Use of big data in project evaluations

Abstract

Purpose – The purpose of this paper is to investigate how Big Data can be used in project evaluations. Design/methodology/approach – The study is based on literature research and interviews with 15 professionals in IT, project and asset management and government agencies. The authors discuss and illustrate what data that can be used for project evaluations and discuss potential obstacles. Findings – New data is creating new opportunities to analyse a phenomenon based on different types of data. Interesting data categories include: internet traffic, movement-related data, physical environment data and data in organisational internal systems. The authors show how these data categories can be applied in project evaluations. Research limitations/implications – Big Data gives an opportunity to add quantitative data in ex post evaluations. Use of Big Data can serve as a step towards a stronger technology focus in evaluations of projects. Practical implications – There are major advantages in using Big Data, increasing the opportunities to find indicators that are relevant when a project is evaluated. Social implications – Possible problematic issues related to use of Big Data that are addressed in the study include: availability, applicability, relevance, privacy policy, ownership, cost and competence. The study indicates that none of the challenges need to hinder use of Big Data when evaluating projects, provided that the issues are properly managed. Originality/value – The study illustrates how Big Data can be applied in project management research.

Approaches, technologies and importance of analysis of the number of train travellers

“…Proximity sensing can be based on logging of Bluetooth units. This is applied by the Norwegian Road Authority (Olsson and Bull Berg, 2015).…”

“…Scriven (1991) use another definition, stating that evaluation is the process of determining the merit, worth or value of something. Data quality and availability are key issues in evaluations (Olsson & Bull-Berg, 2015;Parthasarathi & Levinson, 2010;Small, 1999), and access to good relevant data is a common challenge in project evaluations. This may seem like a paradox, as the volume of data generally increases.…”

A framework for the utilization of Building Management System data in building information models for building design and operation

“…However, there have been challenges in utilizing such data to meaningfully inform and improve subsequent project design and delivery. The Big Data syndrome is also associated with the streaming of energy performance data from such acquisition systems [14]. More often than not, energy performance data collected from acquisition systems grow very large in size and is too unstructured to put to immediate beneficial use without some form of structuring and analysis.…”

Understanding project evaluation – a review and reconceptualization

“…Compatible products with international players, Functionality, Usability, reliability, flexibility, Technical performance, simplicity of the design, Appropriateness Makarova & Sokolova, 2014;Shek & Yu, 2012;Lauras et al, 2010;Tadeu de Oliveira Lacerda et al, 2011;Chiu et al, 2008;Barclay, 2008;Harnisch, 2001;Olsson & Bull-Berg, 2015;Tohumcu & Karasakal, 2010) Project achievements: Meeting the explicit and implicit objectives of the project set by -possibly multiple differentstakeholders, Meeting user requirements, Creating value for the stakeholders, problem solving Tatikonda & Rosenthal, 2000;Alderman & Ivory, 2011;Pisarski et al, 2011;Xu & Yeh, 2014;Al-Meshekeh & Langford, 1999;Ika et al, 2012;McLeod et al, 2012;Andersen et al, 2002;Turner el al., 2009;Chang et al, 2013) Product achievements: Addresses a need, product is used, Functionality, Maintainability, Reliability, availability, safety, Meeting technical performance specifications McLeod et al, 2012;Andersen et al, 2002;Khan et al, 2013;Ali et al, 2008;Rolstadås et al, 2014;Chan et al, 2002;Khan et al, 2013;Ali et al, 2008;Wohlin & Andrews, 2001;Tayler, 1992;Riggs et al, 1992) OBJECTIVE Efficiency (Rosacker & Olson, 2008;Makarova & Sokolova, 2014;Remenyi, 1999;Jovanović, 1999;Stefanou, 2001;Reddy & Sharma, 20...…”