Data and Democracy

Epilogue: The Future of Political Data Science

Andrew Therriault

The 2016 election cycle will be remembered for many things, but for those who work in politics, it may be best remembered as the year that political data reached maturity. For years, much of the “old guard” of political strategists resisted the growing influence of data and analytics, preferring to stick with a more traditional formula: instinct and experience built up over time. This conflict persisted even as the 2008 and 2012 presidential races showed the advantages data can provide, particularly when one campaign has a distinct technological edge. In 2016, though, it’s clear that this fight is all but over, and the data side has won.

When it comes to technology, political data science is likely to follow a similar trajectory to the one playing out in the broader field of data science. If anything, the political field is particularly well-suited to rapid adoption of new technologies, since the election cycle allows for many organizations to completely overhaul their technology every 2 to 4 years. Though physical hardware is still used by many organizations, the adoption of cloud platforms like Amazon Web Services and Microsoft Azure has quickly become mainstream. Along the same lines, while commercial data analysis software such as Stata and SPSS was once standard, most organizations are now relying mainly on open source tools like Python and R. And as the scale of our datasets grows, traditional relational databases ...