Applications of IoT Real-Time Analytics
IoT power may be unlocked using Real-Time Data Analytics. Discover how Seagate’s storage solutions deliver your business vital data.
The Internet of Things (IoT) expands data collection and use sources. IoT goes beyond big data sets. This data can be decrypted and reviewed faster to help operational and strategic decision-making.
Your company can only benefit from real-time insights if it has the infrastructure to process, store, manage, and retrieve data reliably. These lessons apply to many industries and use scenarios. An overview of IoT’s support for Real-Time Data Analytics and how this data can add new value to your company is provided below.
What Is Real Time Data Analytics
Any information that is utilisable as soon as it is gathered is referred to as real-time data. Real-Time Data Analytics allows for almost instantaneous access to insights derived from acquired data.
With access to the most latest data, your organisation can make more informed decisions. You can be alerted by real-time analytics as soon as new trends, patterns, or events appear.
Real Time Analytics Tools
Why It’s an Important IoT Factor
IoT devices give constant data feedback that is not possible with other sensors, meters, or traditional data collection techniques because they can communicate data over an internet connection. Real-Time Data Analytics is one of the most potent features an IoT architecture can enable. This is because IoT can pull the most recent data from multiple sources at once.
Monitoring
Your current surveillance activities are enhanced by Real-Time Data Analytics, which can be used for crowd management and on-premises security. The following are the most popular uses of IoT-based analytics for surveillance:
Instantaneous Reaction Hub
A centralised real-time response centre is advantageous to first res-ponders, law enforcement, and private security since it enhances their capacity to continuously monitor designated areas.
A real-time reaction centre can assist in coordinating a thorough response, which may involve the deployment of security guards, traffic control techniques, and public service announcements, when circumstances of concern arise.
Identification of Anomalies
A computer vision system that is connected to video cameras may automatically watch each surveillance camera’s live feed, looking for unusual occurrences and behaviours. Using Internet of Things cameras, this clever technology may identify potential problems that human observers might miss.
Quick Reaction
Traffic sensors and other IoT devices can determine the most efficient route for emergency res ponders to take, in addition to video cameras and other IoT surveillance devices that can detect the need for emergency res-ponders.
Verticals for Security
Many organisations consider cybersecurity concerns as significant as physical security risks, if not more so. IoT devices and Real-Time Data Analytics help improve your company’s digital infrastructure security:
Detection of intrusions
Incoming data scanning and real-time monitoring can help your business detect, lessen, and even stop hackers trying to access your network.
Multiple layers can be included in the intrusion detection infrastructure, such as contemporary storage drives with trespassing awareness characteristics included in to provide Real-Time Data Analytics and monitoring.
Real Time Analytics Software
Threat Identification and Reaction: An aberrant behaviour or unusual patterns may indicate a network breach. Real-Time Data Analytics can continuously monitor this data using hardware and software solutions and sound an alarm if necessary.
Additionally, based on trigger actions triggered by signals from your analytics, this response can be automated.
Real Time Analytics Architecture
Cybersecurity and Network Traffic: Boost the security of your whole network architecture. Data centres’ real-time analytics and ongoing network traffic monitoring can help you become more adept at identifying and isolating threats to reduce their access and possible harm. Real-Time Data Analytics can monitor network traffic even in complex, hybrid cloud systems found in your data architecture.
Real Time Analytics Use Cases
Additional Use Cases for Real-Time IoT Analytics
IoT-enabled Real-Time Data Analytics have practically limitless potential applications, and new ones are being created on a regular basis. The following are some more general ways that IoT is facilitating real-time analytics that are beneficial to businesses:
Astute Management
IoT devices are collecting data from big environments, like cities and buildings, to create new operational efficiencies. Among the many ways IoT applications create new real-time insights across smart cities and other smart infrastructures are real-time insights about energy use, utility management, transportation, and safety.
Producing
Equipment that requires repair can be identified using real-time data from IoT sensors. Predictive maintenance, which takes care of impending equipment demands before they become mechanically defective, can frequently be supported by this knowledge.
In order to achieve higher productivity and efficiency, manufacturing companies can also benefit from Real-Time Data Analytics by optimising resource utilisation.
Efficiency in Energy Use
IoT sensors can track energy consumption throughout your company to find new chances for energy-saving measures. Analytics can help find possible cost reductions by pointing out high-energy processes and equipment that can be updated to use less energy, or by moving some high-energy activities to off-peak hours.
Moving
IoT traffic sensors aid in better traffic control, which reduces congestion in urban areas. Additionally, sensors can monitor activity on public transit to detect the need for any specialised assistance or services.
Traffic management strategies that suggest other routes and use road signage to alert drivers of critical transportation information can be informed by real-time traffic data.
Optimisation of the Supply Chain
Shipping fleets and other supply chain infrastructures frequently employ Real-Time Data Analytics to maximise throughput and accomplish more effective operations. IoT data facilitates shipment tracking, delivery schedule estimation, and the identification of supply chain components that are struggling or performing poorly.
Farming
Numerous factors influencing agricultural productivity can be monitored by IoT devices, enabling real-time analytics to track data on irrigation, soil quality, climate, and other factors. This makes it possible for farmers and agricultural enterprises to maximise crop productivity, cut waste, and plan irrigation schedules.
Real Time Analytics Platform
IoT’s Use of Physical Storage
Enterprise data storage methods that are suitable for IoT devices facilitate the best possible Real-Time Data Analytics from this data.
Many organisations will need more data storage as IoT use rises, necessitating significant infrastructure investments. The following are some ways that physical storage may affect your IoT network’s worth:
Ability
Longer task cycles, less administration, and better space and power consumption result from using the largest storage device capacities for the application. This can significantly affect cost containment and growth management at scale.
Active Archiving of Historical Data
There are other types of information that can be useful to your company besides real-time data. In addition to comparing current data outputs to past performance, historical data can be utilised to assess trends over time and unearth additional knowledge not recorded by real-time information. But previous data must be maintained and archived. Physical storage can help effectively provide access to historical data that provides continuous and deeper insights, whether it is on-site or at a faraway data centre.
Flexibility and Scalability
Systems for physical storage can be built to be extremely flexible and expandable to meet your demands as they change over time. A lot of contemporary enterprise drives are made to work seamlessly with comparable gear, so you have a lot of alternatives when it comes to configuring this storage, especially if you want to combine it with a public cloud environment.
Processing Data in Real Time
Physical storage is a great option for edge computing workloads that analyse and organise real-time data since it can provide minimal data transfer distances. Proximity to the point of creation is crucial for optimal Real-Time Data Analytics, and physical data storage provides a localised connection to IoT devices that cloud storage may not always provide.
Dependability and Accessibility
Physical storage located on-site can frequently continue to function even in the event of an internet outage. Software functionality, integrated data protection, and hardware redundancy can all be obtained with local physical storage. Local storage further enhances data security and reliability by supporting failover and backups. It also gives you convenient access to IoT data and the strategic insights it can provide.
Real Time Data Analytics Architecture
Using Seagate, Go from Real-Time Data to Action
With the appropriate storage architecture, transform real-time data into insights that can be put to use. In order to optimise data processing speed, guarantee high availability, and provide your company with sufficient storage for historical as well as real-time data, your company should use contemporary physical storage that is built for enterprise workloads.
To satisfy your specific IoT data storage requirements, Seagate has a large selection of physical storage options. The best value per terabyte is offered by their Exos X, Exos E, and Exos Mozaic 3+ hard drives, which together provide a basis for processing historical and Real-Time Data Analytics.
The versatility your organisation may be looking for in terms of storage solutions is provided by their data storage systems, which include Exos CORVAULT data protected JBODs, Exos X hybrid Exos E extension, Exos AP integrated storage servers, and storage arrays.
Seagate Lyve Mobile solutions can be utilised as storage for effective data production, collecting, and transfer at the edge. Lyve Cloud S3 storage enables you to consolidate and preserve historical data while optimising the return on your IoT investment by allowing you to allocate physical storage space to Real-Time Data Analytics.
