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It is now assumed that poor quality data is costing large amounts of money to corporations all over the world. Although research on methods and techniques for data quality assessment and improvement have begun in the early nineties of the past century and being currently abundant and innovative, it is noted that the academic and professional communities virtually have no dialogue, which turns out to be harmful to both of them. The challenge of promoting the relevance in information systems research, without compromising the necessary rigor, is still present in the various disciplines of information systems scientific area [1,2], including the data quality one. In this paper we present "data as a corporate asset" as a business philosophy, and a framework for the concepts related to that philosophy, derived from the academic and professional literature. According to this framework, we present, analyze and discuss a single explanatory case study, developed in a fixed and mobile telecommunications company, operating in one of the European Union Countries. The results show that, in the absence of data stewardship roles, data quality problems become more of an "IT problem" than typically is considered in the literature, owing to Requirements Analysis Teams of the IS Development Units, to become a "quality negotiator" between the various stakeholders. Other findings are their bottom-up approach to data quality management, their biggest focus on motivating employees through innovative forms of communication, which appears to be a critical success factor1 (CSF) for data quality management, as well as the importance of a data quality champion [3] leadership.

"The Household Finance and Consumption Survey (HFCS) provides information about household wealth (real and financial assets as well as liabilities) from 15 Euro-countries around the year 2010 (first wave). The survey will be the central dataset in this topic in the future. However, several aspects point to potential methodological constraints regarding cross-country comparability. Therefore the aim of this paper is to get a better insight in the data quality of this important data source. The framework for our analysis is the 'Guidelines for Micro Statistics on Household Wealth' from the OECD (2013). We have two main focuses: First, we present a synopsis of cross-country differences, which is the core of the paper. We compare the sampling processes, the interview modes, the oversampling techniques, the unit and item non-response rates and how it is dealt with them via weighting and imputation as well as further points which might restrict cross-country comparability of net wealth. We classify the individual country behavior and evaluate the impact on net wealth. Second, we give a first insight in the selectivity of item non-response in a cross-national setting. We make use of logit models to identify differences in characteristics as well as item non-response patterns across countries." (author's abstract)

This paper examines the construction and use of data sets in political science. We focus on three interrelated questions: How might we assess data quality? What factors shape data quality? and How can these factors be addressed to improve data quality? We first outline some problems with existing data set quality, including issues of validity, coverage, and accuracy, and we discuss some ways of identifying problems as well as some consequences of data quality problems. The core of the paper addresses the second question by analyzing the incentives and capabilities facing four key actors in a data supply chain: respondents, data collection agencies (including state bureaucracies and private organizations), international organizations, and finally, academic scholars. We conclude by making some suggestions for improving the use and construction of data sets.It is a capital mistake, Watson, to theorise before you have all the evidence. It biases the judgment.—Sherlock Holmes in "A Study in Scarlet"Statistics make officials, and officials make statistics."—Chinese proverb

"Provides new insights into the accuracy and value of online panels for completing surveys Over the last decade, there has been a major global shift in survey and market research towards data collection, using samples selected from online panels. Yet despite their widespread use, remarkably little is known about the quality of the resulting data. This edited volume is one of the first attempts to carefully examine the quality of the survey data being generated by online samples. It describes some of the best empirically-based research on what has become a very important yet controversial method of collecting data. Online Panel Research presents 19 chapters of previously unpublished work addressing a wide range of topics, including coverage bias, nonresponse, measurement error, adjustment techniques, the relationship between nonresponse and measurement error, impact of smartphone adoption on data collection, Internet rating panels, and operational issues."--Publisher's description

AbstractWith the increasing capabilities of machine learning systems and their potential use in safety-critical systems, ensuring high-quality data is becoming increasingly important. In this paper, we present a novel approach for the assurance of data quality. For this purpose, the mathematical basics are first discussed and the approach is presented using multiple examples. This results in the detection of data points with potentially harmful properties for the use in safety-critical systems.

Whether computer assisted data collection methods should be used for survey data collection is no longer an issue. Most professional research organizations, commercial, government and academic, are adopting these new methods with enthusiasm. Computer assisted telephone interviewing (CATI) is most prevalent, and computer assisted personal interviewing (CAPI) is rapidly gaining in popularity. Also, new forms of electronic reporting of data using computers, telephones and voice recognition technology are emerging. This paper begins with a taxonomy of current computer assisted data collection methods. It then reviews conceptual and theoretical arguments and empirical evidence on such topics as: (1) respondents and interviewer acceptance of new techniques, (2) effect of computer assisted interviewing on data quality, (3) consequences for survey costs and (4) centralized vs. decentralized deployment of CATI.

Abstract. Most web surveys collect data through nonprobability or opt-in online panels, which are characterized by self-selection. A concern in online research is the emergence of professional respondents, who frequently participate in surveys and are mainly doing so for the incentives. This study investigates if professional respondents can be distinguished in online panels and if they provide lower quality data than nonprofessionals. We analyzed a data set of the NOPVO (Netherlands Online Panel Comparison) study that includes 19 panels, which together capture 90% of the respondents in online market research in the Netherlands. Latent class analysis showed that four types of respondents can be distinguished, ranging from the professional respondent to the altruistic respondent. A profile of professional respondents is depicted. Professional respondents appear not to be a great threat to data quality.

We address weaknesses in the Peace Research Insitute Oslo (PRIO) Battle Deaths Dataset, and as a result draw contradicting conclusions to those presented by Lacina and Gleditsch. Our analysis focuses on the availability of data on battle deaths within specific conflict-years and problems encountered when data from multiple types of sources are combined. We repeat Lacina, Gleditsch, and Russett's analysis of battle deaths over time, with an attempt to provide a more robust model and incorporate an estimate of the uncertainty present in the PRIO Battle Deaths Dataset. This reanalysis reveals that the data used to establish the PRIO Battle Deaths Dataset does not offer a clear answer as to whether battle deaths have decreased or increased since the end of the Second World War. We contend that while the PRIO Battle Deaths Dataset offers the most comprehensive assembly of battle deaths data available to date, it is not suitable for analysis across countries or over time.

Two major criteria for assessing the quality of sociological data are a) whether the data is representative and b) whether the respondent's responses can be trusted. Sociological surveys in wartime Ukraine face challenges that may significantly affect these two criteria - and thus data quality - due to factors such as mass migration, occupation, or increased patriotism. This contribution assesses how systematic errors can be assessed with different methods that mitigate impacts, ensuring survey reliability amid challenging conditions. As a result, it can be stated that systematic errors associated with problems of representativeness and respondent sincerity increase under wartime conditions, but not to such an extent that they put into question the main survey results.