Survey & Statistical Computing

We collect data in order to increase our knowledge, but we always have some knowledge before we start. Our existing knowledge raises the questions for which we need more information, and it also guides us in deciding what further data to collect and how to collect it. Models allow us to generalise from specific observed data to a wider situation. When we analyse data we (usually) update our knowledge. If we can find a formal representation for our knowledge, then a standard statistical technique provides a way to formalise the process of updating our knowledge. This can be the basis for the integration of multiple data sets that relate to different aspects of the same system. While of general importance, this approach is the only way of developing a coherent and integrated understanding of complex systems which are too extensive to observe with a single data set. But complex methodology is difficult to understand, so we must address the issues of convincing users from the application domain that our models are appropriate and valid, and of making the results obtained from the methodology accessible. This paper was originally prepared for a keynote presentation at the Association for Survey Computing conference on Maximising Data Value, September 2005.