We help local organizations and non-profits solve problems and answer questions through data by working on projects around data collection, management, and interpretation.
MassMutual's Data Science team launched our bi-annual "Data Days" events in 2016. During Data Days, we work for four days on new teams across different offices to solve small and big technical challenges and brainstorm new solutions for our company. Data Days brought different people together across Data Science, Data Analytics, Data Engineering, Product Management, and Data Visualization and brought new ways to tackle challenges to the forefront. With the success of Data Days in mind, in 2017 the Data Science team launched the first "Data Days for Good" a multi-day event where our teams of data scientists and technical professionals partnered with local organizations to apply data science principles to social challenges and business questions.
During Data Days for Good (DDfG), team members work together on projects for social good. Data Days for Good projects often include work with nonprofits or community agencies, and may also include submissions to open source projects, or independent analysis of publicly available data addressing social, environmental, or health issues. DDfG is intended to foster team collaboration, cross functional interactions, intellectual curiosity, service, and community building. Take an in depth look at Data Days for Good project here.
It is tough to overstate the value of of data that is clean, automated, and easily retrieved. Achieving this state not only enables quick and accurate data summaries, but facilitates decision-making. MassMutual Data Science's team creates knowledge and builds services from data that enable enterprise wide data-driven decision making, connecting business domains to real-time data feeds and statistically rich reports, allowing for hours saved and insights gained. We bring this same approach to community organizations and local nonprofits seeking to collect, manipulate, and leverage their data and reporting in a reliable and meaningful way.