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14 Big Data Analytics for Connected Vehicles and Smart Cities Questions to Be Addressed 15

a future vision along the process that converts data to information, insights,
and actionable strategies, then perhaps it would begin to recognize data as a
raw material that could transform transportation in smart cities. Many believe
that the main issue is the inability to store large volumes of data because of cost
constraints. Fortunately, our new perception of the value of data coincides with
new abilities to store huge volumes of data at relatively low cost. This issue is
addressed in Chapter 11, which covers cost and benefits estimates.
Advances in data science enable the storage and manipulation of data in
ways that were not thought possible in the relatively near-term past. Thanks to
Hadoop, Google, and Amazon, some important new abilities to store and ma-
nipulate data are converging with the new perception of the value of data. There
are also new possibilities for structuring and restructuring data to optimize its
retrieval and management.
For example, a new concept known as a data lake allows data from numer-
ous sources to be merged into a single repository. Data lakes—essentially cen-
tralized data repositories that can ingest data from multiple sources and make
it accessible across an organization or enterprise—can contain a wide variety of
structured and unstructured data. In data lakes, the data is clean, contiguous,
and easily accessible—unlike the contents of data swamps. Thus, data lakes
enable an enterprise-wide view of data. Just like magnificent brick buildings
wrought from humble clay, the data lakes and integrated data exchanges of the
future can be constructed from data as their raw material. Chapter 10 further
details data lakes.

2.4 Questions Instead of Answers

This chapter addresses a critical ingredient of the book. To engage the reader
and explain why it’s worthwhile to understand big data and data analytics, it is
important to define and frame the questions that can be addressed by such tools
and techniques. The chapter also provides a high-level framework that paves the
way for the discussion of use cases in Chapter 9. The questions to be addressed
encapsulate the needs, issues, problems, and objectives that transportation pro-
fessionals encounter. Identifying and defining the questions to be addressed is
also a first important step in a results-driven approach to the use of big data and
data analytics. An understanding of these questions will form the basis for the
definition of use cases and objectives for the analysis work.

2.5 Overview of the Questions

The questions contained in this chapter are not designed to be completely ex-
haustive but rather, to show some examples of the kinds of questions that can be