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.

Overview and Framework for Data and Information Quality Research

Enterprizes need Data Quality Management (DQM) to respond to strategic and operational challenges demanding high-quality corporate data. Hitherto, companies have mostly assigned accountabilities for DQM to Information Technology (IT) departments. They have thereby neglected the organizational issues critical to successful DQM. With data governance, however, companies may implement corporate-wide accountabilities for DQM that encompass professionals from business and IT departments. This research aims at starting a scientific discussion on data governance by transferring concepts from IT governance and organizational theory to the previously largely ignored field of data governance. The article presents the first results of a community action research project on data governance comprising six international companies from various industries. It outlines a data governance model that consists of three components (data quality roles, decision areas, and responsibilities), which together form a responsibility assignment matrix. The data governance model documents data quality roles and their type of interaction with DQM activities. In addition, the article describes a data governance contingency model and demonstrates the influence of performance strategy, diversification breadth, organization structure, competitive strategy, degree of process harmonization, degree of market regulation, and decision-making style on data governance. Based on these findings, companies can structure their specific data governance model.

One Size Does Not Fit All---A Contingency Approach to Data Governance

Focus groups to investigate new ideas are widely used in many research fields. The use of focus groups in design research poses interesting opportunities and challenges. Traditional focus group methods must be adapted to meet two specific goals of design research. For the refinement of an artifact design, exploratory focus groups (EFGs) study the artifact to propose improvements in the design. The cycle of build and evaluate using EFGs continues until the artifact is released for field test in the application environment. Then, the field test of the design artifact may employ confirmatory focus groups (CFGs) to establish the utility of the artifact in field use. Rigorous investigation of the artifact requires multiple CFGs to be run with opportunities for quantitative and qualitative data collection and analyses across the multiple CFGs. In this paper, we discuss the adaptation of focus groups to design research projects. We demonstrate the use of both EFGs and CFGs in a design research project in the health care field.

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Focus Groups for Artifact Refinement and Evaluation in Design Research

In organizations today, the risk of poor information quality is becoming increasingly high as larger and more complex information resources are being collected and managed. To mitigate this risk, decision makers assess the quality of the information provided by their IS systems in order to make effective decisions based on it. To do so, they may rely on quality metadata: objective quality measurements tagged by data managers onto the information used by decision makers. Decision makers may also gauge information quality on their own, subjectively and contextually assessing the usefulness of the information for solving the specific task at hand. Although information quality has been defined as fitness for use, models of information quality assessment have thus far tended to ignore the impact of contextual quality on information use and decision outcomes. Contextual assessments can be as important as objective quality indicators because they can affect which information gets used for decision making tasks. This research offers a theoretical model for understanding users' contextual information quality assessment processes. The model is grounded in dual-process theories of human cognition, which enable simultaneous evaluation of both objective and contextual information quality attributes. Findings of an exploratory laboratory experiment suggest that the theoretical model provides an avenue for understanding contextual aspects of information quality assessment in concert with objective ones. The model offers guidance for the design of information environments that can improve performance by integrating both objective and subjective aspect of users' quality assessments.

https://fohs.bgu.ac.il/research/upload/23022/Watts,%20Shankar,%20Even,%20DSS%20Paper.pdf

Data quality assessment in context: A cognitive perspective

Data consumers assess quality within specific business contexts or decision tasks. The same data resource may have an acceptable level of quality for some contexts but this quality may be unacceptable for other contexts. However, existing data quality metrics are mostly derived impartially, disconnected from the specific contextual characteristics. This study argues for the need to revise data quality metrics and measurement techniques to incorporate and better reflect contextual assessment. It contributes to that end by developing new metrics for assessing data quality along commonly used dimensions - completeness, validity, accuracy, and currency. The metrics are driven by data utility, a conceptual measure of the business value that is associated with the data within a specific usage context. The suggested data quality measurement framework uses utility as a scaling factor for calculating quality measurements at different levels of data hierarchy. Examples are used to demonstrate the use of utility-driven assessment in real-world data management scenarios and the broader implications for data management are discussed

Utility-driven assessment of data quality

Large data volumes, widely distributed data sources, and multiple stakeholders characterize typical e-business settings. Mobile and wireless technologies have further increased data volumes, further distributed the data sources, while permitting access to data anywhere, anytime. Such environments empower and necessitate decision-makers to act/react quicker to all decision-tasks including mission-critical ones. Decision-support in such environments demands efficient data quality management. This paper presents a framework for managing data quality in such environments using the information product approach. It includes a modeling technique to explicitly represent the manufacture of an information product, quality dimensions and methods to compute data quality of the product at any stage in the manufacture, and a set of capabilities to comprehensively manage data quality and implement total data quality management. The paper also posits the notion of a virtual business environment to support dynamic decision-making and describes the role of the data quality framework in this environment.

Ganesan Shankaranarayan

Papers

Managing Data Quality in Dynamic Decision Environments: An Information Product Approach