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Table of contents
1.
Introduction
2.
What is IoT Data Analytics?
3.
Types of IoT Data Analytics
3.1.
Descriptive Analytics
3.2.
Diagnostic Analytics
3.3.
Predictive Analytics
3.4.
Prescriptive Analytics
4.
Devices that Power IoT Analytics
4.1.
Wearable Devices
4.2.
Smart Home Systems
4.3.
Healthcare
4.4.
Voice-Activated Assistants
5.
How IoT Data Analytics Work
6.
Use Cases of IoT Data Analytics
7.
Benefits of IoT Data Analytics
8.
Challenges of IoT Data Analytics
9.
Frequently Asked Questions
9.1.
What skills are required for IoT analytics?
9.2.
What are some common applications of IoT analytics?
9.3.
How can organizations overcome challenges in IoT analytics?
10.
Conclusion
Last Updated: Mar 27, 2024

IoT Data Analytics

Author Shiva
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Master Python: Predicting weather forecasts
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Ashwin Goyal
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Introduction

IoT and Data Analytics are two terms joined together. The Internet of Things is referred to as IoT. A network of physical objects is central to the Internet of Things (IoT) idea. It is often called "things,". These come with software, sensors, etc. In contrast, data analytics is methodically looking over and analysing data collections. This is done to find patterns in their data and derive insightful conclusions from it.

iot data analytics

In this article, you will learn about IoT Data Analytics. 

What is IoT Data Analytics?

IoT data analytics refers to analysing data from IoT devices. This is done by using specific tools and methods. It transforms big unstructured data from diverse devices and sensors within the heterogeneous IoT ecosystem into useful information. This helps in decision-making and further data analysis.

There has been a huge increase in connected devices and sensors in industries. This has made big development in IoT data analytics. Its applications span various sectors. It includes healthcare, retail, eCommerce, manufacturing, transportation, and beyond.

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Types of IoT Data Analytics

IoT analytics includes a variety of analyses kinds that are categorized based on their focus and goal. These are the four main categories:

Descriptive Analytics

The goal of descriptive IoT analytics is to summarise and share previously stored data from IoT devices. It helps businesses figure out what happened in the past by looking at a summary of events, patterns, and trends. Descriptive analytics allows businesses to obtain important insights into their operations. This allows wise decisions based on prior performance by looking at historical data.

Diagnostic Analytics

Diagnostic IoT analytics don't just summarise what happened in the past; they also look for underlying causes and connections between different factors. It means looking at data to find trends, correlations, and oddities and determining why certain things happened. Diagnostic analytics can help organisations determine the root causes of problems, find places to improve, and take the right action.

Predictive Analytics

Predictive IoT analytics uses data from the past and advanced statistical methods to guess what will happen and what the effects will be. By looking at patterns and trends, predictive analytics lets businesses predict likely situations and move before they happen. This kind of analytics helps predict demand, do predictive maintenance, figure out risks, and allocate resources as best as possible. It is based on what needs are expected to come up.

Prescriptive Analytics

Prescriptive IoT analytics go beyond predictive analytics because they tell you how to get the desired results. It combines facts from the past, models that can predict the future, and optimisation algorithms to suggest the best ways to make decisions. Prescriptive analytics helps organisations make smart choices, improve workflows, and boost performance based on what they think will happen.

Devices that Power IoT Analytics

The Internet of Things (IoT) has brought about a major change in how our devices are connected and work together. Various types of devices collect important data like:

Devices that Power IoT Analytics

Wearable Devices

Wearables like fitness trackers and smartwatches do more than count steps. They can track your friends' activities, let you compete with them, send messages, and even answer calls by connecting to the internet. Companies use this data to offer personalised exercise plans, diet tips, and more packages. Some advanced smartwatches can even monitor your heart rate and rhythm, helping to detect potential heart problems.

Smart Home Systems

Devices in smart homes improve convenience and security. Appliances may be managed using voice commands, and security systems can be managed remotely. In order to better understand energy usage trends and increase efficiency, these gadgets collect information about how you use your home.

Healthcare

Numerous IoT devices have a significant impact on healthcare. Examples include hearing aids with Bluetooth capabilities, equipment for measuring blood pressure and pulse rate, and emergency help-calling alarm systems. These devices' data collection aids in the advancement of technology and healthcare.

Voice-Activated Assistants

We can do a lot with the help of digital assistants like Siri, Alexa, and Google. They can set alarms, order transportation, play music, take notes, and search for information. Not only it helps us, but it is also helpful for visually impaired persons to use the device.

How IoT Data Analytics Work

Here’s how IoT Analytics works: 

  • Data Collection: The first step is collecting data from various available sources. 
     
  • Data Processing: Once collected, it should be processed. This is done because not every single bit of data is required further in the process.
     
  • Data Storage: The processed data is then stored. It is stored in time series format. 
     
  • Analysis Techniques: SQL serves as a valuable tool for extracting data from databases, while machine learning techniques enable the identification of patterns within the data and the ability to predict future outcomes.
     
  • Insights and Predictions: Valuable insights and predictions can be derived through the analysis. These insights help understand trends, identify anomalies, and make informed decisions. Predictive analytics based on IoT data can provide valuable foresight, enabling proactive actions and optimizations.
     
  • Application Development: With the obtained insights and predictions, organizations can develop systems and applications tailored to their needs. 

Use Cases of IoT Data Analytics

IoT Data Analytics can be used in many ways for business, such as: 

  • In smart agriculture, IoT analytics can be used to make connected field equipment work based on what they learn from IoT research. The analysis takes into account time, place, weather, altitude, and the local surroundings. For example, watering systems can be tuned to give just the right amount of water based on predictions of rain.
     
  • Real-time inventory management makes it easy for businesses to keep track of their goods and restock them quickly. For example, a company that runs food vending machines and has connected equipment can set up automatic calls to restock when the number of products reaches a certain level.
     
  • IoT analysis is used in predictive maintenance to figure out what repair needs different infrastructures have. To figure out when maintenance needs to be done, you can use templates and models that have already been set up. IoT analytics, for example, can predict when repair is needed in long-distance transport trucks with heating and cooling systems to keep cargo from getting damaged.
     
  • IoT data can measure how well business processes work and help make any changes that are needed. Through data analysis, process bottlenecks in current and future workflows can be found, which increases efficiency.

Benefits of IoT Data Analytics

IoT Analytics brings a wide range of benefits, such as: 

  • Improved Decision-Making: Using the generated data, organisations can make good decisions. Because of this, businesses can make better choices.
     
  • Cost Reduction: IoT analytics-driven automation use can cut costs. By cutting out wasteful spending, process simplicity makes operations run more smoothly.
     
  • New Revenue Streams: By solving operational problems through IoT analytics, organisations can unlock new ways to make money. Businesses can identify innovative ways to monetise their services or products using data insights.
     
  • Enhanced Customer Experience: By analysing customer purchase history and behaviour, Organizations can provide tailored recommendations, personalised offers, and better support, increasing customer satisfaction and loyalty.
     
  • Enhanced Safety and Security: Safety and security can be increased with IoT data. By looking at data from IoT-enabled security systems, organisations can spot problems, find potential risks, and react to security threats on time, ensuring that assets, people, and buildings are safe.

Challenges of IoT Data Analytics

The benefits of IoT Analytics far outways the challenges. But, Let’s take a look at the challenges of IoT Analytics: 

  • Balancing Speed and Storage: Companies must analyse data quickly, especially with time-sensitive information. Also, analysis often needs facts from the past to be useful. This requires storing info for a long time. Because of this, finding the right mix can be hard.
     
  • Skillset and Expertise: Professionals who know how to handle databases, do data science, and process data are needed. It might be hard to find workers with the right skills. 
     
  • Security and Privacy Concerns: It might be hard to ensure data is correct, keep private information safe from unauthorised access, and follow data protection laws. 
     
  • Scalability and Infrastructure: Adding more infrastructure to meet the growing needs of IoT data can take a lot of time. For structures that can grow, organisations must carefully plan and budget. This is to keep things running smoothly and make room for growth in the future.
     
  • Governance and Ethical Considerations: IoT analytics raises control and ethics questions about who owns the data, who has the right to use it, and how transparent the data is. Organisations need to set up clear policies for data governance, think about the ethical aspects, and make sure they follow the rules and standards.  

Frequently Asked Questions

What skills are required for IoT analytics?

You need to know how IoT technologies work and how to handle data. Other than being able to analyse and interpret data, you also need to be able to code in Python or R, understand statistics and ML. Also, be able to draw useful conclusions.

What are some common applications of IoT analytics?

IoT analytics is often used to make smart buildings use less energy. It improves predictive maintenance in manufacturing. It improves crop yield and resource management in agriculture. It allows for personalised healthcare. It makes the supply chain efficient in logistics.

How can organizations overcome challenges in IoT analytics?

Challenges in IoT analytics can be overcome by investing in scalable infrastructure. By making sure the quality of the data. It can also be done by putting in place strong security measures, thinking about ethical issues, and setting up clear rules for data governance.

Conclusion

This article covered everything you need to know to get started with IoT Data Analytics. We understood what IoT Data Analytics, Related Devices is, How it works, and lastly, some benefits and challenges of IoT Data Analytics. This was more of an overview. There’s still a lot to know, a lot to learn. 

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