Abstract: A data analytics for recovery, using granular and large-scale failure data from the distribution grid. A key characteristic of the data analytics is its generalizability. The data analysis applies to a large number (169) of failure events rather than one disruption. Further, a data driven recovery scaling law characterizes how recovery speed scales with respect to the severity of weather-induced failures from moderate to extreme. The data analysis also demonstrates the promise of mitigating fundamental limitations of typical recovery through smart grid infrastructure. The data analytics generalizes from one service region in New York to another in Massachusetts. As data used are commonly available to most distribution system operators, the analytics is potentially applicable across the US and parts of the world.