Chemists are paying a renewed interest in chemical reaction data and seek novel ways to extract value from them. The accurate prediction of retrosynthetic routes , the analysis and optimization of existing reaction classes, or the discovery of new ones are just few examples that highlight the impact of reaction analytics and reactivity modeling on chemical science. Such fundamental shift in how scientists consume and use this data creates a demand for innovative ways to shape raw data points, excerpt insightful information, and build actionable knowledge. Moreover, on the one side, the traditional combination of visual inspection and databases searches is made progressively less effective by the increased amount of available reaction data. On the other side, some innovations in data science and machine learning provide more efficient instruments to navigate the available information to support scientists in making data-informed decisions faster.