Data Quality: Looking at more fundamental aspects
of big data, there are a number of challenges that
are associated with the quality of the data. Data
captured by different people under special regimes
and stored in distinctive databases is rarely stored in
any standard formats [22]. Relying on crowd
sourcing and collaboration of multiple providers will
result in data that suffers from a lack of structure
and consequently consistency, heterogeneity, and
disparity issues will have a greater chance to occur.
Accordingly, “there is no universal way to retrieve
and transform the data automatically and universally
into a unified data source for useful analysis” [22].
That will cause more challenges like data
uncertainty and trustworthiness. For example,
sensor data collected through a third party without
a centralized control could have been produced by
sensors that are faulty, wrongly calibrated, or beyond their lifetime. The challenge may also extend
to the outputs of analysing existing data (given the
possibility of errors) and reporting the results for
use by others, who may not be aware of such issues.
Therefore, continuously updating data gathering and
usage policies, sharing and discussing them among
all entities in a smart city, ensuring that the citizens
understand and apply the policies correctly is vital
and challenging at the same time