INFORMATION QUALITY IN LARGE ENGINEERING AND CONSTRUCTION PROJECTS: A DELPHI CASE STUDY

This Delphi study identifies problems that have significant impacts on profits gained from large engineering and construction projects in a European company. Information quality gained remarkable weight among the identified problems. The problems were ranked in accordance to their estimated impact on the project profit margins. Within a consolidated list of 125 problems identified altogether, the final ranking of the top 18 problems was strongly agreed upon by an expert panel. The panel involved experienced engineering and management professionals throughout the construction project supply chain. Among the top 18, eight problems, including the top six, concerned information quality. The results address a need for increased focus on information quality challenges in the target organization and provide a detailed account of such challenges in comparison to the previous literature on information quality in engineering and construction

Managing data and information quality in construction engineering : a system design approach

Following the ADR method made it possible not only to develop a tool, but also to formulate design principles and abstract the findings to a generalizable level. This study contributes to knowledge and practice in several ways. First, I offer five design principles for DQ/IQ assessment tools. These design principles are specifically aimed at mitigating the unavoidable challenges and their consequences in construction engineering projects. By accepting these unavoidable challenges and consequences and subsequently providing means for managing the results in a controlled manner, these principles makes it possible to avoid project delays and still reach a sufficient level of DQ/IQ in the end. Second, the development and implementation of a tool in which these design principles are embedded demonstrates the effectiveness of the design principles. A formal evaluation performed by comparing a project that used the tool with two projects that did not, showed a significantly better level of DQ/IQ in the project using the tool. Third, as a result of implementing the tool in a total of 12 construction engineering projects, it was possible to determine three needed and sufficient quality dimensions for rule-based assessment. This finding offers valuable information to theory as well as to practitioners aiming at assessing DQ/IQ in their projects. Fourth, by revealing the relationship between unavoidable challenges and their consequences in construction engineering, this thesis offers unique insights into the nature of projects in that field, which is highly needed when performing DQ/IQ assessment. These insights will help DQ/IQ researchers enhance their understanding of a very complex and under-researched context. Fifth, by providing a ranked list of DQ/IQ problems experienced at EUMEC, this thesis offers a more detailed explanation of DQ/IQ problems causing delays and cost overruns than is the case in previous research. All in all, this research reduces a gap in the existing literature, namely the scarce amount of DQ/IQ research on construction engineering. The complexity of this industry makes it difficult and time consuming for an information systems researcher to fully understand the nature of construction engineering. This complexity might explain the scarce amount of research in this cross-disciplinary field, and this thesis helps reduce that gap

Data and Information Quality Dimensions in Engineering Construction Projects

Poor data and information quality (DQ/IQ) causes delays and cost overruns in engineering construction projects. However, only little DQ/IQ research has been performed in this context. This paper explores quality dimensions in the context of engineering construction projects. The most important dimensions identified by Ge, et al., (2011) is used as a basis and compared with dimensions used in 12 large engineering construction projects in one organization. The findings show that six of these dimensions are in use in those projects: accessibility, security, relevancy, completeness, consistency, and timeliness. In addition, the findings indicate another dimension also very important in this context; logical coherence. The logical coherence dimension compares different data values and determines if there is any illogicality between them. Three dimensions are monitored by rules provided by a DQ/IQ tool, and we discuss about the contributions which such a tool can provide for an engineering construction firm

IMPLEMENTING CLOUD BASED BIG DATA PLATFORMS – A CASE USING MICROSOFT AZURE

Digital transformation and implementation of big data platforms are inevitable in any industry. Big data constitutes an important area of research, however, implementation of platforms like Microsoft Azure have yet to be explored. Through a narrative case study, we aim to explore the implementation of such big data platforms in the power industry. Our case is based in a Norwegian power company who are early movers in implementing Microsoft’s Azure platform across multiple units in the organization. With the support of top management and eager business units one would expect this process to be fairly straight forward. Our findings show that the maturity of the technology, in addition to challenges of being an early mover, create an unexpected path to success.IMPLEMENTING CLOUD BASED BIG DATA PLATFORMS – A CASE USING MICROSOFT AZUREpublishedVersionNivå

Discontinuities and Best Practices in Virtual Research Collaboration

Research collaboration has become increasingly global, as collaboration technologies continue to advance and as research problems become more interdisciplinary and global. Virtual research teams have processes and challenges that are unique from a typical virtual team, and we need a better understanding of how such teams can utilize virtual research environments to their advantage. We examine this question from a review of the relevant literature and an analysis of experiences and reflections from a doctoral seminar that studied and experienced the process of virtual research collaboration

The Design and Emergence of a Data/Information Quality System

Poor data and information quality (DQ/IQ) has remained a consistent problem plaguing both the practitioner and academic communities in Information Systems (IS). The consequences of poor DQ/IQ is particularly severe in Construction Engineering, and the field lacks sufficient DQ/IQ assessment frameworks and tools. To address this shortcoming, we applied an action design research (ADR) approach to develop and implement a DQ/IQ assessment tool called Information Quality System (IQS). The multi-year research project took place in a European construction engineering company, and lasted from 2007 to 2012. We drew upon insights from the literature on DQ/IQ assessment and related challenges in construction engineering, as well as practical lessons learned from managing DQ/IQ in the target organization. Through our research, we develop a set of design principles for meeting DQ/IQ challenges.Validerad; 2015; Nivå 2; 20150828 (mausei

IMPLEMENTING CLOUD BASED BIG DATA PLATFORMS – A CASE USING MICROSOFT AZURE

Digital transformation and implementation of big data platforms are inevitable in any industry. Big data constitutes an important area of research, however, implementation of platforms like Microsoft Azure have yet to be explored. Through a narrative case study, we aim to explore the implementation of such big data platforms in the power industry. Our case is based in a Norwegian power company who are early movers in implementing Microsoft’s Azure platform across multiple units in the organization. With the support of top management and eager business units one would expect this process to be fairly straight forward. Our findings show that the maturity of the technology, in addition to challenges of being an early mover, create an unexpected path to success

Improving data quality in construction engineering projects : an action design research approach

Aurthor's version of an article in the journal: Journal of Management in Engineering. Also available from the publisher at: http://dx.doi.org/10.1061/(ASCE)ME.1943-5479.0000202The topic of data and information quality (DQ/IQ) is a longstanding issue of interest in both academia and practice in the construction engineering field. Poor DQ/IQ has led to poor engineering drawings that, in turn, have led to delays and, eventually, cost overruns. In this paper, a study is reported that took an action design research (ADR) approach to develop and evaluate a DQ/IQ assessment tool, which is called the information quality system (IQS), in a large global engineering and construction company. The evaluation was performed by comparing the level of DQ/IQ in a project that used IQS with two projects that did not use the tool. The result is encouraging: The DQ/IQ in the project using IQS was significantly higher overall than in the two other projects. The implication is that a tool based on the design principles on which IQS was built is likely to help improve DQ/IQ in engineering systems and, hence, in engineering drawings. Consequently, it will decrease project delays and cost overruns. More generally, this paper adds to the discourse in the literature on the use of information and communication technologies (ICTs) in the construction context. This paper illustrates another successful application of action-oriented research that can solve practical problems while generating academic knowledge. In taking a design approach, the literature on the use of action research in construction engineering and management is augmented