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Solving Data Overload in Smart City Systems
Learn how smart cities can tackle data overload challenges with strategic data management strategies and innovative data storage solutions.
As smart cities deploy Internet of Things (IoT) technology across their urban infrastructure, the data generated by these devices will grow exponentially. Without the right data storage strategy in place, this rapid influx of information puts those cities at risk of suffering data overload.
Avoiding data overload isn’t as simple as expanding your city’s storage capacity to keep up with the rate of data collection. Storage systems must be designed to capture and store smart city data, and enable easy management, retrieval, and analysis of this information.
Read on to learn more about the challenges of data overload, along with valuable insights on how to enable better data management on a smart city scale.
Data overload occurs when your city’s network of IoT devices generate data at a scale and speed the existing infrastructure can’t support. This often occurs when IoT endpoint devices are deployed in a city without upgrading the underlying storage and data management infrastructure.
The system stress—which leads to information overload—can often be quantified across three key factors: volume, variety, and velocity of data.
The volume of data generated is determined by the number of IoT devices operating in your city. As your network of devices grows, so will the volume of data that will require management and storage. There’s no such thing as too much data—unless you don’t have anywhere to put it.
Different types of data require different storage and management needs. Text-based data, static images, video, and audio all come from various sources and may require unique data management needs.
The rate at which data must be processed, stored, and analyzed can place stress on your data storage infrastructure. When data is being used for real-time monitoring and analysis, this velocity can be even higher.
Effective data management is a city’s best defense against data overload and the disruptions it brings. As you plan out IoT adoption across your city infrastructure, investments also need to be made in upgrades for your data management and data storage systems.
Here are some ways to enhance your data management practices:
Strategic storage across multiple locations can improve the efficiency and speed with which data is stored and retrieved. Enhanced network connectivity and upgraded storage hardware and software can accelerate your ability to process and analyze data in real-time.
Data management on a smart city scale requires a comprehensive data governance framework to harmonize your system data and ensure the integrity and utility of that information.
A strong data governance program implements data standards and policies to define data ownership, reduce data silos, establish data security expectations, and maintain compliance with all relevant data regulations.
Cloud-based resource management tools can use analytics to enhance resource allocation for your smart city systems, improving performance and operational efficiencies while controlling data management costs.
Your resource allocation procedures can be further improved with the help of data created by your IoT infrastructure. This will offer insights on how best to distribute resources for the data management systems and overall smart city operations.
Smart cities and commercial companies will be regular targets of cyber-attacks and attempted data theft due to the volume of data stored in their systems. The risk of information overload can create new, complex challenges for IT security teams. Data management procedures should be backed by a stronger security architecture to prevent this.
Data encryption, secure data transmission, the utilization of hot and cold storage, pseudonymization, access controls, and real-time network monitoring and threat detection are all necessary components of a smart city data security front.
As cities onboard new IoT endpoint devices, your existing infrastructure might not be equipped with the capabilities and features needed to support this transformation.
Modern technologies—ranging from upgraded hardware to edge computing to blockchain-backed security—may be necessary to minimize the risk of data overload as your network evolves.
As cities upgrade their data storage and management infrastructure, they’re likely to face several potential stumbling blocks that should be addressed to minimize the data overload risk and preserve the integrity and functionality of your IoT network.
These challenges include the following:
The bandwidth demands of a citywide network of IoT devices might surpass the bandwidth your city needed in the past.
Fiber-optic and/or 5G networks may be an essential infrastructure upgrade. Traffic distribution strategies such as load balancing and setting up content delivery networks (CDNs) may also be effective options for improving bandwidth limits.
With thousands of potential IoT endpoints operating across your city—not to mention massive amounts of citizen data stored in your systems—data security must remain top-of-mind when addressing data overload.
Cities should invest in hardware and software solutions that improve data security. Hardware-based encryption, key management, and cold storage are just a few ways to bolster security for a citywide network.
The lack of data standards reduces the value of saved data. By processing raw data stored in your system, you can implement standards for all data storage locations, making information easier to locate and use.
With so many types of information coming from so many different data sources—and likely being stored across multiple locations—integrating and organizing this data is often a complex process for any smart city.
Effective data integration requires an intensive approach to developing a strategy and implementing best-fit tools to process and manage the data. The integration must be achieved while enforcing data governance and making sure data management practices are compliant with all relevant regulations.
The threat of data overload will only increase over time as smart cities deploy IoT endpoints and increase their volume, velocity, and variety of generated data.
But cutting-edge technology and IT infrastructure design can help companies and cities of any size establish guardrails to reduce the risk of data overload. Here are five strategies to consider:
Not all smart city data is created equal. Certain information is particularly sensitive and/or critical to city services and operations, while others are subject to specific compliance and privacy regulations.
Your data management practices should identify the most critical datasets and prioritize them with the best resources and security possible. During a disaster recovery and response event, this critical data should also be restored first to maintain system continuity.
Edge computing can help process and filter data faster, allowing your city to immediately generate value from data-driven insights.
Faster filtering also helps you manage data at scale by organizing it, routing it to the appropriate storage locations, and optimizing resource utilization to reduce the risk of data overload.
Compressed data— lossy, lossless, or a combination of the two—reduces your storage space requirements and makes it easier to move data across your network. This can alleviate storage and bandwidth requirements, upgrading performance and consistency across your entire network.
Data overload and its related cost overruns can be overwhelming for smart cities. Cloud-based storage offers flexible, scalable, and cost-efficient space to help manage ballooning data without impulsively deleting this information due to capacity constraints.
Processing data on a city scale—and within a single infrastructure—can be extremely difficult without the right tools. Edge computing enables faster processing close to the point of creation, cutting out cloud-based processing to reduce latency.
Stream processing is an edge computing solution that leverages a streaming analytics engine to help process and interpret data from events in real-time—a critical solution for cities using data for live monitoring and response services.
Better data management helps smart cities avoid the fallout created by data overload. It also offers the framework you need to put your captured data to good use—regardless of whether that value is realized immediately or in the future.
Better data management improves data security, controls your IT costs, and makes your data readily available for analysis by human experts as well as machine learning and artificial intelligence tools. The use of both hot and cold storage gives smart cities an ideal blend of storage options to optimize data management based on cost, data availability, security, and performance.
Seagate Exos® CORVAULT™ data storage solutions are designed for enterprise scale and provide robust storage capabilities and performance for on-premises, cold data storage. And Lyve™ Cloud offers multi-cloud flexibility to implement public, private, and hybrid environments based on your storage and data access needs.
The combination of hot and cold, on-premises and cloud storage also enables smart cities to implement a recommended 3-2-1 backup strategy regardless of the scale of your current or future storage needs.
Find out how Seagate’s innovative data storage systems can be configured to meet your city’s specific data management needs and goals. Talk to a Seagate expert today to see how Seagate storage solutions can help you store your smart city data.
