The Internet of Things (IoT) adds value to practically every industry, from manufacturing and logistics to retail and resource management. Drones, delivery vehicles, medical gadgets, security cameras, and construction equipment are among the linked “things” that the Internet of Things collects data from.
While IoT sensors and devices collect a plethora of data, they also generate massive, difficult-to-manage, high-speed data streams that must be managed, analyzed, stored, and protected. IoT data is also perishable, and without the right tools, companies will miss out on the most valuable time-sensitive insights.
This post will look at how real-time data analytics and IoT applications work together to open up new possibilities in various industries.
What does real-time data processing mean for the internet of things (IoT) applications?
Organizations from all sectors struggle to keep up with the vast datasets growing exponentially as IoT use grows. IoT devices and sensors, for example, may gather gigabytes of data in just a few hours—and that’s before you include data from your CRM, social media channels, financial reports, and other sources.
Artificial intelligence, big data analytics, and machine learning are all advancing at a rapid pace at the same time. By using AI to IoT data management and analytics, organizations can quickly extract vital information from these massive, heterogeneous data sets and adjust to real-time conditions. These technologies are coming together to produce game-changing breakthroughs. For example, Big Data’s inherent features make it perfect for fast training AI and machine learning systems.
These intelligent programs can then automate operations, predict equipment failures, and detect security threats in real-time. In fully autonomous solutions, AI assumes control and is guided by a network of connected IoT devices.
By transmitting data as autonomous driving proceeds at all levels, real-time analytics can assist drivers with safety features like as automated braking, parking, and collision avoidance. While there are several examples of what AI, advanced analytics, and the Internet of Things can achieve, they cannot deliver on their promises without the right tools.
Powerful computing is required for real-time insights
The majority of today’s IoT systems were created to link various devices within a network and merge and process data streams from multiple heterogeneous sources. Many of the IoT’s concerns, such as storage, security, and interoperability, are addressed by these platforms, which can also be integrated with data analytics tools to deliver valuable business insights. Real-time data processing isn’t possible with most data analytics systems since they use a cloud computing architecture called Platform as a Service (PaaS).
According to a recent analysis, using cloud-based systems to handle IoT data has various drawbacks, including security issues, latency, and missed opportunities to act on solid and real-time insights. While IoT data streams record what’s going on at the moment, processing them implies transferring them to the cloud for off-line analysis and processing, which can then be evaluated at a later date. You’re also part of a system where you transfer large amounts of data to a faraway place, perhaps exceeding network bandwidth and wasting storage space and processing power on useless data.
While 29% of participating companies have included edge computing in their analytics strategy, 69 percent of respondents felt that prioritizing edge computing for IoT data processing will help them accomplish their core business goals. On the other hand, Edge computing will not be enough to enable real-time data analytics.
The IoT and big data analytics convergence
The combination of IoT, Big Data, and AI-driven analytics opens up a slew of new possibilities for businesses to develop more competitive business models. For digital driving transformation, enterprise strategy is becoming increasingly important. While the research notes that big data enthusiasm has dropped in recent years, advances in AI and machine learning are rekindling interest by presenting new ways to process data and put it to good use.
Simultaneously, increasingly affordable hardware, software, and sensors and evolving standards and best practices accelerate IoT adoption. As a result, the number of linked “things” (including audio, video, and photos) gathering continuous data streams and metrics that monitor machine functioning, ambient conditions, and other factors is fast increasing.
Big data’s importance in the internet of things
Businesses employ Internet of Things (IoT) devices to collect data. Because the data collected by IoT devices is unstructured, Big Data analyses it in real-time and saves it in various storage systems. As a result, there is a compelling need for IoT big data.
The processing of IoT big data is divided into four steps.
- IoT devices produce a lot of unstructured data, subsequently stored in a big data system.
- A distributed database that stores a significant amount of data is a big data system.
- To investigate the data that has been stored, analytic tools such as Hadoop MapReduce or Spark are employed.
- Then, using the data that has been analyzed, create reports.
What are the effects of IoT and big data on each other?
IoT and Big Data are inextricably linked and significantly impact one another. As the IoT expands, so does the demand for big data capabilities. The daily expansion of data volume needs more advanced and unique storage solutions, necessitating the updating of a company’s big data storage infrastructure.
The future of big data and IoT are intertwined. The two areas will undoubtedly co-create new solutions and possibilities with long-term implications.
IoT and Big Data analytics are no longer merely props for future use cases; they’re quickly becoming essential tools for staying competitive. They help companies get more value from IoT sensors and systems by merging IoT data with existing business tools and third-party data sets to add context.
The information can then improve products, services, and customer experiences. Companies must, however, ensure that they have the infrastructure in place to handle real-time data processing at scale to get the most out of their investments.