Lessons for supporting data science from the everyday automation experience of spell-checkers

We apply two theoretical frameworks to analyze spell-checkers as a form of automation and apply the lessons learned to analyze opportunities to support data science. The analysis distinguishes between automation of analysis to suggest actions and automation of implementation of actions. Having the automation work in the same space as users (e.g., editing the same document) supports stigmergic coordination between the two, but attention is needed to ensure that the contributions can be combined and have a recognizable form that indicates their purpose.