The Importance of Efficiency, Analytics and Scale in Smart Spaces

February 6, 2019 | Posted by

Smart cities have been gaining momentum as technology advances and new partnerships between public and private sectors emerge. As innovative cities move past the pilot phase, smart city projects must consider the factors of management complexity at scale and the need for effective and efficient data processing and analytics.

Unlimited Scalability to Unlock the Benefits of a Connected City

Infrastructure that is not scalable will be useless as smart city capabilities continue to evolve. While modular components are indeed necessary building blocks for smart cities, the amount of data used to power these modular components must be able to scale up as the amount of data produced increases.

For instance, as cities continue weaving together bus routes, ride-sharing apps and gridlock patterns with transportation infrastructure like traffic lights, data usage will soar. Without the ability to scale and connect the data pulled from each of these devices, the full benefits of a connected, smart city cannot be fully manifest themselves.

The Need for Effective and Efficient Data Processing and Analytics

The ability to effectively and efficiently capture, store and analyze ever-growing amounts of IoT data closer to the edge is what really accelerates the benefits of smart cities. Smart cities are only as good as their ability to process data, which requires an intelligent and automated infrastructure that can handle exponential data creation and deliver the capabilities required to support long-term storage, processing and analysis.

For example, prioritizing the most important or most useful data is crucial so that it can be processed and analyzed in real time for the continuous delivery of mission-critical business services. Without the ability to automate how data is prioritized, even connectivity will be irrelevant. Smart city initiatives need to invest in infrastructure with intelligence that can scale as needed, handle the data load and support accurate analytics tools in order to react quickly and responsibly.

Facial recognition is a perfect example of an emerging technology that requires infrastructure that can deliver the highest performance across both storage and analytics. It must store large amounts of video footage but also process that footage, looking for specific markers. In the case of school shootings, for example, this can have a profound impact in helping law enforcement identify the shooter and their location in life-saving seconds.

Next week, we tackle how cities can approach data protection to assuage the public’s privacy concerns. In the meantime, follow me at  Twitter or LinkedIn.

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