Jun 19 2015 Big data in collective impact efforts: lessons from The School Zone
Big data, recently defined as “data sets that are granular enough to facilitate program decisions, broad enough to address complex issues, and comprehensive enough to enable an accurate estimate of social impact,” presents opportunities to gain a deep understanding of community-level and individual trends when collected and used thoughtfully. Acquiring and using big data has become an aspirational goal for many collaborative projects, particularly those that hope to positively impact low-income communities.
In a recent SSIR article, Sacha Litman, Managing Director at Measuring Success, explores how collective impact efforts are using big data to reliably measure the changes they catalyze in their communities and the impact they have on the individuals they serve. He offers an insider’s perspective on The School Zone (TSZ), a cradle-to-career initiative and partner of Measuring Success that serves low-income families in West Dallas, Texas.
TSZ focuses on breaking cycles of intergenerational poverty by providing children from low-income families with resources, tools, and support to succeed academically from birth until the age of 18. Launched in 2011, it is an intersector collaboration that includes 29 non-profit organizations, 16 public, private, and charter schools, the Dallas Independent School District, and two universities. The initiative offers a wide array of supportive programming such as in-school and out-of-school enrichment programs, substance abuse intervention, housing and food assistance, and medical care.
As part of their collective impact efforts, partners sought to understand the effect of comprehensive supportive programming on student achievement. The Center on Research and Evaluation (CORE) at Southern Methodist University (SMU) partnered with Measuring Success to develop a system to share student data across entities. The goal is to collect and aggregate student achievement data and participation rates in supportive programming to longitudinally track a student’s academic and socio-emotional development, contextualizing data in the rates of poverty of a student’s community.
Although the initiative is still in its early stages, it has finalized data-sharing agreements and created a “data cohort” that includes 11 of the 29 non-profit organizations. The data cohort aims to develop a common language around indicators and information collected from and about students and their families. This ensures that the data is transferable and promotes a unity of purpose among organizations that may be working with the same student or family in different capacities. In the context of this initiative, Litman provides these lessons on data collection for collective impact projects:
- Treat data use as a journey, not a destination.
It takes time for organizations with no prior experience working together to arrive at a common understanding of what data should be collected and to develop the appropriate legal and software infrastructure to collect and store data.
- Handle privacy issues forthrightly and constructively.
All partners need to be aware of the risks and benefits involved in collecting sensitive information from vulnerable populations. Organizations can also consider sharing proprietary information when their work can be amplified through collaborative efforts.
- Allow for database autonomy.
Some organizations depend on their platforms for operations and management, in addition to data collection and storage. These systems might work well for them, and partners may experience push back when requesting to change an organization’s long-standing system. Instead, partners should consider ways to export the same type of data from different systems.
- Build your data system iteratively.
Collectively building upon a data sharing platform prototype facilitates partner engagement with questions on data collection and use. This expands ownership of the system and might ensure a faster program staff take up rate with the agreed upon system.