Purpose-Focused View of Information Quality

This chapter presents a logical technology-independent fully content-focused inquiry into the operations quality problems of any symbolic representations of reality. This teleological operations-research-based approach demonstrates that a purpose-focused view, natural within the operation- research (OR) methodology, facilitates faster progress in identifying the fundamental relationships of more lasting validity for business, public administration, and military purposive operations. Products of the Information Quality Programs and Initiatives at MIT (MITIQ Program) serve as recognized research references. It contains definitions of (1)A tentatively universal hierarchical taxonomy of the entire universe of quality requirements, (2) The tentative definitions of the first five tentatively universal operations quality requirements for any situation, (3) An economic sequence of their examination, and (4) The first seven tentatively universal principles in this domain. This quality framework may assist researchers in further studies and assist practitioners in understanding the intricate relationships among operations quality attributes. The chapter presents the tentative results of the author’s research in progress.

Galaxy's Data Quality Program

This paper is a case study of the data quality program implemented for Galaxy, a large health care data warehouse owned by UnitedHealth Group and operated by Ingenix. The paper presents an overview of the program’s goals and components. It focuses on the program’s metrics and includes examples of the practical application of statistical process control (SPC) for measuring and reporting on data quality. These measurements pertain directly to the quality of the data and have implications for the wider question of information quality. The paper provides examples of specific measures, the benefits gained in applying them in a data warehouse setting, and lessons learned in the process of implementing and evolving the program.

DQ Options

Data plays a critical role in organizations up to the point of being considered a competitive advantage. However, the quality of the organizations’ data is often inadequate, affecting strategic and tactical decision making, and even weakening the organization’s image. Nevertheless it is still challenging to encourage management to invest in data quality improvement projects. Performing a traditional feasibility analysis based on Return on Investment, Net Present Value, etc., may not capture the advantages of data quality projects: their benefits are often difficult to quantify and uncertain; also, they are mostly valuable because of the new opportunities they bring about. Dealing with this problem through a real options approach, in order to model its intrinsic uncertainty, seems to be an interesting starting point. This paper presents a methodological framework to assess the benefits of a Data Quality project using a real options approach. Its adequacy is validated with a case study.

Management of Data Streams for Large-Scale Data Mining

In an enterprise setting, a major challenge for any data mining operation is managing data streams or feeds, both data and metadata, to ensure a stable and certifiably accurate flow of data. Data feeds in this environment can be complex, numerous and opaque. The management of frequently changing data and metadata presents a considerable challenge. In this paper, we articulate the technical issues involved in the task of managing enterprise data and propose a multi-disciplinary solution, derived from fields such as knowledge engineering and statistics, to understand, standardize, and automate information acquisition and quality management in preparation for enterprise mining.

Analyzing Information Quality in Virtual Networks of the Services Sector with Qualitative Interview Data

In this chapter, a novel framework is introduced for analyzing information quality within information processes of complex organizational networks on the basis of qualitative data. Networking and virtualization call for new ways of looking into information quality. Public organizations, cooperatives and non-governmental organizations are forming networks, or entering into networks of companies. Tools for analyzing information quality in such environments have been lacking. The newly developed framework is operationalized within multi-actor, multi-professional networks offering safety telephone services for ageing people. These services utilize well-being technology. The analysis is based on data from interviews with professionals working in several service networks of different types and sizes in Finland. The information quality analysis framework helps in identifying information quality dimensions that are weak in a network. This analysis is usefully combined with an investigation of network collaboration that identifies weaknesses and strengths in network collaboration affecting management of information quality.

Qualtiy Measures and the Information Consumer

This chapter discusses the proposal for using quality criteria to facilitate information searching. It suggests that the information consumer can be assisted in searching for information by using a consumer-oriented model of quality. This is achieved by presenting the consumer with a set of relevant quality criteria from which they can select those of most importance to them at that present time, and allowing them to state preference values and importance weightings for each criterion. The consumer’s quality profile can then be used to focus an information search onto relevant search domains, and produce a more focused output. The chapter presents our model of quality and shows that quality measures can be used to focus information searches by achieving statistically significant changes in the ordering of the obtained search results.

Information Quality

Commercial database management systems (DBMS) have come a long way with respect to efficiency and more recently, with respect to quality and user friendliness. Not only do they provide an efficient means to store large amounts of data and intuitive query languages to access the data; popular DBMS also provide a whole suite of tools to assess, store, manage, clean, and retrieve data in a user-friendly way. Some of these feature address database experts, others are targeted at end-users with little or even no database knowledge. The recent developments in the field of autonomic computing drive the ease-of-use even further. In this report we study how well a typical DBMS meets the goal of providing a high-quality data storage and retrieval facility. To this end, we draw on an established set of information quality criteria and assess how well an exemplary DBMS fares. While quality criteria are usually defined for a set of data, we extend, wherever possible, the definitions to the systems that manage this data

Applying Name Knowledge to Information Qualtiy Assessments

An introduction to name knowledge and its application to information quality assessments through an expert system is discussed in this chapter. The quality of name information has become an increasingly important issue as companies strive to implement Customer Relationship Management (CRM) strategies in which the customer name plays an important role in the entity resolution process for data integration applications - ultimately impacting customer recognition systems. As many applications have been developed and refined for assessing and improving the quality of mailing address information, the potential exists to affect a similar success for customer name information. This chapter discusses both theoretical and practical considerations in the approach, design, and administration of systems for assessing the quality of name information.

Metadata Quality Problems in Federated Collections

This chapter presents results from our empirical studies of metadata quality in large corpuses of metadata harvested under Open Archives Initiative (OAI) protocols. Along with a discussion of why and how metadata quality is important, an approach to conceptualizing, assessing metadata quality is presented. The approach is based on a more general model of information quality for many kinds of information beyond just metadata. A key feature of the general model is its ability to condition quality assessments by context of information use, such as the types of activities that use the information, and the typified norms and values of relevant information-using communities. The chapter presents a number of statistical characterizations of samples of metadata from a large corpus built as part of the Institute of Museum and Library Services Digital Collections and Contents project containing OAI-harvested metadata, interprets these statistical assessments and links to the quality measures. Finally the chapter discusses several approaches to quality improvement for metadata based on the study findings.

Assessing Mass Consumer Information Quality Requirements Using Conjoint Analysis

This chapter demonstrates how conjoint analysis can be used to improve the design and delivery of mass consumer information products. Conjoint analysis is a technique that market researchers have used since the 1960’s to better understand how buyers make complex purchase decisions, to estimate preferences and importance ratings for product features, and to predict buyer behavior. This chapter describes the steps for performing a conjoint analysis to assess information quality preferences of potential home buyers interested in using a real estate website to help them locate properties for sale. The author hopes that this tutorial will convince information systems professionals of the usefulness of conjoint analysis as a tool for discerning how to prioritize information quality requirements so that the resulting systems produce information products that better serve the needs of their customers.