Introducing the Data Maturity Framework
In our four years of running the Data Science for Social Good Fellowship and the Center for Data Science and Public Policy, we have talked to hundreds of organizations about their data, technology, missions, culture, pain points, and impact.
For the projects we take on during DSSG , we look for certain common features. But we also spend a lot of time talking with many more organizations about their data, their organizational culture, and their ability to act on any insights coming out of our projects. In doing so, we often find ourselves asking the same questions about problem definition, data and technology readiness, and organizational readiness.
Based on those conversations and our work with dozens of organizations, we’re publishing Data Maturity Framework. This tool is a questionnaire that will help people at non-profits, government agencies, and other groups take the most important first steps towards a successful data-driven social impact project.
Not all organizations will be doing projects that require robust machine learning and predictive analytics. Not all organizations need or want that kind of a project, and many more organizations don’t have the organizational buy-in or the data in place to produce meaningful operational changes and interventions. For each type of problem that can be solved, there is a different level of maturity required.
This framework will help governments and non-profits evaluate themselves on where they are in their data maturity and what they need to do to move forward. We believe this tool will be useful both in thinking about potential partnerships and projects with DSSG and DSaPP, as well as within the larger data-driven social impact space.
We would also like to collect enough data to conduct a benchmarking survey and release aggregate results about the types of projects organizations are interested in doing, and the data/technology strengths and weaknesses across the sector. Our hope is that this tool and report will help bridge the gap between the people with important problems to solve and the people who can help solve them with data and analytics.