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180 Big Data Analytics for Connected Vehicles and Smart Cities Building a Data Lake 181

Data and Analytics Exchange

This element of the data lake supports both data exchange and analytic sharing.
Experience shows that providing multiple users with data is not enough to mo-
tivate and enlighten them regarding the use of the data for their own purposes.
It is also important to share analytics for two major reasons. In the first case,
analytics may be directly applicable to the user’s job function. In the second
case, the analytics can be used as a communication tool or model to illustrate
how analytics could be applied to the user’s job function.
The ingestion, preparation, discovery, and exchange of data is supported
within the data lake environment that includes both hardware and software.
In addition to supporting these essential functions, the data lake environment
can also support the appropriate security arrangements and the definition of
metadata, data lineage and other master data operations required to maintain
a single version of the truth and catalog the data within the data lake. The data
lake technology environment would also support operations such as the admin-
istration required to keep the data lake running.

Delivery of Insight and Understanding to Smart City Practitioners
The analytics that result from the discovery process are presented to smart city
practitioners to form the basis for response strategies in the light of the insight
and understanding. The whole data lake configuration is designed to support
a strong connection between data and such insight and understanding. To har-
ness the full value of the analytics it is necessary to develop actionable work
items and strategies that represent a response to new information regarding
prevailing transportation conditions and the quality of transportation service
delivery in the smart city.
Support for Smart City Services
Ultimately the strategies derived from new insights and understandings will
provide support for the 16 smart city services defined in Chapter 5. For exam-
ple, with respect to the smart grid, roadway electrification, and electric vehicle,
the output from the data lake could provide insight into optimum placement
for the electric vehicle charging points. It could also provide insight into en-
ergy requirement changes that would result from large-scale adoption of electric
vehicles in the smart city. With respect to the integrated electronic payment
service, the data lake could provide insight into the optimum fee or ticketing
structure to maximize user experience, minimize operating costs, and ensure
that revenue is as predicted.
It is expected that support for smart city services would cover the spec-
trum of transportation activities from planning, design, and operations through
maintenance. The support would address both public- and private-sector needs.