Open data analysis platforms are being adopted to support collaboration in science and business. Studies suggest that analytic work in an enterprise occurs in a complex ecosystem of people, data, and software working in a coordinated manner. These studies also point to friction between the elements of this ecosystem that reduces user productivity and quality of work. LabBook is an open, social, and collaborative data analysis platform designed explicitly to reduce this friction and accelerate discovery. Its goal is to help users leverage each other's knowledge and experience to find the data, tools and collaborators they need to integrate, visualize, and analyze data. The key insight is to collect and use more metadata about all elements of the analytic ecosystem by means of an architecture and user experience that reduce the cost of contributing such metadata. We demonstrate how metadata can be exploited to improve the collaborative user experience and facilitate collaborative data integration and recommendations. We describe a specific use case and discuss several design issues concerning the capture, representation, querying and use of metadata.

If Lisa visits Dr. Brown, and there is no record of the drug he prescribed her, can we find it? Data sources, much to analysts' dismay, are too often plagued with incompleteness, making business analytics over the data difficult. Data entries with incomplete values are ignored, making some analytic queries fail to accurately describe how an organization is performing. We introduce a principled way of performing value imputation on missing values, allowing a user to choose a correct value after viewing possible values and why they were inferred. We achieve this by turning our data into a graph network and performing link prediction on nodes of interest using the belief propagation algorithm.