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Digital image analysis is at a crossroads. While the technology has made great strides over the past few decades, there is an urgent need for image analysis to inform the next wave of large scale tissue biomarker discovery studies in cancer. Drawing parallels from the growth of next generation sequencing, this presentation will consider the case for a common language or standard format for storing and communicating digital image analysis data. In this context, image analysis data comprises more than simply an image with markups and attached key-value pair metrics. The desire to objectively benchmark competing platforms or a push for data to be deposited to public repositories much like genomics data may drive the need for a standard that also encompasses granular, cell-by-cell data.