Page 210 - Big Data Analytics for Connected Vehicles and Smart Cities

190 Big Data Analytics for Connected Vehicles and Smart Cities Building a Data Lake 191

Table 9.1
How the Approach Addresses the Challenges
Data Lake Challenges How the Approach Addresses the Challenges
Lack of clear strategy The approach allows the development of a clear strategy that
can be tested and practiced using a pilot. This strategy will then
be revised and enhanced in the light of lessons learned and
information received during the pilot.
Existing data scattered and The adoption of an incremental approach allows early results
not well understood while providing the time necessary to bring scattered data
together. Early results will also act as a communication tool to
motivate staff to identify additional data and help to bring it
together.
Difficulty in turning data into The delivery of early results illustrates the full process from data
action collection to creation of a data lake, demonstration of ability to
turn data into action, and the development of real-life strategies to
serve as models for future analytics.
Lack of big data skills The approach enables both public- and private-sector resources to
be combined and transitions to be created from initial project to
full-scale project.
Insufficient governance and Data governance and security arrangements are tested during the
security pilot project.
After initial establishment, Data quality control measures can also be tested and developed
degradation over time without during the pilot project, forming a practical platform for preventing
data quality control degradation of operation over time.
Lack of self-service The ability to share data and analytics in pilot use cases enables
capabilities and long the establishment of self-service capabilities and shortened
development times development times using agile development approaches.
Lack of features to motivate Early delivery of practical results provides the tools necessary to
and enable smart city and motivate smart city and transportation exponents on the use of the
transportation exponents data lake. This also stimulates exponents to consider their own
use of the data and what further analytics would be required. This
has the added benefit of enabling results-driven data collection
and acquisition.

ments. This is unlikely to provide the best return on investment for the creation
of a smart city data lake. In order to realize the new potential and possibilities
offered by the data lake, it is necessary to implement organizational change
and to build awareness among end users regarding the potential. One possible
way to address this challenge is the development of an organizational plan as
part of a pilot project. The transportation data analytics that emanate from the
pilot project can also be used to support pilot arrangements for fine-tuning the
organization. This may also require a cultural change that focuses on the orga-
nization’s ability to adopt innovative techniques rather than following existing
processes and procedures. In this respect, data analytics can be used as a bridge
from the data lake to job functions for end user staff. I have grappled with the
issue of how to develop organizational arrangements that would be the best fit
for both technological and commercial layers of the architecture for some time.