IoT Data Analytics: Benefits, Use Cases, and Implementation
Updated on Jul 13th, 2023
With time, we’re always trying to find new methods to make our lives simple, organized, and connected. And somehow, thanks to technological advancement, we have achieved this in ways we could never have imagined. One of the most significant technological advancements in recent years is the Internet of Things (IoT).
“Internet of Things” was first coined formally in 1999 by Kevin Ashton. Since then, it has come with more than two decades of development before speeding up as becoming an essential component of contemporary daily life, business operations, and other industry sectors. With the ongoing advancement of technology, there is no indication that IoT adoption will slow down any time soon. In fact, according to Statista by 2030, there will be more than 29 billion IoT devices, nearly tripling from the estimated 9.7 billion in 2020.
What does this mean?
Through the interconnection of a massive network of machines, gadgets, and sensors, the Internet of Things (IoT) is transforming our way of living and working. The data produced by these interconnected devices have a huge potential to benefit businesses and organizations by enhancing operations and providing insightful data. However, it can be difficult to extract useful information due to the overwhelming volume and complexity of IoT data.
This is where IoT data analytics comes to play!
And today, with businesses seeking to leverage the power of IoT to gain a competitive edge, IoT development services are increasingly in demand. Let’s see how…
- Internet of Things (IoT) analytics is the application of data science approaches to extract useful information from the enormous amounts of data gathered by IoT devices. Supply chain management, prescriptive analytics, and predictive maintenance are examples of common IoT analytics applications.
- With IoT data analytics, firms can also boost ROI for their IoT projects by gaining useful insights from their data to get more out of their connected equipment.
- To choose the right IoT data analytics platforms, certain things that you should consider are- privacy/security, service type, connectivity, hardware, and integrations.
Table of Contents
What is IoT Data Analytics?
IoT data analytics, or IoT analytics, is the evaluation of data produced and gathered by IoT devices using a particular set of data analytics tools and techniques.
The real purpose of IoT data analytics is to turn vast amounts of unstructured data from various devices and sensors into insightful and useful knowledge. This information can further be used to drive future data analysis and smart business decisions.
Additionally, IoT analytics enables the discovery of patterns in data sets, including both historical and real-time data, which may then be utilized to make predictions and also alter future events.
Further, businesses may better understand how their products and services are being used, spot areas for improvement, and create new products and services to satisfy changing consumer demands by analyzing this data.
Your wearable device’s data may be able to warn you about a potential cardiac issue. Data could be utilized on the production line to streamline industrial procedures and generate savings in the billions of dollars.
Different Types of IoT Data Analytics
IoT data analytics is the most rising topic these days; businesses are utilizing a variety of analytics techniques to glean insights from IoT data. Here are a few of the various types of IoT data analytics that can be divided as per the streams to work upon:
It uses historical data analysis to create a summary of past events. It aids organizations in comprehending the past and seeing trends and patterns that might guide future decisions.
A report that details what happened, when it happened, and how often it happened is produced using the historical data acquired from devices after being processed and analyzed. This kind of IoT analysis can be used to identify any irregularities as well as to provide answers to inquiries regarding the behavior of objects or people.
Diagnostic IoT analytics, in contrast to descriptive analytics, goes further to address the question of why something occurred by digging deeper into the data to pinpoint the underlying cause of a particular problem. To search for hidden patterns and relationships in data that can provide actionable insights into the origins of certain problems, diagnostic analytics makes use of methodologies like data mining and statistical analysis.
Predictive IoT analytics, as the name suggests, uses previous data and trends to forecast upcoming events. Various statistical and machine learning algorithms are employed in this form of analytics to create models that can be used to forecast what will happen in the future. When it comes to inventory management, demand forecasting, and other business choices, this kind of analytics is quite helpful.
By employing analytics and alerts to spot equipment wear and tear or malfunctions early on, predictive maintenance elevates analytics and alerts to a new level. For makers of mission-critical devices used in healthcare, automotive, aerospace, and other industries, this is one of the most common IoT use cases. It enables businesses to minimize physical device inspection, respond quickly to anomalies, and prevent unplanned downtime. Predictive maintenance often entails continuously analyzing sensor data and comparing it to the technical algorithms that define typical equipment operation.
Based on the information gleaned through descriptive, diagnostic, and predictive analytics, this sort of analytics offers actions that firms can take. It aids organizations in optimizing their operations and making data-driven decisions.
The most sophisticated kind of IoT analytics — prescriptive IoT analytics, not only forecasts future events but also makes suggestions for how to proceed to accomplish the intended business objectives. This kind of analytics uses optimization algorithms to determine the optimum course of action to pursue to accomplish a particular objective.
The company’s information, including technical specifications for devices, how-to manuals, Q&As, etc., is combined with IoT data analytics to achieve this. As a result, the system not only recognizes a problem or issue on a remote device but also reports on known solutions, instances of a similar problem in the past, and suggested next steps. By combining the benefits of automated action reports, which ensure a quick and precise response, with predictive maintenance, which detects early symptoms, prescriptive IoT apps can significantly improve your technical assistance.
Further, using our data consulting services, you can progress to a more complex level of autonomous or semi-autonomous troubleshooting from here.
Real-time IoT data analytics refers to the method of gathering, processing, analyzing, and interpreting data from IoT devices in real-time to derive meaningful insights and make informed decisions. IoT devices are capable of collecting and transmitting vast amounts of data, which can be used to gain insights into the performance, behavior, and usage patterns of various devices, systems, and processes.
Real-time IoT data analytics involves the use of advanced analytical tools and algorithms to process and analyze the data generated by IoT devices. This includes machine learning algorithms, statistical models, and other analytical techniques, which are used to identify patterns, trends, and anomalies in the data.
The insights derived from real-time IoT data analytics can be used to optimize and improve various processes, systems, and devices. For example, in manufacturing, real-time data analytics can be used to monitor and optimize production processes, while in healthcare, it can be used to monitor patient health and provide early warnings of potential health issues.
By providing real-time insights into various processes and systems, IoT data analytics can help organizations to optimize operations, reduce costs, and enhance overall efficiency.
Edge analytics analyses data closer to where it is created, at the network’s edge. It benefits organizations by processing data locally rather than transferring it to a centralized server, reducing latency and enhancing response times.
One of the most cutting-edge IoT technologies, edge analytics enables the processing and analysis of IoT data right on the device that collects it. It decreases the burden on cloud servers and increases data processing speed and latency. For real-time and mission-critical applications found in Industrial IoT, automotive, and other applications that deal with enormous amounts of data, edge analytics is particularly valuable.
Further, with the introduction of 5g and higher data transfer rates, edge analytics will allow IoT devices to carry out necessary data processing and decision-making without turning to the cloud. However, depending on the actual user scenario, the majority of such devices, notably IoT gateways, use edge and cloud-based data processing simultaneously.
If you need cloud integration services for your devices, Matellio is just a click away to serve you!
Cognitive IoT data analytics refers to the use of cognitive computing and artificial intelligence (AI) techniques to analyze the majority amount of data generated by IoT devices. The goal of cognitive IoT data analytics is to enable more intelligent decision-making and automation by uncovering patterns, insights, and correlations in the data.
Cognitive IoT data analytics can involve the use of machine learning algorithms, natural language processing, and other advanced analytics techniques to identify trends, anomalies, and predictive insights from IoT data. It can help organizations optimize their operations, enhance customer experiences, and bring innovation by leveraging the insights gained from analyzing IoT data.
In addition, cognitive IoT data analytics can enable proactive and predictive maintenance of IoT devices, lessen downtime, and minimize the need for manual intervention. It can also improve security by identifying and responding to threats in real time.
Companies can combine these various IoT data analytics to extract useful insights from IoT data. The choice of analytics approaches will rely on the business objectives, the type of data, and the environment in which the data is being used. Each type of analytics has its own advantages and disadvantages.
Devices that Power IoT Analytics
IoT analytics involves collecting, processing, and analyzing large volumes of data generated by IoT devices. There are several devices that power IoT analytics to support this process. Here are some examples:
Dedicated trackers like Fitbit and other devices now do more than just count steps. By connecting your gadgets over the Internet, you can keep tabs on your friends’ exercise routines, compete with them, send them messages, and even take their calls. Fitness businesses track this data so they may develop unique membership packages if you sign up. This can involve eating habits, exercise regimens, and other things. The newest smartwatches even track heart rates and rhythms and have successfully identified wearers’ cardiac conditions.
The security systems in smart homes may be accessed and managed while you are away from home, and you can use technology to turn on and off equipment. You may integrate a wide variety of devices into your house, and a wide variety of data can be gathered to evaluate usage patterns, the effectiveness of systems, and other factors.
IoT devices are widely used in healthcare. With the help of Bluetooth technology, hearing aids can be made, as well as devices that monitor pulse-based alarm systems and record blood pressure and heart rate. This has made a significant contribution to improving healthcare. The information gathered is crucial for developing new and improved technology.
IoT devices include digital assistants in many forms. Taking notes, looking up information, playing music, ordering cabs, getting the weather, setting alarms, and doing everything else using Alexa, Siri, and Google. To make these digital helpers more functional, the Internet releases updates frequently. Based on your regular interactions with digital assistants, their data lets businesses customize their services for you.
These are just a few examples of devices that power IoT analytics. The choice of device depends on the specific use case and the requirements of the IoT system. By selecting the right devices, businesses can optimize their IoT analytics processes and gain valuable insights from IoT data.
The Relation Between IoT, Big Data Analytics and IIoT
You’re being reminded of big data analytics when we talk about enormous volumes of data, aren’t you? Do they share a connection of any kind? The Internet of Things is frequently confused with Big Analytics by many people.
Well, the sole difference between both is the Data Source: IoT analytics only gather and analyze data produced by connected IoT devices and sensors, whereas big data analytics deals with data sets from a wide range of streams and sources. Accordingly, we may contend that IoT data analytics is a subdivision of big data analytics that helps with the interpretation of data obtained from connected objects inside the Internet of Things ecosystem.
IoT analytics can be used to handle a variety of problems and difficulties that big data analytics cannot, such as real-time streaming data analysis, near-time processing, edge computing, predictive maintenance, etc. IoT and big data analytics can thus be utilized together to create company value and achieve a competitive advantage.
Further, the IIoT is a word you may be familiar with. Or you can call it Industrial IoT. It is the use of IoT technology in the manufacturing and other sectors of industry with the goal of enhancing operational effectiveness and safety while lowering production costs and downtime. IIoT data analytics, a subset of IoT analytics, is used to process and analyze data produced by IIoT devices and sensors to provide insightful data for scheduling maintenance and managing logistics on manufacturing equipment, weather stations, pipelines, smart meters, delivery trucks, and other types of machines.
The industrial industry is not the only one where the IIoT has applications. The IIoT is booming in many other industries, including healthcare, retail, transportation, energy, and so on.
Some of the Pros of IoT Analytics
IoT data analytics has stepped a long way since its early days to reach the mainstream. It is more likely than ever that business owners would adopt IoT data analytics to make the most of the data assets and strengthen their business decisions if they already accept data analytics due to its observable benefits.
The vast network of IoT devices is bringing new levels of automation, connectivity, and intelligence. The huge amount of real-time data that they produce is transforming many industries, including considerable improvements to service delivery and customer experience. When integrated into data operations, IoT data analytics can bring many advantages.
The benefits of IoT analytics can be divided into two primary groups: business benefits and technological benefits. Let’s explore each of them.
Business Benefits of IoT Analytics
Enhancing Operational Efficiency
Businesses can spot faults and problems that result in inefficiencies by analyzing data produced by IoT devices, and they can then take steps to address such concerns. For example, a company that produces food and beverages can utilize IoT data analytics to monitor the temperature of its coolers in real time and stop food from spoiling because of power outages or broken equipment.
IoT data analytics may assist firms in cost-saving measures, including lowering energy usage, reducing downtime, and maximizing asset utilization. For instance, a manufacturer can utilize IoT data analytics to track the efficiency of its production line and adjust to reduce material waste.
Enhancing the customer experience
IoT data analytics can be used to gather and analyze consumer data to comprehend their wants and preferences. This, in turn, can assist companies in creating better products and services that satisfy their clients’ needs. For instance, a merchant can watch the customer’s activities within the store and then provide them with personalized recommendations based on their preferences using IoT data analytics.
Businesses can discover possible safety dangers and take preventative action to avert them by analyzing data from various sensors. To prevent mishaps, a construction company, for example, can utilize IoT data analytics to track the status of its machines and equipment.
Technical Benefits of IoT Analytics
Real-time Data Analysis
The capacity to analyze real-time data points is one of the key benefits of IoT data analytics. The use of streaming analytics, a sort of analytics that can process data the moment it is generated, makes it conceivable.
With IoT data analytics, you can easily and swiftly scale up your operations without paying additional fees i.e., you need to pay only for the resources you really utilize. And the best thing is, IoT data analytics can be implemented on the cloud so access from anywhere you like.
IoT data analytics can also assist you in analyzing the data with a high level of precision. With its modern analytical approaches, you can collect data from a wide range of sources and then analyze it further using IoT data analytics.
IoT data analytics can also assist you in enhancing the security of your data. Further with which, you can locate and monitor possible hazards before taking preventative measures.
Lastly, the driving value from IoT data is rather simple. There are a number of well-known use cases that you can refer to in order to better monetize your IoT device data.
Use Cases of IoT Analytics
The majority of industries today are using IoT analytics to make their operations smart, enhancing maintenance processes, advancing any other operations, generating energy in the case of an energy company, managing healthcare operations in the case of a hospital, or managing store operations in the case of a retail company. Let’s see some of them in brief:
IoT analytics is used in manufacturing to optimize production processes, monitor equipment performance, and reduce downtime. By analyzing data in real-time, manufacturing companies detect patterns of inefficiency and take corrective action before they lead to downtime or other issues.
For example, data collected from sensors on machines can be analyzed in real-time to identify patterns of inefficiency or potential equipment failures, allowing for proactive maintenance and improved productivity.
For smart cities, IoT analytics is used to analyze data from various sources, like traffic sensors, weather sensors, and social media, to optimize city operations and improve the quality of life for citizens. Leveraging the power of IoT analytics, smart cities make data-driven decisions that lead to more efficient and effective management of resources and services.
For example, data from smart parking sensors can be analyzed to identify available parking spaces in real time, reducing traffic congestion and improving parking management.
Healthcare industries use IoT analytics to monitor patients remotely, optimize hospital operations, and improve patient outcomes. They collect and analyze data from various sources, such as wearable devices and sensors, to gain valuable insights into patient health and make data-driven decisions with IoT analytics.
For example, wearable devices and sensors can collect data on patient vitals, which can be analyzed to detect early warning signs of health issues and provide timely interventions.
Energy and Utilities
For the energy and utilities industry, IoT analytics monitor and optimize energy consumption, detect equipment failures and improve energy efficiency. Smart buildings also use IoT analytics to monitor the usage of heating, ventilation, and air conditioning (HVAC) systems and adjust them based on occupancy and weather conditions.
For example, data from smart meters and sensors on energy infrastructure can be analyzed to identify patterns of energy usage and wastage, allowing for targeted interventions to reduce energy consumption and costs.
IoT analytics is used in agriculture to optimize crop management, monitor soil conditions, and improve irrigation efficiency. Specialists monitor plant health, enabling farmers to detect early signs of disease or other issues and take corrective action before they spread with IoT analytics. This helps to reduce crop losses and improve overall yields.
For example, data from sensors in the fields can be analyzed to provide insights into soil moisture levels, temperature, and nutrient levels, allowing farmers to make data-driven decisions on irrigation, fertilization, and pest management.
Transportation and Logistics
IoT analytics is used in transportation and logistics to optimize supply chain operations, improve fleet management, and enhance transportation efficiency. It is also used to improve fleet maintenance by analyzing data from sensors that monitor vehicle performance. This allows transportation companies to identify potential maintenance issues before they become more serious, reducing downtime and increasing vehicle lifespan.
For example, data from GPS trackers and sensors on vehicles can be analyzed to optimize routes, reduce fuel consumption, and improve delivery schedules.
For retail industries, IoT analytics enhances customer experiences, optimizes inventory management, and personalizes marketing campaigns. It is used to optimize inventory management by providing real-time visibility into inventory levels and stock movements. This enables retailers to make data-driven decisions about when and how much inventory to order, reducing waste and improving efficiency.
For example, data from in-store sensors, RFID tags, and customer interactions can be analyzed to understand customer behaviors and preferences, leading to personalized offers, targeted promotions, and improved inventory management.
These were just a few instances of how IoT analytics is being used across different industries. The potential of IoT analytics is vast, and it continues to evolve as businesses find new ways to leverage the power of IoT data to gain insights, optimize operations, and drive innovation.
How to Efficiently Implement IoT Analytics in an Organization?
IoT data analytics are increasingly in demand as it enters new sectors of the economy. While many businesses are moving towards adopting the IoT, not all of them are aware of how to do so effectively. What is the most efficient strategy for implementing IoT analytics within a company so that it can be done quickly? We’ll walk you through some best practices that can lead to a quick and easy implementation of IoT analytics within an organization to assist you in better understanding the process.
Define the Business Objectives
The first step in implementing IoT analytics to your business is to define the objectives. This involves identifying your business problems that need to be solved or the opportunities to carry off. By having a clear understanding of your business objectives, you will be able to better design an IoT analytics strategy that aligns with your business goals.
Identify the Data Sources
Once you are able to define your business objectives, the next step is to identify the data sources. This involves understanding all the data you have, where it’s located, and how it’s collected. Make sure that all your data is clean, reliable, and consistent to get accurate insights.
Select the Right Analytics Tools
One you identify the data sources; you can then select the right analytics tools for the job. There are various analytics tools available in the market that you can use, such as data visualization tools, machine learning tools, and predictive analytics tools. Choose the tools that fit your business objectives and are easy to integrate into your existing systems.
If you are facing any difficulty in picking the right analytics tool for your business, we provide the best enterprise solutions that could be a perfect fit.
Build the Analytics Infrastructure
With the data sources and analytics tools in place, you can further start building analytics infrastructure. This involves setting up the necessary hardware and software, including data storage and processing systems, cloud infrastructure, and security measures. It’s important to ensure that the infrastructure is scalable and can accommodate the growing volume of IoT data.
Test and Validate
Before deploying the analytics system, test and validate the system to ensure that the things are functioning as intended. This involves running the analytics algorithms on a subset of the data to ensure that the insights are accurate and reliable. It’s also important to ensure that your system is secure and complies with relevant regulations.
Deploy and Monitor
With the analytics system tested and validated, you can then deploy the system and monitor its performance. This involves tracking key performance indicators and making necessary adjustments to the system to ensure that it continues to meet your objectives.
Implementing IoT analytics in an organization requires careful planning, and the above steps can help businesses efficiently implement an effective IoT analytics strategy. Further, hiring a leading custom enterprise software development company like us is the best solution to get yourself free from all those hectic steps and get error-free deliverables on time.
Challenges in Implementation
The many advantages of IoT analytics are pretty obvious, but there are some implementation challenges as well. IoT analytics face several significant issues, including:
Identifying time series and data structures
As part of IoT analytics, sensors frequently receive a flood of static data. Until someone changes it, this data doesn’t change. It is challenging to understand what drives the change over such lengthy periods, and the impact on analysis is frequently uncertain. This may affect efforts at diagnosis and forecasting.
Finding the balance
Companies frequently struggle with finding the correct balance between storing the right amount of data and processing it swiftly. It can be difficult to scale up these two procedures when dealing with time-sensitive data, especially when comparisons need to be made with past data. Because it is necessary to keep historical data for a long period, the cost of storage rises, placing further demand on available funds.
Choosing the appropriate professionals
A business must employ experts from several industries (or us) to execute IoT analytics. Depending on your organization and the type of work it does, you will require developers, database specialists, data scientists, data processing specialists, and other specialized skill sets. And we have it all!
Data are aplenty
Due to the rise in popularity of IoT devices, data is constantly streaming into organizations through personal gadgets, smart home devices, cars, and other sources. Building a solid IoT analysis-based system is essential for businesses wishing to take advantage of this. IoT analytics can release IoT data’s full potential, creating a number of advantages that a business can make use of to outperform the competition.
IoT Data Analytics is the Solution
In conclusion, IoT data analytics is a critical component of any successful IoT development. By using advanced analytics tools and techniques, businesses can obtain valuable insights into their operations, enhance data security, and make data-driven decisions that drive growth and improve customer experiences. As IoT technology continues to evolve, the importance of IoT data analytics will only increase, making it essential for businesses to invest in this area.
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