Richard Y. Wang

Data quality assessment

How good is a companys data quality? Answering this question requires usable data quality metrics. Currently, most data quality measures are developed on an ad hoc basis to solve specific problems [6, 8], and fundamental principles necessary for developing usable metrics in practice are lacking. In this article, we describe principles that can help organizations develop usable data quality metrics.

Anchoring data quality dimensions in ontological foundations

of an organization. A leading computer industry information service firm indicated that it “expects most business process reengineering initiatives to fail through lack of attention to data quality.” An industry executive report noted that more than 60% of surveyed firms (500 medium-size corporations with annual sales of more than

AIMQ: a methodology for information quality assessment

20 million) had problems with data quality. The Wall Street Journal also reported that, “Thanks to computers, huge databases brimming with information are at our fingertips, just waiting to be tapped. They can be mined to find sales Anchoring Data Quality Dimensions Ontological Foundations

Data quality in context

Information quality (IQ) is critical in organizations. Yet, despite a decade of active research and practice, the field lacks comprehensive methodologies for its assessment and improvement. Here, we develop such a methodology, which we call AIM quality (AIMQ) to form a basis for IQ assessment and benchmarking. The methodology is illustrated through its application to five major organizations. The methodology encompasses a model of IQ, a questionnaire to measure IQ, and analysis techniques for interpreting the IQ measures. We develop and validate the questionnaire and use it to collect data on the status of organizational IQ. These data are used to assess and benchmark IQ for four quadrants of the model. These analysis techniques are applied to analyze the gap between an organization and best practices. They are also applied to analyze gaps between IS professionals and information consumers. The results of the techniques are useful for determining the best area for IQ improvement activities.

A product perspective on total data quality management

ATA-QUALITY (DQ) PROBLEMS ARE INCREASINGLY EVIdent, particularly in organizational databases. Indeed, 50% to 80% of computerized criminal records in the U.S. were found to be inaccurate, incomplete, or ambiguous. The social and economic impact of poor-quality data costs billions of dollars. [5-7, 10]. Organizational databases, however, reside in the larger context of information systems (IS). Within this larger context, data is collected from multiple data sources and stored in databases. From this stored data, useful information is generated for organizational decision-making. A new study reveals businesses are defining data quality with the consumer in mind.

Information quality benchmarks: product and service performance

The field of information quality (IQ) has experienced significant advances during its relatively brief history. Today, researchers and practitioners alike have moved beyond establishing information quality as an important field to resolving IQ problems—problems ranging from IQ definition, measurement, analysis, and improvement to tools, methods, and processes. However, theoretically-grounded methodologies for Total Data Quality Management (TDQM) are still lacking. Based on cumulated research efforts, this article presents such a methodology for addressing these problems. The purpose of this TDQM methodology is to deliver highquality information products (IP) to information consumers. It aims to facilitate the implementation of an organization’s overall data quality policy formally expressed by top management [10]. Richard Y. Wang

A framework for analysis of data quality research

Information quality (IQ) is an inexact science in terms of assessment and benchmarks. Although various aspects of quality and information have been investigated [1, 4, 6, 7, 9, 12], there is still a critical need for a methodology that assesses how well organizations develop information products and deliver information services to consumers. Benchmarks developed from such a methodology can help compare information quality across organizations, and provide a baseline for assessing IQ improvements.

Overview and Framework for Data and Information Quality Research

Organizational databases are pervaded with data of poor quality. However, there has not been an analysis of the data quality literature that provides an overall understanding of the state-of-art research in this area. Using an analogy between product manufacturing and data manufacturing, this paper develops a framework for analyzing data quality research, and uses it as the basis for organizing the data quality literature. This framework consists of seven elements: management responsibilities, operation and assurance costs, research and development, production, distribution, personnel management, and legal function. The analysis reveals that most research efforts focus on operation and assurance costs, research and development, and production of data products. Unexplored research topics and unresolved issues are identified and directions for future research provided. >

Toward quality data: an attribute-based approach

Awareness of data and information quality issues has grown rapidly in light of the critical role played by the quality of information in our data-intensive, knowledge-based economy. Research in the past two decades has produced a large body of data quality knowledge and has expanded our ability to solve many data and information quality problems. In this article, we present an overview of the evolution and current landscape of data and information quality research. We introduce a framework to characterize the research along two dimensions: topics and methods. Representative papers are cited for purposes of illustrating the issues addressed and the methods used. We also identify and discuss challenges to be addressed in future research.

Data quality requirements analysis and modeling

Abstract A quality perspective in data resource management is critical. Because users have different criteria for determining the quality of data, we propose tagging data at the cell level with quality indicators, which are objective characteristics of the data and its manufacturing process. Based on these indicators, the user may assess the datas quality for the intended application. This paper investigates how such quality indicators may be specified, stored, retrieved, and processed. We propose an attribute-based data model, query algebra, and integrity rules that facilitate cell-level tagging as well as the processing of application data that is augmented with quality indicators. An ER-based data quality requirements analysis methodology is proposed for specification of the kinds of quality indicator to be modeled.