Article by Justin Piff: “One of the five conditions of collective impact, “shared measurement systems,” calls upon initiatives to identify and share key metrics of success that align partners toward a common vision. While the premise that data should guide shared decision-making is not unique to collective impact, its articulation 10 years ago as a necessary condition for collective impact catalyzed a focus on data use across the social sector. In the original article on collective impact in Stanford Social Innovation Review, the authors describe the benefits of using consistent metrics to identify patterns, make comparisons, promote learning, and hold actors accountable for success. While this vision for data collection remains relevant today, the field has developed a more nuanced understanding of how to make it a reality….
Here are four lessons from our work to help collective impact initiatives and their funders use data more effectively for social change.
1. Prioritize the Learning, Not the Data System
Those of us who are “data people” have espoused the benefits of shared data systems and common metrics too many times to recount. But a shared measurement system is only a means to an end, not an end in itself. Too often, new collective impact initiatives focus on creating the mythical, all-knowing data system—spending weeks, months, and even years researching or developing the perfect software that captures, aggregates, and computes data from multiple sectors. They let the perfect become the enemy of the good, as the pursuit of perfect data and technical precision inhibits meaningful action. And communities pay the price.
Using data to solve complex social problems requires more than a technical solution. Many communities in the US have more data than they know what to do with, yet they rarely spend time thinking about the data they actually need. Before building a data system, partners must focus on how they hope to use data in their work and identify the sources and types of data that can help them achieve their goals. Once those data are identified and collected, partners, residents, students, and others can work together to develop a shared understanding of what the data mean and move forward. In Connecticut, the Hartford Data Collaborative helps community agencies and leaders do just this. For example, it has matched programmatic data against Hartford Public Schools data and National Student Clearinghouse data to get a clear picture of postsecondary enrollment patterns across the community. The data also capture services provided to residents across multiple agencies and can be disaggregated by gender, race, and ethnicity to identify and address service gaps….(More)”.