Text mining of Post Project Reviews

Post Project Reviews (PPR) are a rich source of knowledge and information for organisations - if they have the time and resources to analyse them. Too often such reports are stored, unread by many who can benefit from them. PPRs attempt to document the project experience – both good and bad. If these reports were analysed collectively, they may expose important detail, perhaps repeated between projects. However, because most companies do not have the resources to examine these PPR, either individually or collectively, important insights are missed thereby leading to a missed opportunity to learn from previous projects. Hidden knowledge and experiences can be captured by using knowledge discovery and text mining to uncover patterns, associations, and trends in data. The results might then be used to enhance processes, improve customer relationships, and identify specific problem areas to address. This paper outlines an ongoing research project that investigates the use of knowledge discovery and text mining on Post Project Reviews. An illustrative example will be presented using case studies from the construction sector. The PPR processes of two construction companies were mapped with the aim of understanding the context, format, terminologies used and key knowledge areas suitable for text mining. The textual examination of the PPR reports was complemented by semi-structured interviews and workshops to understand the production and content of the reports. Preliminary results highlight that although organisations have publicised, standard processes for PPR, there is a variance in how these are conducted and produced on a regional basis. These variances provide a number of challenges for organisations from a corporate perspective. Also, there is an over-reliance on key individuals with little attempt to make some of their knowledge more explicit and therefore easier to disseminate between project team members. This paper summarises the challenges in identifying the type of knowledge to be text mined, the format of PPR reports and the process of conducting PPR. It will also highlights the development of suitable ontologies for text mining PPR reports and provides recommendations on how to improve the PPR process of companies