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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.

Several studies have pointed to the difficulties of obtaining good data on train ridership. There are at least two challenges regarding these data. First, train operators consider such data confidential business information, especially in high resolution. Second, the data that actually are available vary in quality and coverage. This paper studies mobile phone data as an alternative measure to obtain data about train ridership.Handset counts were obtained from one telecom operator for selected mobile phone base stations and compared with timetable data and APC. The selected base stations are located so that it is likely that a large share of the mobile phone traffic is generated by train passengers. The number of units connected to a base station is found to correspond relatively well with the trains that pass close to the base stations. A ratio between the handset count and APC data appear as promising in utilising handset count to calculate train ridership, with ratios around one in the rush hours. We discuss preliminary results as well as methodological and technical challenges.To make sure that we do not violate privacy concerns, the data used in the study have been approved by personal privacy representatives.

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