization’s basis for competition has changed from tangible products to intangible information. More and more organizations believe that quality information is critical to their success (Wang et al., 1998). However, not many of them have turned this belief into effective action. Poor quality information can have significant social and business impacts (Strong, Lee & Wang, 1997). There is strong evidence that data quality problems are becoming increasingly prevalent in practice. It is likely that some data stakeholders are not satisfied with the quality of the information delivered in their organizations. In brief, information quality issues have become important for organizations that want to perform well, obtain competitive advantage, or even just survive in the 21st century. In particular, Accounting Information Systems (AIS) maintain and produce the data used by organizations to plan, evaluate, and diagnose the dynamics of operations and financial circumstances (Anthony, Reese & Herrenstein, 1994). Providing and assuring quality data is an objective of accounting. With the advent of AIS, the traditional focus on the input and recording of data needs to be offset with recognition that the systems themselves may affect the quality of data (Fedorowicz & Lee, 1998). Indeed, empirical evidence suggests that data quality is problematic in AIS (Johnson, Leith, & Neter, 1981). AIS data quality is concerned with detecting the presence or absence of target error classes in accounts. Thus, knowledge of the critical factors that influence data quality in AIS will assist organizations to improve their accounting information systems’ data quality. While many AIS studies have looked at internal control and audit, Data Quality (DQ) studies have focused on the measurement of DQ outcomes. It appears that there have been very few attempts to identify the Critical Success Factors (CSFs) for data quality in AIS.

Information technology has changed the way in which traditional accounting systems work. There is more and more electronically captured information that needs to be , stored, and distributed through IT-based accounting systems. Advanced IT has dramatically increased the ability and capability of processing accounting information. At the same time, however, it has also introduced some issues the traditional accounting systems have not experienced. One critical issue is the data quality in AIS. IT advantages can sometimes create problems rather than benefiting an organization, if data quality issues have not been properly addressed. Information overload is a good example. Do we really need the quantity of information generated by the systems to make the right decision? Another example is e-commerce. Should the quality of data captured online always be trusted? Data quality has become crucial for the success of AIS in today’s IT age. The need arises for quality management of data, as data processing has shifted from the role of operations support to a major operation in itself (Wang, Kon & Madnick, 1993b). Therefore, knowledge of those factors impact on data quality in accounting information systems is desirable, because those factors can increase the operating efficiency of AIS and contribute to the effectiveness of management decision-making.

Data quality dimensions are:

Accuracy, which occurs when the recorded value is in conformity with the actual value.

Timeliness, which occurs when the recorded value is not out of date.

Completeness, which occurs when all values for a certain variable are recorded.

Consistency, which occurs when the representation of the data values, is the same in all cases. (Ballou et al. 1982, 1985,1987,1993)

Product quality and service quality

Information should be treated as both a product and a service Product quality includes product features that involve the tangible measures of information quality, such as accuracy, completeness, and freedom from errors. Service quality includes dimensions related to the service delivery process, and intangible measures such as ease of manipulation, security, and added value of the information to consumers (Kahn, Strong & Wang, 2002).

Data quality in database systems

In a conventional database management system (DBMS), the quality of data has been treated implicitly through functions such as recovery, concurrency, integrity, and security control However, from the data consumer’s perspective, those functions are not sufficient to ensure the quality of data in the database (Laudon, 1986; Liepins & Uppuluri, 1990; Redman, 1992; Wang, Kon & Madnick, 1993b). For example, although there are some essential built-in functions for ensuring data quality in a database like integrity constraints and validity checks, they are often not sufficient to win consumers’ confidence on data (Maxwell, 1989). In fact, data is used by a range of different organizational functions with different perceptions of what constitutes quality data, and therefore it is difficult to meet all data consumers’ quality requirements. Thus, data quality needs to be calibrated in a manner that enables consumers to use their own yardsticks to measure the quality . In database design, although the primary focus is not on data quality itself, there are many tools that have been developed for the purpose of data quality management. For example, it is recommended to build integrity constraints and use normalization theory to prevent data incompleteness and inconsistencies, as well as through transaction management to prevent data corruption (Codd, 1970; Codd, 1986;Elmasri, 1989; Stonebraker & Kemnitz, 1991). However, those tools are only related to system design and control. Although they can help for making sure of the quality of data in the system, by themselves they are not sufficient to solve the issue of imperfect data in the real world. Data quality is affected by other factors rather than only by the system, such as whether it reflects real world conditions, and can be easily used and understood by the data user. If the data is not interpretable and accessible by the user, even accurate data is of little value . Therefore, a methodology for designing and representing corporate data models is needed. The use of scenarios, subject areas and design rationale was found to be effective in enhancing understanding of corporate data models (Shanks & Darke, 1999).

Total data quality management (TDQM)

To achieve a state of high data quality, an organization needs to implement Total Data Quality Management (TDQM). Different industries with different goals and environments can develop more specific and customized programs for data quality management to suit their own needs. However, some researchers argue that regardless of differences organizations must follow certain steps in order to enable the successful implementation of a viable TDQM:

1) Clearly define what the organization means by quality in general and data quality in particular.

2) Develop a set of measures for the important dimensions of data quality for the organization that can be linked to the organization’s general goals and objectives.

Data quality in e-Business

Data quality in the context of e Business has some different features from the issues in the traditional environment, because of the increasing use of Internet and online transactions in the e Business environment. The e Business organization has more interactions with the environment, which adds complexities to data quality. Therefore, it is imperative for e Business organizations to establish data quality strategies and implementation methodologies that suit their e Business transformation approaches (Segev & Wang, 2001). While basic principles of traditional information systems methodologies still apply, the scope and context have changed significantly in the e Business environment.

AIS Data Quality

AIS data quality is concerned with detecting the presence or absence of target error classes in the accounts. Two broadly different approaches to assessing AIS data quality have been developed. In the first approach, referred to as direct testing, an auditor treats the information system as a black box .Using sampling techniques, a random sample of the inputs to and outputs of the system is developed and tested against independent external sources. Inferences drawn from this sample are used to draw conclusions about the presence or absence of the target error classes in the accounts. In contrast, in the second approach, referred to as the indirect testing approach, the auditor treats the system as a white box and develops a model of the information system in terms of a language. In this approach, the data quality of the information system is inferred based on testing that determines whether the controls for eliminating target error classes are operating as originally designed.

Possible factors that impact on data quality in accounting information systems

Although the critical success factors for high data quality in AIS have not been addressed, there have been many studies of critical success factors in quality management such as Total Quality Management (TQM) and Just-In-Time (JIT) (Saraph et al 1989; Porter & Parker )

According to the relationships of those factors, they were organized which contains five constructs at three levels. The first level is the external environment that consists of external factors, the second level is the organizational environment that consists of organizational factors, and the third level is the accounting information systems, which has AIS characteristics and DQ characteristics. Stakeholders of AIS could come from within the AIS, outside the AIS but within the organization, and outside the organization. For example, AIS could have both internal and external information suppliers and customers. Although there is only one factor, nature of the AIS, under the category of AIS characteristics, this factor has many attributes, such as the number of the systems / packages, the number of staff, what kind of the system it is, the age and maturity of the system, and the organizational structure of the system. There are seven factors listed under the category of DQ characteristics, those factors are all related directly to the data quality itself. They are: appropriate DQ policies and standard and its implementation, DQ approaches (control & improvement), Role of DQ, Internal control, Input control, Understanding of the systems and DQ, and Continuous improvement of DQ. As previously mentioned the stakeholders of AIS could come from both inside and outside the AIS and the organization. Human related factors have always been the focus within social science and IT research. They include, top management’s commitment to DQ, role of DQ manager/manager group, customer focus, employee/personnel relations, information supplier quality management, and audits and reviews. In the organizational level, there are seven factors, training, organizational structure, organizational culture, performance evaluation & rewards, management of change, evaluation of cost/benefit tradeoffs, and teamwork (communication). External factors have been identified as factors outside the organization from the external environment, and the organization has little or no control over them.

Stakeholder groups for DQ in AIS

In order to understand the stakeholders groups’ impact on accounting information quality, it is essential to identify their relationships with accounting information systems. For understanding stakeholders in accounting information systems proposed the stakeholder concepts from data quality, data warehouse, accounting information systems and quality management areas. In data quality and data warehouse fields, there are four stakeholder groups that have been identified who are responsible for creating, maintaining, using, and managing data. They are data producers, data custodians, data consumers, and data managers (Strong et al, 1997; Wang, 1998; Sharks and Darke 1998). In the accounting information systems area, auditors were recognized as fulfilling the role of monitoring how the accounting information systems work and the quality of the information which has been generated by the systems. Internal auditors especially perform the internal policing and quality adviser role within the organization. Data suppliers also play a role in data quality management. so, in summary and combination of the above mentioned areas, the stakeholders in accounting information systems have been identified as follows:

• Information producers: create or collect information for the AIS

• Information custodians: design, develop and operate the AIS

• Information users: use the accounting information in their works

• Information managers: are responsible for managing the information quality in the AIS

• Internal auditors: monitor the AIS and its data quality, check internal controls in the AIS

• Data suppliers: provide the unorganized raw data to the AIS

Accounting information systems, different stakeholders have different functional roles in relation to the quality of the information. On three different levels. The lower level has only one stakeholder group – the data suppliers who provide unorganized raw data to the AIS. It represents the input stage, which is getting raw data into the AIS. In the middle level, there are four stakeholder groups, namely, information producers, information custodians, information managers, and internal auditors, who are responsible for creating and collecting the information, designing, developing and operating the AIS, managing information, and monitoring AIS and information respectively. This important level contains the processing, storing, maintaining, and monitoring stages. The final and highest level distributes the organized, useful information to the information users, and it is the output stage.

CONCLUSION

In brief, this report has provided an understanding of the importance of critical success factors for data quality in accounting information systems. That is, data quality management is crucial for the successful implementation of accounting information systems. The critical factors identified can serve practitioners in accounting and IT fields as well as management as a useful guide to data quality management activities, and improvement efforts. High-level data quality management practice is one of the keys to success for many organizations. Plan of the critical success factors of DQ management in AIS can permit managers to obtain a better understanding of accounting information system data quality management practices. If organizations focus on those critical success factors, they may be able to evaluate the perception of data quality management in their organizations’ AIS, and ensure the quality of the accounting information. In addition, they will be able to identify those areas of AIS data quality management where improvements should be made, and improve overall data quality in the future.