The 2012 Obama campaign ran the first personalized presidential campaign in history. The data team was made up of people from diverse backgrounds who embraced data science in service of the goal. Civis Analytics emerged from this team and today enables organizations to use the same methods outside politics. Katie Malone shares lessons learned from these experiences for building effective teams.
Working on a political campaign is not that different from working in a consumer-focused organization. The pressure is high, the timelines are tight, and there are often shifting priorities. The data and the modeling need to happen on a national scale. Noisy data is coming in rapidly and needs to be assimilated into existing models and simulations. The best methods to use are often ambiguous at best. The analytics focus is on the actions of the individual. To be effective in this environment, a data scientists must use a myriad of technologies, and these technologies may need to serve different needs from those that the engineering team has to enable high-throughput writes or to help analysts serve dashboards.
This environment heavily informed the technology stack in the early days of Civis Analytics. Katie begins by discussing work Civis did for a national healthcare nonprofit, which, after the passage of the Affordable Care Act, needed to run a national-level campaign to inform people about and, ultimately, influence them to sign up for healthcare, covering everything from data collection to modeling, message testing, and consumer outreach.
Following this work, Civis found that its technical solutions and processes didn’t scale. Katie explores the changes Civis made—particularly for creating effective data-driven teams—that allowed it to continue to deliver the same caliber of work. The company has applied many of these same lessons to bring data-driven decision making to some of the largest organizations in the country. Enabling effective data-driven teams starts with building trust around the process, from the team itself to the decision makers and the IT team that, in the end, controls access to the data, while also building efficiencies in the team and the organization. If you’re a data scientist who wants to understand how to make a larger impact in your organization or a decision maker who wants to know how to elicit sustained value from your data science team, this session is for you.
Katie Malone is director of data science at data science software and services company Civis Analytics, where she leads a team of diverse data scientists who serve as technical and methodological advisors to the Civis consulting team and write the core machine learning and data science software that underpins the Civis Data Science Platform. Previously, she worked at CERN on Higgs boson searches and was the instructor of Udacity’s Introduction to Machine Learning course. Katie hosts Linear Digressions, a weekly podcast on data science and machine learning. She holds a PhD in physics from Stanford.
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