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This PDW, inspired by a session at the 2009 meeting in Chicago, brings together two developing threads of empirical research in organization theory and explores the potential that these might enable abductive research methods that do not minimize the continuing presence of informational differences in human systems. The ubiquity of the internet and advances in data storage have revolutionized the distributed collection, coding and analysis of narrative data reducing the need to summarize content, a practice that introduces bias and eliminates unrecognized differences in the data. Although these differences might be perceived to be irrelevant for the current project, they might become important as the growing data set is used in later studies. Separately, there is also increasing evidence that many statistical distributions in human systems exhibit power laws rather than normal distributions as is usually assumed in studies. New analysis techniques can potentially identify relevant weak signals in the tails of the above described data distributions from which one might infer hypothesized relationships to motivate future projects. Large and growing data sets combined with weak signal detection raise the prospect of an abductive approach to theory development. This PDW will gather interested members from both practice and research to develop future directions for this research and its implications for practice. Brief presentations and a panel discussion on research and practice will begin discussion. A workshop will follow to define potential practice directions and research questions. Cross-disciplinary links identified in the workshop will create future research programs and publications.