We all know the Internet of Things is a network of interconnected devices and systems that gather and share data for making better-informed decisions. Your business generates a large amount of data useful for its prospects in the long run.
Haven’t you thought about making good use of this data to induce exponential growth in your business? If you still need to, then take the first big step now! Indeed, IoT services include IoT data analytics which helps collect, analyze and process data generated by these systems.
For that reason, the process utilizes several tools to gather precise information from large amounts of data. We shall look through its various aspects that cover;
i) Introduction to IoT data analytics,
ii) The type of data analytics
iii) Advantages of IoT analytics in business
iv) Devices powered by IoT analytics and many more!
Read on and find what makes things tick in IoT data analytics. We shall explain the basics and go deep into the subject. So let’s start with the introduction to IoT data analytics that forms part of IoT services. Here we go!
IoT analytics refers to finding trends and patterns from the large amounts of data generated by connected devices and systems. Hence, it is designed to keep tabs on the trends, recognize deviations, and give business insights from the analyzed IoT data.
IoT analytics involves the application of data analysis tools. Therefore, the purpose of IoT analytics is to improve business processes by optimizing the connected device’s performance by analyzing and resolving issues in the early phase.
Comprehensive IoT services focus on providing IoT analytics solutions, which is very useful for industrial automation. As a result, it provides valuable insights into business operations and helps with decision-making based on data analysis.
Generally, there are four types of IoT data analytics. Let’s consider these three common types of IoT data analytics and select a platform that offers the best analytics. Shall we?
The four most important types of IoT analytics include:
i) Predictive analytics
ii) Descriptive analytics
iii) Prescriptive analytics
iv) Real-time analytics
Do you know predictive analytics in IoT services and data analytics use real-time and past data collected from IoT systems and devices? Well, it is used for predicting future results.
To be precise, it involves the application of several statistical methods, such as linear regression, to analyze trends and patterns to forecast future behaviours.
Predictive analytics uses machine learning (ML) capabilities to identify the possibilities of a future event. ML models use large amounts of past data to identify patterns and trends.
Accordingly, it makes use of real-time data gathered from IoT devices to predict outcomes. Indeed, such insights from data help organizations act before issues surface and prevent undesired outcomes.
Descriptive analytics in IoT services make use of real-time data from connected devices. It keeps tabs on the device’s performance and checks whether it runs at the expected level. Furthermore, descriptive analytics identifies any deviations from standards.
It helps businesses to understand operations and identify areas where they can improve and monitor performance based on past data. Also, it facilitates organizations to derive great insights from their data, helping them to implement advanced analytical processes.
Among the IoT analytics, prescriptive analytics comes with the most advanced capabilities.
It gives an additional understanding of the actions that influence the results of predictive and descriptive analytics. Also, it enables enterprises to avoid failures and enhance productivity.
Diagnostic Analytics, another important component in IoT services and solutions, helps understand the reasons why a particular outcome happens.
It enables enterprises to identify poor performance. Suppose a device does not perform as expected; diagnostic analytics serves to find the issues. It helps answer the question, ‘Why did a particular outcome happen’.
Here are a few great benefits businesses derive from IoT analytics, an important part of IoT services and solutions. Let’s list them down.
Several IoT devices collect data, and a few of them include:
Smart homes improve security where customers can remotely access and control equipment and devices. For example, suppose they are away for a vacation; they can turn on and switch off lights, fans and other devices in their home with digital help.
Further, they can integrate several devices into their house. Furthermore, the data collected from these devices helps investigate the devices’ consumption patterns, performance efficiency and so on.
With the help of connected devices, it is easy to track fitness habits. Smart fitness watches have gone beyond the concept of tracking activities. Hence, fitness companies track the data collected for creating personalized workout packages for customers.
For example, based on your needs and goals, fitness companies recommend a workout routine, create a nutrition meal chart and help you achieve the desired results. Therefore, wearables help maintain health and include heart rate monitoring and sleep-tracking capabilities.
Digital assistants are a form of IoT device that uses data analytics. Assists in our everyday lives, digital assistants help us in all realms of activities.
Above all, data collected from these devices helps companies offer customized services based on customers’ preferences and interests.
What are the best practices that need to be followed for adopting IoT data analytics in an organization? Do you need to consider anything particular to implement IoT analytics? Let’s see how an organization should capitalize on IoT analytics, which forms a major component in IoT services.
There are several ways to implement IoT data analytics in an organization. As a result, it gets prioritised based on the type of industry and the specific requirements of a business. However, there are some best practices businesses can follow, irrespective of their industry.
Businesses should ensure IoT analytics can seamlessly integrate with IoT platforms and business stacks to load, merge and handle several data types. Additionally, It should help with customization to enable employees to access data easily.
It should feature data management and governance to share analytics effortlessly. Also, IoT analytics must provide deployment for a wide range of environments and should be compatible with hybrid platforms as well.
What is data analytics for the Internet of Things?
IoT data analytics, also called IoT analytics, involves evaluating data gathered from IoT devices with the help of data analytics tools.
What are the different types of IoT analytics?
The four types of IoT analytics include predictive, descriptive, prescriptive, and diagnostic analytics.
What are the three major components of the Internet of Things?
The Internet of Things uses several components to ensure its proper functioning. Hence, its three key components include devices, networks and sensors.
Leverage ThinkPalm’s IoT services to take full advantage of data analytics in IoT. Also, ensure that you lead from the front with the expertise of ThinkPalm to beat the tough competition, no matter the type of industry.
Our expertise in advanced IoT services and solutions enables your business to create better value by helping you understand the implications of data. Also, we help you test the capabilities of IoT analytics platforms and their integration abilities with third-party analytics solutions. Furthermore, it helps organizations test their expertise in using platform tools.