2000115-Strengthening-Communities-with-Neighborhood-Data

Progress in Data and Technology 127
for overcoming the barriers that remain. The following strategies for
improvements in data, technology, and skills might seem obvious, but
they deserve to be evaluated as potential opportunities for intervention.
Invest in Data
Increase the Availability of Raw Data
Despite the rapidly growing volume of open data published by government
and nongovernmental sources, many more datasets remain siloed.
But siloed information is not the only potential lode to be mined if we
wish to increase the data supply. Great volumes of data could be generated
by the simple process of aggregating raw data geographically (e.g.,
calculating sum and mean for the value of foreclosed properties at the
city, county, and school district levels), chronologically (e.g., calculating
mean values for each month, quarter, and year), or topically (e.g., combining
small categories into broader ones). These aggregated datasets
would still constitute a kind of raw data in that they would be numbers
rather than maps, charts, or other retail products.
Nongovernmental data, too, might make a valuable contribution to
the raw data supply. For example, researchers and advocates would love
to have access to customer usage data from the broadband providers
and energy distributors noted above, but currently they can only make
educated guesses.
Increasing the already-massive volume of open data might seem like
overkill, unless you are a stakeholder searching for granular data relevant
to an issue that is important to you. This dynamic—that is, atomized
needs for specialized data—continuously puts pressure on governments
and other providers to release everything, all the time, in formats disaggregated
geographically, chronologically, and topically. The cost of
continuous data updates can be quite high, and the provider runs a significant
risk of consumer backlash when expectations are not met for
issues such as accuracy, formatting, frequency of update, and too much
or too little protection of personally identifiable information.
Increase Data Quality
Data quality encompasses everything from address cleaning to granularity
to how the data are structured. But perhaps the most important
aspect of data quality is the inclusion of elements that link each row of