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Presently, we are well aware that poor quality data is costing large amounts of money to corporations all over the world. Nevertheless, little research has been done about the way Organizations are dealing with data quality management and the strategies they are using. This work aims to find some answers to the following questions: which business drivers motivate the organizations to engage in a data quality management initiative?, how do they implement data quality management? and which objectives have been achieved, so far? Due to the kind of research questions involved, a decision was made to adopt the use of multiple exploratory case studies as research strategy [32]. The case studies were developed in a telecommunications company (MyTelecom), a public bank (PublicBank) and in the central bank (CentralBank) of one European Union Country. The results show that the main drivers to data quality (DQ) initiatives were the reduction in non quality costs, risk management, mergers, and the improvement of the company's image among its customers, those aspects being in line with literature [7, 8, 20]. The commercial corporations (MyTelecom and PublicBank) began their DQ projects with customer data, this being in accordance with literature [18], while CentralBank, which mainly works with analytical systems, began with data source metadata characterization and reuse. None of the organizations uses a formal DQ methodology, but they are using tools for data profiling, standardization and cleaning. PublicBank and CentralBank are working towards a Corporate Data Policy, aligned with their Business Policy, which is not the case of MyTelecom. The findings enabled us to prepare a first draft of a "Data Governance strategic impact grid", adapted from Nolan& MacFarlan IT Governance strategic impact grid [17], this framework needing further empirical support.

Today, it is a well known fact that poor quality data is costing large amounts of money to corporations all over the world. Despite the increasing research on methods, concepts, and tools for data quality (DQ) assessment and improvement, little has been done about corporate DQ management. The purpose of this research is to understand the nature and complexity of corporate DQ management, through various perspectives. These include the various kind of sponsorship, type and level of collaboration between business and IS/IT, organizational position of the DQ management team, scope of the DQ initiatives, roles, services provided, DQ methodologies, techniques and tools in use, etc. This paper presents, analyzes and discusses a single pilot exploratory case study, undertaken in a fixed and mobile telecommunications company in a European Union Country. The purpose of this case study is to check the validity of some initial propositions, and eventually find new ones, to be used in a subsequent multiple-case study, in order to provide an in-depth understanding of the corporate DQ management phenomenon.

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.

Publishing in top-ranking journals in the social sciences and international relations requires writing with clarity. Accurately described and transparent methods sections ensure high-quality academic writing. The methodology section of empirical papers should explain the exact steps taken by the authors when operationalizing concepts and testing hypotheses to facilitate replication. This also allows for monitoring quality, challenging findings, and promoting good scientific practices. The quality of methodology sections is the result of the interaction between academic cultures of data sharing, effective application of rules, and good-quality research data management (RDM). This article evaluates the impact of standards on replicability. We present an empirical analysis of a set of sixty-six articles published during the period 1984-2013 that use data from all waves of the European Values Survey. We find differences demonstrating the impact of good RDM and data policies on good scientific practice.

Big Data is an essential research area for governments, institutions, and private agencies to support their analytics decisions. Big Data refers to all about data, how it is collected, processed, and analyzed to generate value-added data-driven insights and decisions. Degradation in Data Quality may result in unpredictable consequences. In this case, confidence and worthiness in the data and its source are lost. In the Big Data context, data characteristics, such as volume, multi-heterogeneous data sources, and fast data generation, increase the risk of quality degradation and require efficient mechanisms to check data worthiness. However, ensuring Big Data Quality (BDQ) is a very costly and time-consuming process, since excessive computing resources are required. Maintaining Quality through the Big Data lifecycle requires quality profiling and verification before its processing decision. A BDQ Management Framework for enhancing the pre-processing activities while strengthening data control is proposed. The proposed framework uses a new concept called Big Data Quality Profile. This concept captures quality outline, requirements, attributes, dimensions, scores, and rules. Using Big Data profiling and sampling components of the framework, a faster and efficient data quality estimation is initiated before and after an intermediate pre-processing phase. The exploratory profiling component of the framework plays an initial role in quality profiling; it uses a set of predefined quality metrics to evaluate important data quality dimensions. It generates quality rules by applying various pre-processing activities and their related functions. These rules mainly aim at the Data Quality Profile and result in quality scores for the selected quality attributes. The framework implementation and dataflow management across various quality management processes have been discussed, further some ongoing work on framework evaluation and deployment to support quality evaluation decisions conclude the paper.