Assessment methods for information quality criteria (original) (raw)

A Conceptual Framework and Belief Function Approach to Assessing Overall Information Quality

2001

We develop an information quality model based on a user-centric view adapted from Financial Accounting Standards Board 1 , Wang et al. 2 , and Wang and Strong 3 . The model consists of four essential attributes (or assertions): 'Accessibility,' 'Interpretability,' 'Relevance,' and 'Integrity.' Four sub-attributes lead to an evaluation of Integrity: 'Accuracy,' 'Completeness,' 'Consistency,' and 'Existence.' These sub-attributes relating to 'Integrity' are intrinsic in nature and relate to the process of how the information was created while the first three attributes: 'Accessibility,' 'Interpretability,' and 'Relevance' are extrinsic in nature. We present our model as an evidential network under the belief-function framework to permit user assessment of quality parameters. Two algorithms for combining assessments into an overall IQ measure are explored, and examples in the domain of medical information are used to illustrate key concepts. We discuss two scenarios, 'online-user' and 'assurance-provider,' which reflect two likely and important aspects of IQ evaluation currently facing information users -concerns about the impact of poor quality online information, and the need for information quality assurance.

AIMQ: a methodology for information quality assessment

Information & Management, 2002

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.

Toward a Unified Model for Information Quality

2008

ABSTRACT We present a model which allows to de! ne in an uniform way information-uality dimensions related to heterogeneous types of information, such as structured data managed in data bases, semijstructured and unstructured texts and imj ages. We! rst de! ne a set of concepts that allow to represent several basic characteristics of such heterogeneous types of information.

Algorithmic check of standards for information quality dimensions

In: The philosophy of information quality. Floridi, Luciano and Illari, Phyllis, eds. Springer. (In Press)

An important aspect of defining IQ standards is that sound information conforming to a specification should be error-free . We propose to assess information quality dimensions and check their standards by way of an algorithmic procedure. We design an effective procedural way to determine if and where IQ standards fail and to establish algorithmic resolution and evaluation methods that provide a metric appropriate to our quality checking system. This model is largely inspired by systems for quality standard assessment of software production, but it assumes a very high abstraction level. Our claim is that any information processing system, also not necessarily software based ones, can be designed after (some variations of) our model. A detailed formal translation of the definitions involved in our model is available in a machine-checked code.

Heuristic Principles and Differential Judgments in the Assessment of Information Quality

Journal of the Association for Information Systems, 2017

Information quality (IQ) is a multidimensional construct and includes dimensions such as accuracy, completeness, objectivity, and representation that are difficult to measure. Recently, research has shown that independent assessors who rated IQ yielded high inter-rater agreement for some information quality dimensions as opposed to others. In this paper, we explore the reasons that underlie the differences in the "measurability" of IQ. Employing Gigerenzer's "building blocks" framework, we conjecture that the feasibility of using a set of heuristic principles consistently when assessing different dimensions of IQ is a key factor driving inter-rater agreement in IQ judgments. We report on two studies. In the first study, we qualitatively explored the manner in which participants applied the heuristic principles of search rules, stopping rules, and decision rules in assessing the IQ dimensions of accuracy, completeness, objectivity, and representation. In the second study, we investigated the extent to which participants could reach an agreement in rating the quality of Wikipedia articles along these dimensions. Our findings show an alignment between the consistent application of heuristic principles and inter-rater agreement levels found on particular dimensions of IQ judgments. Specifically, on the dimensions of completeness and representation, assessors applied the heuristic principles consistently and tended to agree in their ratings, whereas, on the dimensions of accuracy and objectivity, they not apply the heuristic principles in a uniform manner and inter-rater agreement was relatively low. We discuss our findings implications for research and practice.

Information quality: Purpose and dimensions

In this article I examine the problem of categorising dimensions of information quality (IQ), against the background of a serious engagement with the hypothesis that IQ is purposedependent. First, I examine some attempts to offer categories for IQ, and a specific problem that impedes convergence in such categorisations is diagnosed. Based on this new understanding, I suggest a new way of categorising both IQ dimensions and the metrics used in implementation of IQ improvement programmes according to what they are properties of. I conclude the paper by outlining an initial categorisation of some IQ dimensions and metrics in standard use to illustrate the value of the approach.

Subjective Information Quality in Data Integration

Advances in business strategy and competitive advantage book series, 2014

This chapter focuses on the science of human perception of information quality and describes a subset of Information Quality (IQ) dimensions, which are termed Subjective Information Quality (SIQ). These dimensions typically require a user's opinion and do not have a clear mathematical technique for finding their value. Note that most dimensions can be measured through multiple techniques, but the SIQ ones are most useful when the user's experience, opinion, or performance is accounted for. This chapter explores SIQ while considering information obtained from multiple sources, which is a common occurrence when employing visualizations to perform business or intelligence analytics. Thus, the issues addressed here are the assessment of subjective perception of quality of data shown through visual means and principles on how to estimate the subjective quality of combined information sources. Value-Added Can increase the value of data The user can judge or assess the value added to the data Objectivity Formulas applied User opinion Timeliness Can reflect how up-to-date the data is with respect to the task User judges based on previous experience Understandability Can provide clear and simple data User can understand the data easily Concise Representation The shortest representation is known User judges based on previous experience Appropriate Amount of Data The needed amount is known User expertise is required Security Against a standard metric Users experience or performance with the data Accessibility Against a standard metric Based on user's experiences Consistent Representation Count different representations User's opinion Accuracy Formula based on known, exact value Expert estimation when exact value not available Completeness Count missing values in structured sources User's opinion for unstructured text