Data, Big and Small: Notes on Community Data and Community Action

Philanthropy New York Panel

11/16/15

David Micah Greenberg

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Community data and community action, 1854

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A new(er) age of data Google and the flu (Ginsberg et al 2009) 911 data used to develop a new theory of neighborhoods and

violence, contra “broken windows” (O’Brien and Sampson 2015)

“Million dollar blocks” and incarceration (Cadora 2012) IRS data and a re-examination of neighborhood effects

(Chetty, Hendren, and Katz 2015) “Array of things” sensory networks Abundance of proprietary (i.e. ETO, Salesforce) data

systems to capture practice

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A new(er) age of dataGovernment, Foundation, and Community Initiatives White House, Smart Cities Initiative MacArthur Foundation’s New Communities Program New York City’s Change Capital Fund Open data movements nationally

Collective insights: Build data infrastructure among community organizations Align data to meaningful constructs and program goals Be cautious about data biases, interpret contextually

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How to translate into actionable learning?Four challenges: Accessibility (beyond availability) Relevance (conceptual, validity of data) Agency (data reasonably linked to controllable actions) Interpretability (rigorous comparative or counterfactual design)

Case Study: CCF/CEO’s exploration of UI data Take advantage of new NYS law (access) Choose data based on overarching initiative outcome (relevance) Choose data for those directly served (agency) Look down the road for comparison group (interpretability)

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Community data and meaningful action Fundamental equation of data analysis:

Significance level = size of effect x size of study This means that “big” data can detect small effects that may

statistically significant, but may not be truly meaningful What principles can we embrace for “significant” community-level

work around inequality, violence, housing or health? Translating diagnosis into action may mean:

Aligning research infrastructure to deeply resourced community interventions, known to be effective at the individual level

Creating appropriate and frequent assessment feedback loops Building interpretability of data, often by “small” and strategic data

collection efforts