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184 Big Data Analytics for Connected Vehicles and Smart Cities Building a Data Lake 185

analytics; it can also form a stimulus for innovation on a wider front within
a smart city. Deeper insights into and understanding of travel behavior and
transportation service delivery can provide the raw material on which to design
innovative strategies and services for both the public and private sector.

Providing Support for Smart City Service Delivery
Finally, the establishment of a data lake will provide support for smart city ser-
vice delivery. By enabling the management and measurement of smart city ser-
vice delivery, the data lake provides better management possibilities and richer
opportunities for smart city service delivery improvement.

9.7 Challenges

Based on prior experience, there are many challenges that must be addressed
when building a data lake. These can be summarized as follows:

• Lack of clear strategy;
• Existing data scattered and not well understood;
• Difficulty in turning data into action;
• Lack of big data skills;
• Insufficient governance and security;
• Degradation of the data over time without data quality control;
• Lack of self-service capabilities and long development times;
• Lack of features to motivate and enable smart city and transportation
exponents.

Each of these challenges is explained in the following sections.

Lack of Clear Strategy

Like many information technology tools, the hardware and software environ-
ment required to create a data lake can be acquired with little thought to the
overall strategy or endgame. While this enables rapid progress, if the direction
and the ultimate destination are not known or ambiguous, it is highly possible
that the entire initiative will end up in a dead end. It is necessary to have a
clear strategy that incorporates the goals of the exercise and clearly articulates
the value and benefits. From a smart city and transportation perspective, it is
important to define the safety, efficiency, and user experience objectives and to