Managers in complex organisations often have to make quick decisions on whether new information sharing developments are worth undertaking or not. Such decisions are hard to make, especially at an enterprise level. Both costs and risks are regularly underestimated. Existing approaches to managing risk and estimating cost are principally focused on creating detailed predictions based on substantial models of the planned development. They aim to support project managers throughout the development process itself, rather than giving a low-cost indicator for use in early-stage decision making. Our objective is to help managers and stakeholders of large, complex organisations, such as the National Health Service (NHS) in the UK, make better informed decisions on points of cost and risk of new software systems that will reuse or extend their existing information infrastructure, before any implementation is undertaken. We analysed 18 case studies describing recent software developments introduced by providers of health care services, looking for common points of high cost and risk. From the case studies analysis, we found that the movement of data within and between organisations was a key indicator of high cost and risk. Data movement can be hindered by numerous technical barriers, but also by other challenges arising from social aspects of an organisation. Hence, we devised a catalogue of socio-technical data movement anti-patterns that under certain conditions can introduce high cost and risk to the organisation. In this thesis, we propose a new method aiming to identify places of high cost and risk when existing data needs to move to a new development. The method is low-cost and combines both technical and social aspects, but relies only on information that is likely to be already known to key stakeholders, or will be cheap to acquire. The method is based on the data journey model, a new lightweight technique that captures movements of data within or between organisations. The data journey model describes an abstraction of large, complex eco-systems focusing on the high-level journeys data take through networks of people and systems. To assess the effectiveness of our method and the accuracy of our predictions, we applied the method in real world settings in the NHS domain. We worked with clinicians to model the movements of data in five NHS studies from different Foundation Trusts across the UK. The results of the evaluation showed that our method was able to cheaply and quickly identify most of the points of high cost/risk that the hospital staff had identified, along with several other possible directions that the staff did not identify for themselves, but agreed could be promising. Finally, the results of the evaluation showed that the data journey modelling method can be completed in less than a couple of hours (including training). Also, the simplicity of our modelling technique can empower domain experts with no particular modelling expertise to quickly identify opportunities for cost savings in new developments, as well as existing ones.