Cost overruns in infrastructure projects has been a well-known phenomenon for almost a decade. As per the generally accepted definition, the industry has been measuring cost overruns as the cost exceedance with respect to the approved contractor’s budget. But researchers are realizing the fact that the decision-to-build is generally taken long before the ‘budgeting’, which thereby falsifies the existing definition of ‘cost overruns’. It has been henceforth also realized that projects under overruns also don’t offer the best capital value because their as-built costs exceed the costs estimated for them during the “decision-to-build” stage. As a result, such projects debar other deserving projects from getting approved by the funding authority/infrastructure ministry, which could potentially deliver more value for the same invested costs. A lot of projects which were in high overruns in the recent past shouldn’t have been approved/funded/preferred by the decision makers over others (For example: The Channel Tunnel in the UK, cost overruns = 100%, Denver intl. airport in the USA, cost overruns =200%). Consistent overruns also indicate that even with consecutive projects, the experts are not learning to improve on it as well. As a result, cost overruns in infrastructure projects have been a subject of discussion for more than 2 decades now. It has been realized that not only the estimates presented to the decision makers should be accurate, but also the total project costs post approval (at the decision to build point) should adhere (be controlled) to the approved estimate. Either of the two situations/or both would lead to cost escalations/overruns. Researchers have been demanding more exploration on the front-end cost escalations of the projects rather than on the cost overruns in the execution phases. It has been realized that the front-end phases allow for the maximum control over the as-built costs, but ironically, the industry experiences more cost escalations in the front-end phases. It has been emphazied that ex-post studies on the front-end phases of projects can bring revelations on how projects suffer front-end escalations. Limited research exists on the front-end phases of infrastructure projcts, with almost all of them based on the Nordic countries and only one on the Dutch governance setting by Nijkamp & Ubbels (1998). The limited research mostly includes the studies that have produced ‘characteristic escalation figures’ & the ‘factors/causes’ (technical, political & cognitive). All these factors except ‘technical & cognitive’ are quite external to the project teams and are difficult to research on through ex-post studies. Citing ‘political factors’ as the prime cause, most researches on the ‘solutions’ counterpart have been discussing over ‘political misrepresentation’ as the main factor/cause of front-end cost escalations. Very less researches have been done on the ‘technical & cognitive’ factors, which are more internal to the project teams and can be easily studied through the ex-post researches on the front-end phases of recently completed infrastructure projects. This thesis aimed to gather such ‘technical/control’ based factors & their data collection/analysis methods, in order steer them for controlling the total project costs from the very front-end phases itself. This thesis gathered 19 such ‘control’-based technical factors from Arcadis cost experts and considered it as the starting point. Structured-questionnaire surveys then helped to filter out the most important factors which lead to front-end cost escalations. Finally, 12 interviews from two recent Dutch projects (Schiphol Expansion : Landside works & Redevelopment of highway N270) gave insights on what approach should be taken for data collection [cross-learning (CL)/open-data gathering (OA)] & data analysis [top-down (TD)/bottom-up (BU)] methods in order to steer the ‘factors’ in the front-end phases of the projects. The thesis concluded that ‘project-control’ based technical factors are crucial as well, and can be steered. The research results gave some insights on which data collection/analysis approaches should be taken for steering each of such ‘factor’ from the front-end phases itself. The thesis conclusively highlighted the need of further research on gathering more such ‘factors’ and obtaining suitable data collection (CL/OA) & data analysis (TD/BU) methods for steering them. The need to focus on ‘project controls’ based factors (and not just political factors) from the very front-end phases was also conveyed.