2000115-Strengthening-Communities-with-Neighborhood-Data

28 Strengthening Communities with Neighborhood Data
through work on broader topics like economic development or education
policy.
The partners’ adoption of additional methods to strengthen distressed
neighborhoods depends on their core mission. Some organizations, such
as the Greater New Orleans Community Data Center, commit to supporting
inclusive communities, but function as explicitly neutral providers
of data and analysis. Other advocacy and organizing groups then use
the data to promote equitable policies and mobilize disadvantaged communities.
In contrast, some partner organizations are vocal advocates for
social justice. For example, the Urban Strategies Council website states
that “all policies, practices, and programs must be considered through an
‘equity lens’ where eliminating disparities is a primary concern” (Urban
Strategies Council n.d.). Their “equity model” includes data collection
to support equity, as well as the responsibility of defining and communicating
equity and ensuring an initiative’s leadership is representative of
community. Wherever NNIP partners fall on the spectrum of activism,
they all must be sensitive to their relationships with government agencies
as providers and users of the data.
Funding and Sustainability
NNIP partners are varied in scale. A 2009 survey of 24 NNIP partner
organizations showed an average annual budget for data and information
services of $335,000, ranging from $75,000 to $1.1 million. 11 The total
was below $150,000 for 38 percent of the partners and above $500,000
for 29 percent. They had on average 4.1 full-time equivalent employees. 12
All partners received some general-support funding. Local philanthropies
and the broader institutions of which NNIP organizations are
a part provided most of the funding of that type (e.g., support provided
by a university to its own community research institute). However, all the
partners are also funded in part by fees received for conducting studies
or performing other data-related services for various clients. Such fees
accounted for 58 percent of total revenues on average, but there was considerable
variation in that amount. Review of the information on scale
and funding composition revealed no systematic differences between
university-based intermediaries and those housed in other types of institutions.
There was considerable variation within both groups.
There is a sizable fixed-cost component in expenses for basic data
intermediary functions (assembling and cleaning data from a number