Yang W. Lee

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.

AIMQ: a methodology for information quality assessment

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.

Data quality in context

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.

Overview and Framework for Data and Information Quality Research

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.

10 potholes in the road to information quality

Poor information quality can create chaos. Unless its root cause is diagnosed, efforts to address it are akin to patching potholes. The article describes ten key causes, warning signs, and typical patches. With this knowledge, organisations can identify and address these problems before they have financial and legal consequences.

Knowing-Why About Data Processes and Data Quality

Knowledge about work processes is a prerequisite for performing work. We investigate whether a certain mode of knowledge, knowing-why, affects work performance and whether the knowledge held by different work roles matters for work performance. We operationalize these questions in the specific domain of data production processes and data quality. We analyze responses from three roles within data production processes, data collectors, data custodians, and data consumers, to investigate the effects of different knowledge modes held by different work roles on data quality. We find that work roles and the mode of knowledge do matter. Specifically, data collectors with why-knowledge about the data production process contribute to producing better quality data. Overall, knowledge of data collectors is more critical than that of data custodians.

Process-Embedded Data Integrity

Despite the established theory and the history of the practical use of integrity rules, data quality problems, which should be solvable using data integrity rules, persist in organizations. One effective mechanism to solve this problem is to embed data integrity in a continuous data quality improvement process. The result is an iterative data quality improvement process as data integrity rules are defined, violations of these rules are measured and analyzed, and then the rules are redefined to reflect the dynamic and global context of business process changes. Using action research, we study a global manufacturing company that applied these ideas for improving data quality as it built a global data warehouse. This research merges data integrity theory with management theories about quality improvement using a data quality lens, and it demonstrates the usefulness of the combined theory for data quality improvement.

Developing data production maps: meeting patient discharge data submission requirements

Recent research in information quality management concluded that firms must manage information as a product, and that the entire information manufacturing system must be managed in order to enable firms to assure delivery of information products with high quality. In this paper, we report a longitudinal case study in a major hospital on how data production maps are developed and used to improve the quality of information products. Specifically, we focus on data production maps for patient discharge data. These data production maps have enabled this hospital to model, analyse, and improve the quality of patient-level data that must be submitted to the State Department of Health Services annually. Implications and lessons learned are also discussed.

Editorial for the Inaugural Issue of the ACM Journal of Data and Information Quality (JDIQ)

A growing component of organizational operations today involves the collection, storage, and dissemination of unprecedented vast volumes of data. However, this expansion comes not without growing pains. Organizations are often unable to translate this data into meaningful insights that can be used to improve business processes and change the way we work. The reasons for this difficulty can often be traced to issues of data and information quality, involving both problematic symptoms and their underlying causes. Previously collected data can turn out to be inconsistent, inaccurate, incomplete, or outof-date. Organizations can have inappropriate or conflicting strategies across the “pockets” of an enterprise that interfere with the ability to get the right information to the right stakeholders in the right format at the right place and time. To make matters worse, the boundary of stakeholders is broadening and increasingly involves extended enterprises often reaching a global interenterprise scale. The time horizon for the use of information also becomes an open and moving target. In recent years, several terms have emerged to refer to these issues, such as Information Quality and Data Quality. We have chosen to name this journal Data and Information Quality to cover the full range of issues and will generally use these terms interchangeably. Complicating matters is the fact that today’s organizations need to do more with their data if they are to compete effectively. Data quality as measured by its fitness for use in a particular application is a major consideration and possibly a thorny issue when discussing issues such as data privacy and protection, data lineage and provenance, enterprise architecture, data mining, data cleaning, as well as data integration processes such as entity resolution and master data-management. Particularly in the area of data integration processes, organizations must grapple with how to deal with incomplete customer data, inaccurate or conflicting data, and fuzzy data as they strive to develop measures of confidence for the information produced in this environment. Even more daunting is the reality that even if organizations get the creation and management of information right for current stakeholders, there is always the prospect of unexpected future stakeholders to consider. How does one ensure that over the long term information will remain accessible, trustworthy, and meaningful in the face of rapidly changing computing and storage technologies and corresponding demands for use? What types of models, methods,

Editors’ Comments: ACM Journal of Data and Information Quality (JDIQ) is alive and well!

We have looked to the data quality, computer science, and MIS communities to recruit associate editors and reviewers to build the core of the review force. Additionally, we have reached out to a list of scholars who have either direct experience as EICs or experience as senior advisors. Finally, we have an inhouse administrator, Jiun Hsu, for coordination and communication with the ACM headquarters and the reviewers. We have 10 senior advisors, 24 associate editors, and over 200 reviewers so far. This year, we plan to include more associate editors, while we expect that the number of reviewers will naturally increase as more manuscripts are received.