Issues of concern  Lack of a formalized data pipeline approach to feed computational platforms  Resulting in data sets consisting of single or relatively few types of outcomes or measures  Embedding business or domain logic in data structures  Technology changes render historical data incompatible  Inability to apply generalized, industry proven data management techniques and technologies  Contextual data (results) are not annotated with conceptual data (methods)  Inability to normalize across technologies  Proprietary files feed specialized analysis tools  Differentiation of transaction and decision support system

Addressed some of the issues  Begin data management at study assay design  Apply workflow and process automation tools  Isolate conceptual information in the metadata layer  Data Warehousing tool and technologies  API or customized data sets from the warehouse

How the hell are we going to do that  Data Definition  Flow ontology that reflects both assay components and biological system  Curate the evolving nature of both flow techniques and biological beliefs (what once was a suppressor cell is now a regulator)  Navigate diverse data  Visualization – Intuitive data exploration (how do I know what data I have to ask questions of and is it the right data)  Terminology (ontology again) is a patient visit a time point or an event  Data access interface – how to get access (or even find ) data that may not be with in my own sandbox (semantic web type API