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Above: PredPol uses big data analytics to assign probabilities to where and when future crimes may occur. Courtesy
PredPol.
themselves are neutral, and the devil is in the
application. When does predictive policing
become cyber-profiling, the digital version of
stop-and-frisk? Will “data profiling” result in
a segregated Web, where what you see on
the Web depends on your age, race, gender
or income? When do an individual’s rights to
control personal data trump the public interest
in the good that can be achieved by pooling
this data?
What This Means for Museums
Data analytics give museums tools that enable
them to hone their business practices and
become more efficient in operations like food
service, sales, pricing, marketing campaigns,
retail, development and exhibit design.
Museums can index attendance data to literacy
rates, household incomes, average number of
children and other community services to yield
an intimately detailed picture of whom they
are serving. Data mining can help museums
understand how weather patterns affect
attendance, or create personalized promotions,
experiences and discounts based on demographics
and past behavior. It has the ability to
transcend “traditional” market research information
(age, household income, etc.) to create
and target demographic and psychographic
profiles, delve deeper into understanding
human behaviors and reach desired audiences.
Even though many powerful data sets (U.S.
Census data, for example) are freely available
in the public domain, harnessing the power of
big data can still be relatively expensive. Will
the competitive advantage conferred by data
analytics widen the gap between museums
that afford such services and those that
cannot? Jacob Harold, CEO of the nonprofit
financial watchdog GuideStar, is encouraging
nonprofits to master “medium data” about
who they are and what they are trying to do as
a manageable first step before tackling the
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