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Kaa's tight integration with Open Distro Elasticsearch brings powerful analytics and alerting capabilities into to your IoT solution

Features

Analytics features

Seamlessly integrated with the Kaa UI, Open Distro provides the following features:

Why Integrate with Open Distro?

Open Distro for Elasticsearch is hugely popular btechnology for providing log analytics, notifications, and search. Fueled by the freedom open source provides to developers and organizations. As an open-source innovation, Open Distro continues to thrive and gain popularity am0ng developers by providing a fully featured, 100% open source, community-driven distribution that makes it easy for everyone to use, collaborate, and contribute.

What is IoT Analytics?

As a key part of modern IoT solutions, IoT analytics enable businesses and IoT enthusiasts to convert data collected from their devices into valuable knowledge. That knowledge may come in multiple forms, such as integrated performance metrics, direct insights into user preferences, or timely anomaly detection for remote devices. No matter the line of business, driving valuable insights from their data helps companies achieve better outcomes from their connected devices and thus improve ROI for their IoT projects.

In Kaa, IoT analytics is organically integrated with other IoT features, such as device management, user management, and IoT dashboards. It helps streamline IoT data processing and maximize the value of device data by enabling users to create end-to-end analytical funnels on a single platform. Even when starting with very basic IoT use cases, companies can use IoT analytics to scale up their IoT projects over time and come up with more advanced solutions for their business.

Looking for a powerful IoT analytics solution? Let us show you how Kaa can help!

Contact us

USAGE

IoT analytics use cases

How to master IoT analytics in Kaa

Create a free Kaa Cloud account

Connect your first device to send data or use a device simulator

Read our tutorial on how to get started with Open Distro alerting

Set up some basic analytics and alerts for your device

Play around, explore more functions

Use our direct help chat in the Kaa Cloud to ask questions

Refer to Open Distro documentation to learn about some advanced options

Powerful Iot platform

Get free access to Kaa Cloud

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IoT Data Analytics in Kaa IoT Platform | Kaa IoT Cloud

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Kaa's tight integration with Open Distro Elasticsearch brings powerful analytics and alerting capabilities into to your IoT solution Features Seamlessly integrated with the Kaa UI, Open Distro provides the following features: Why Integrate with Open Distro? Open Distro for Elasticsearch is hugely popular btechnology for providing log analytics, notifications, and search.

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Explore IoT data analytics use cases, benefits & how it works. Learn how we can enable you to discover actionable insights from IoT data to make informed decisions.

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IoT analytics involves using specialized tools and techniques to analyze the vast amounts of data generated by connected devices in order to extract meaningful insights and inform decision-making. It helps organizations to better understand their operations, identify patterns and trends, and optimize their systems and processes.

What is IoT analytics?

IoT (Internet of Things) analytics refers to the process of collecting, storing, and analyzing data generated by the Internet of Things (IoT) devices. IoT devices are connected to the internet and are able to collect and transmit data about their environment, usage, and other characteristics. This data can be used to improve the performance and efficiency of the device, as well as to gain insights into the behavior and characteristics of the device's users.

IoT analytics often involves the use of specialized software and tools to process and analyze the data generated by IoT devices. This may include data visualization tools, machine learning algorithms, and other advanced analytics techniques. The goal of IoT analytics is to extract valuable insights and knowledge from the data generated by IoT devices and to use this information to make informed decisions and optimize the performance of the device and its related systems.

IoT analytics architecture

Effectively implementing IoT architecture will guarantee that you have access to useful insights from IoT analytics whenever you need them. Think about each of the following IoT architecture components.

Data generation: Continuous data is produced by sensors, IoT devices, and smart gadgets.

IoT message broker and MQTT protocol: The MQTT protocol and an IoT message broker are used by IoT devices to communicate since internet connectivity is frequently intermittent. In order to communicate with other services that subscribe to particular topics within the message broker in order to access device data, the message broker employs a publish and subscribe technique.

A streaming service: To provide dependable ingestion and delivery to a staging table in the cloud data warehouse, real-time device data is ingested and buffered using a streaming service.

Cloud object storage: Cloud object storage is used to stage batch data prior to ingestion when the application calls for it. For instance, minute-by-minute data might be kept in cloud object storage, while longer-term aggregated data might be kept in the cloud data warehouse.

Streaming data support: Make sure your cloud data warehouse supports JSON and other semi-structured data types natively for simple device data ingestion.

IoT rules engine: The business logic needed by the application is hosted by an IoT rules engine, which runs on data from the message broker and the cloud data warehouse. The rules engine communicates with the controls through messages.

What are the types of IoT analytics?

There are several types of IoT analytics, including:

Descriptive analytics

Descriptive analytics involves summarizing and visualizing data to understand what has happened in the past. This type of analysis is useful for understanding trends and identifying patterns in the data.

Predictive analytics

Predictive analytics involves using data and machine learning algorithms to make predictions about future events. This type of analysis is useful for forecasting demand, identifying potential problems, and making informed decisions.

Prescriptive analytics

Prescriptive analytics involves using data and machine learning algorithms to not only predict what will happen in the future but also recommend actions to take in response. This type of analysis is useful for automating decision-making and optimizing processes.

Real-time analytics

Real-time analytics involves analyzing data as it is generated by IoT devices in order to make immediate decisions or take immediate actions. This type of analysis is useful for detecting and responding to problems in real time.

Historical analytics

Historical analytics involves analyzing data over a longer period of time, typically months or years. This type of analysis is useful for understanding long-term trends and identifying patterns that may not be immediately apparent in real-time data.

How does IoT analytics work?

IoT analytics typically involves several key steps. Let's review them step by step.

Data collection

The first step in IoT analytics is to collect data from IoT devices. This may involve installing sensors or other data collection devices on the device or connecting the device to a network that allows it to transmit data.

Data storage

Once the data has been collected, it needs to be stored in a central repository or database. This may be done using a cloud-based storage solution or by using on-premises servers or storage devices.
NB-IoT will shape the future of smart cities
Data processing

Once the data has been collected and stored, it needs to be processed and analyzed. This may involve using specialized software and tools to filter, clean, and transform the data, as well as to extract insights and generate reports.

Data visualization

To make the insights and analysis generated by IoT analytics more easily understood, it is often helpful to use data visualization techniques such as charts, graphs, and maps. These can help to highlight trends, patterns, and relationships in the data that may not be immediately apparent from raw data alone.

Data-driven decision making

The final step in IoT analytics is to use the insights and analysis generated from the data to inform decision-making. This may involve adjusting the performance or behavior of the IoT device or making changes to related systems and processes to optimize their performance and efficiency.

Overall, the goal of IoT analytics is to extract valuable insights and knowledge from the data generated by IoT devices and to use this information to improve the performance and efficiency of the device and its related systems. By analyzing data from IoT devices, organizations can gain a deeper understanding of how the device is being used, identify opportunities for optimization, and make informed decisions that drive business value.

The use cases of IoT analytics

There are many different business use cases for IoT analytics, depending on the specific industry and needs of the organization. Some common examples include:

Supply chain optimization: IoT devices can be used to track the movement and status of goods throughout the supply chain, allowing organizations to optimize logistics and improve efficiency.

Predictive maintenance: By analyzing data from IoT sensors on equipment and machines, organizations can identify potential problems before they occur, allowing for proactive maintenance and reducing the risk of equipment failure.

Customer experience: IoT analytics can be used to track customer interactions with products and services, allowing organizations to identify opportunities to improve the customer experience and increase customer satisfaction.

Asset tracking: IoT devices can be used to track the location and status of assets, such as vehicles or equipment, allowing organizations to optimize the use of these assets and reduce the risk of loss or theft.

Environmental monitoring: IoT sensors can be used to monitor environmental conditions, such as temperature, humidity, and air quality, allowing organizations to optimize energy usage and improve sustainability.

IoT analytics jobs and opportunities

There are a variety of job roles related to IoT analytics, including:

Data scientist: Data scientists are responsible for designing and implementing algorithms and models to analyze and interpret IoT data. They may use techniques such as machine learning and statistical analysis to extract insights and identify patterns in the data.

IoT data engineer: IoT data engineers are responsible for designing and building the infrastructure and pipelines necessary to collect, store, and process IoT data. This may involve working with databases, cloud platforms, and data processing tools to ensure that data is available for analysis in a timely and reliable manner.

IoT solution architect: IoT solution architects are responsible for designing and implementing end-to-end IoT solutions. They may work closely with data scientists and data engineers to ensure that the necessary data is collected, processed, and analyzed to meet the needs of the business.

BI analyst: Business intelligence analysts are responsible for using data to inform business decisions. They may work with IoT data to understand trends, identify opportunities, and make recommendations to stakeholders.

IoT project manager: IoT project managers are responsible for planning and executing IoT projects. They may work closely with cross-functional teams to ensure that projects are delivered on time and within budget.

IoT sensors smarten everyday objects with awareness and cognition
Best IoT analytics companies and platforms

There are many different companies and platforms that offer IoT analytics solutions, and it can be difficult to determine which one is the best fit for a particular organization. Some of the top IoT analytics companies and platforms include:

AWS IoT Analytics

AWS IoT Analytics is a fully-managed service that makes it simple to run complicated analytics on enormous volumes of IoT data without worrying about the expense and complexity involved in developing your own IoT analytics platform.

SensorCloud

The SensorCloud platform from MicroStrain is a cutting-edge platform for storing, visualizing, and remotely managing sensor data. It makes use of strong cloud computing technologies to offer exceptional data scalability, quick visualization, and user-programmable analysis.

Exosite ExoSense IoT

ExoSense is a remote condition monitoring tool that may give operational insight into industrial assets, systems, and equipment. It can be set up in a matter of minutes. Any firm may now gather and visualize sensor data to give new features and services to consumers, estimate maintenance needs, and warn users of dangerous operating situations.

TrendMiner

Industrial Analytics Solutions for Process Manufacturing with Self-Service Self-service data analytics are provided by TrendMiner, a Software AG firm that is a part of the IoT & Analytics division, to optimize process performance in sectors like chemical, petrochemical, oil & gas, pharmaceutical, metals & mining, and other process manufacturing industries. With the help of no data scientists, consumers can directly query time-series data using the high-performance analytics engine at the foundation of TrendMiner software.

Google Cloud IoT Core

In order to connect, monitor, and consume data from widely scattered devices in an easy and safe manner, Google has developed a completely managed service.

Conclusion

From a business perspective, IoT analytics can offer significant risks, rewards, and benefits. Some potential risks include data security and privacy concerns, as well as the risk of investing in technologies that may become obsolete or unsupported.

However, the rewards of IoT analytics can also be significant. By collecting and analyzing data from IoT devices and systems, businesses can gain insights into their operations, improve efficiency, and make informed decisions. This can lead to cost savings, increased productivity, and competitive advantage.

Additionally, the benefits of IoT analytics extend beyond the business itself, as the insights gained through analysis can be used to improve products and services and to solve problems in a variety of industries and sectors. Overall, while there are risks associated with IoT analytics, the potential rewards and benefits make it a valuable tool for businesses looking to improve their operations and drive growth.

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Unlocking the full potential of connected devices with IoT analytics

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IoT analytics involves using specialized tools and techniques to analyze the vast amounts of data generated by connected devices in order to extract meaningful insights and inform decision-making. It helps organizations to better understand their operations, identify patterns and trends, and optimize their systems and processes.

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Many businesses today are using IoT analytics to make strategic decisions. A decade ago, such insights were very difficult to obtain quickly. IoT analytics' ability to transform business strategy making is one of the key factors behind the recent boom in IoT adoption worldwide.

What is IoT analytics?

IoT analytics refers to data analysis tools that analyze the large quantities of data generated by thousands of IoT devices.

In many ways, IoT analytics is related to big data. Firstly, IoT analytics uses analytical techniques used in big data. Secondly, the volume of data analyzed in IoT analytics is comparable to the volumes analyzed in big data analytics. Even though IoT and big data are distinct concepts, the emerging field of IoT analytics has formed a bridge between the two.

How does IoT analytics work?

IoT analytics work in the following manner:

1. Unprocessed raw data recorded by IoT sensors is collected from the entire IoT network. The data is usually in multiple formats.

2. The data collected in step 1 is processed to clean it up for analysis.

3. The cleaned up, processed data is stored in time-series format.

4. IoT analytics tools use various techniques to analyze the time series data and present insights via user-friendly dashboards.

5. Businesses act on these insights to improve their operations.

Types of IoT analytics

IoT analytics use several big data analytic techniques, such as:

1. Descriptive analytics: These analytics indicate whether business operations are going as planned and alert when disruptions occur.

2. Diagnostic analytics: These analytics deal with the identification of problems in business operations.

3. Predictive analytics: As the name suggests, predictive analytics uses machine learning capabilities and historical data to provide predictions on the performance of business operations and assets.

4. Prescriptive analytics: Prescriptive analytics deals with providing actionable steps to take to fix problems in business operations.

Benefits of IoT analytics

IoT analytics offer several benefits for businesses that use them:

1. Visibility on the entire IoT network - IoT analytics enables businesses to oversee the performance of their IoT network in real-time.

2. Fast identification and resolution of problems in business operations - Businesses can use diagnostic analytic capabilities to quickly identify performance problems and use prescriptive analytics to fix such problems.

3. Better asset utilization - Businesses can use IoT analytics to monitor the performance of their assets, such as machinery, and tweak their utilization to ensure the long term health of assets.

4. Cost optimization - IoT analytics help identify areas of cost reduction and steps to implement to achieve such cost reduction.

5. Expansion into new markets - IoT analytics offer valuable insights on operations and consumer behavior to ease expansion into new markets.

6. Improved product development - Businesses can study historical trends in product usage by consumers to identify areas of improvement for future versions of their products.

7. Better customer experience - IoT analytics helps businesses identify customer problems in real-time and act quickly to fix those problems, thus enhancing customer experience and delight.

IoT Analytics: Challenges

Businesses may face challenges in using IoT analytics tools. Some of these challenges are -

1. Excessive data generation and storage requirements -The aggregate data generated by thousands of IoT sensors are usually very large, thus making it expensive to manage and store such data.

2. The complexity of data - Data from multiple IoT devices consists of different types, formats and sizes, thus making it very complex and difficult to process and clean.

3. Security - Businesses, especially those dealing with consumer data, have to take various security measures to protect the stored IoT network data against hacking attempts and leaks.

4. Inaccurate data - Faulty IoT devices lead to inaccurate measurements, thereby messing up the analysis of such data. When you have faulty IoT devices at large, the insights offered by IoT analytics tools become unreliable.

5. Building a competent data analysis team - Businesses need to hire data scientists and analysts to run analytical techniques on the IoT data and derive actionable insights.

IoT Analytics: Use Cases

IoT analytics are useful in many ways, such as:

1. Predictive maintenance of machines - IoT analytics can predict when machines will break down. Businesses can use such predictions to conduct maintenance activities before such breakdowns actually occur.

2. Facilitating updates to consumer product software - IoT analytics can alert businesses when consumer products are malfunctioning. Businesses can react quickly and update the consumer product software via on the air updates.

3. Tracking inventory - IoT analytics help businesses track shelf inventory and avoid situations, such as stockouts.

How to select an IoT analytics tool?

Clearly, IoT analytics is empowering businesses to become agile and respond faster to rapidly changing business environments. Given the challenges involved in using IoT analytics tools, businesses need to be careful when trying to select an IoT analytics tool. The following factors can help them make a sensible decision -

1. Integration with Enterprise apps - IoT analytics tools need to have the capability of integrating with enterprise apps used by the business. This enables businesses to manage their data across multiple apps seamlessly.

2. Security - IoT analytics tools need to have built-in security features. For example, the Airtel IoT hub provides telco-grade security for IoT data with a dedicated private network.

3. Cloud-based - Cloud-based analytics tools are much cheaper to use as the data is stored on cloud servers.

4. Customization - IoT analytics tools should give users the option to run custom analytic techniques as well as create custom dashboards for data management.

By keeping these factors in mind, businesses can extract the full potential of IoT analytics and mitigate the challenges in implementation.

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IoT Analytics: Benefits, Challenges & Use Cases - Airtel Business

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Many businesses today are using IoT analytics to make strategic decisions. A decade ago, such insights were very difficult to obtain quickly. IoT analytics' ability to transform business strategy making is one of the key factors behind the recent boom in IoT adoption worldwide.

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IoT data analytics, often known as IoT analytics, is the act of evaluating data generated and gathered by IoT devices using a specific set of data analytics tools and techniques.

The actual goal of IoT data analytics is to transform massive amounts of unstructured data from diverse devices and sensors across the heterogeneous Internet of Things ecosystem into meaningful and actionable insights for driving smart business decisions and further data analysis.

Furthermore, IoT analytics allows for the identification of patterns in data sets, including both current and historical data, which may then be used to make predictions and changes concerning future events.
Table of Contents: Here is a video on IoT by Intellipaat:
Devices powered by IoT Analytics
There are numerous IoT devices that collect data, including:
Wearables

Fitbit and other smartwatches have progressed beyond simply tracking steps. By connecting your devices to the Internet, you can track your friends' fitness habits, compete with them, message them, and even answer the phone.

Fitness firms track this data, allowing them to design personalized packages if you sign up. This can involve workout habits, food, and objectives, among other things. The most recent smartwatches can even monitor heart rates and rhythms and correctly diagnose cardiac disorders in their users.
Kickstart your career by enrolling in Data Analytics Training Courses in Bangalore.
Smart Home

Smart houses include security systems that can be accessed and controlled while you are away from home, as well as appliances that can be turned on and off with digital assistance.

There are numerous devices that may be integrated into your house, as well as numerous data sets that can be collected to examine consumption patterns, system efficacy, and other factors.

Healthcare

IoT devices are widely used in healthcare. Bluetooth technology is used to produce hearing aids, monitor pulse-based alarm systems, and call for help.

This has significantly improved healthcare. The data gathered is invaluable in terms of developing newer and better technology.

Voice-Activated Everything

IoT gadgets include digital assistants. Alexa, Siri, and Google can take notes, look up information, play music, order cabs, check the weather, set alarms, and do a variety of other things.

The internet is constantly updating these digital helpers to increase their usefulness. Based on your regular interactions with digital assistants, their data enables businesses to customize their services for you.

IoT Analytics use cases

Smart agriculture:

Using IoT analytics, connected field machinery operates on the basis of data produced through IoT analysis. Time, geographical location, weather, altitude, and local environmental conditions are all elements in the study. Irrigation systems, for example, can be improved to give the exact amount of water predicted by rainfall.

Regular restocking of supplies:

Real-time inventory monitoring A food vending company with connected machines can have its machines request refilling based on product depletion. When the machine's stocks reach a certain level, this can be triggered.

Predictive maintenance:

Regular maintenance is required for various infrastructures. Pre-set templates can aid in the development of excellent predictive maintenance models for specific purposes using IoT analysis. For example, in long-distance transport vehicles equipped with heating and cooling systems, IoT analytics can indicate when vehicles need to be overhauled to prevent cargo damage.

Process Efficiency scoring:

Every organization has a variety of processes in place. IoT analytics can assess the efficiency of various processes and recommend modifications. IoT analytics results can detect present and potential bottlenecks and boost efficiencies.

How does IoT Analytics work?

There is a constant flow of data in massive amounts from a variety of devices. Without any hardware or infrastructure, IoT analytics can assist examine this data across all linked devices.

As your organization's demands vary, computational power and data storage scale up or down correspondingly, ensuring that your IoT analysis has the necessary capacity.

Let's talk about working with IoT analytics in 5 simple steps:

1. The first step is to gather data from various sources, in various forms, and at various frequencies.

2. This information is then processed using a variety of external sources.

3. The data is then saved in a time series for later analysis.

4. The analysis can be performed in a variety of methods, including specialized analysis tools, regular SQL queries, and machine learning analysis techniques. The outcomes can be utilized to create numerous predictions.

5. Organizations can use the information they get to create a variety of systems and applications to help with business processes.

Benefits of IoT Analytics

IoT analytics provides numerous advantages, including actionable intelligence and essential insights. These can lead to:

Better visibility and control result in faster decision-making.

Scalability of business requirements and expansion into other markets.

Automation results in lower operational costs and greater resource utilization.

New revenue streams as a result of operational difficulties being resolved.

Quicker solutions result from accurate problem identification.

Issues are resolved faster, and they do not reoccur.

Improved customer experience as a result of purchase history analysis.

Product development that is both faster and more relevant.

Conclusion

The development of IoT and big data is accelerating, affecting all technological and business sectors and increasing the benefits for both businesses and individuals. The increasing volume of data created by IoT systems has played an important role in the big data environment, promising to improve the capabilities of existing IoT systems.
Come to Intellipaat's IoT Community if you have more queries on the Internet of Things (IoT)!

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IoT Data Analytics: Benefits & Use Cases

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IoT data analytics, often known as IoT analytics, is the act of evaluating data generated and gathered by IoT devices using a specific set of data analytics tools and techniques.

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The vast network of IoT devices are bringing new levels of automation, connectivity, and intelligence. The huge amount of real-time data that they produce are transforming many industries, including considerable improvements to service delivery and customer experience. When integrated into data operations, IoT data analytics can bring many advantages.

Currently, the biggest obstacles to fully utilizing IoT data analytics are the costs and speed of storing, processing, and analyzing the extensive flows of data from distributed IoT devices. Despite these issues, we'll take a look at some of the most important areas where IoT data analytics are proving to be valuable.

Nine IoT data analytics use cases

Predictive maintenance

For industries that rely upon large machinery or equipment, such as utility service providers , IoT sensors and connected devices can measure the wear and tear of the machinery and predict failures. IoT data analysis measures and correlates load, usage, material throughputs, temperature, and other stressors. Information from the data generated allows organizations to identify issues before they happen and schedule maintenance accordingly. Per McKinsey, using IoT data analytics for predictive maintenance can lower maintenance costs by 40% and reduce equipment downtime by 50%.

Smart metering

Smart home devices and systems can reduce energy consumption through a centralized dashboard where homeowners can easily identify and optimize their energy use. When combined with smart metering analytics, energy providers can collect and analyze the data to develop tailor-made packages to meet specific users' needs or incentivize greater energy conservation.

Improving customer experience

Understanding how customers interact with a product or service and how to improve their experience is also well within the capabilities of IoT analytics. There are many ways IoT data analytics can be used to improve business offerings. In one example, AGT International analyzed fan movements, reactions, and heatmaps at basketball games. They then used this data to optimize seating placement and facility access, to improve fans' gameday experience.

Supply chain management
Medical devices
Remote IoT devices help improve patient outcomes with features like insulin monitoring and fall detection. With IoT data analytics, health service delivery is improving with insights gained into certain conditions and the effectiveness of prescribed medications. Armed with this information, medical practitioners are modifying and improving their treatment plans.

Smart parking

Smart building monitoring

Natural disasters and consistent environmental change can create a considerable risk to buildings and infrastructure, and potentially cost lives. Data gathered from IoT devices and IoT data analysis can track important metrics over time, such as tremors that may otherwise have gone unnoticed, moisture levels in walls, or deterioration in brickwork or plaster. This data can monitor structural health, predict issues, and any necessary remediation work.

Smart grids

Agriculture

Environmental sensors can measure real-time weather changes, calculate direct sunlight and rainfall and determine nutrients and moisture levels in the soil. All sensor data can be correlated against crop conditions, health and yield requirements. All powered by big data and IoT data analytics.

Performing IoT data analytics

To find out more about how Intertrust Platform enables important IoT data analytics use cases across the world, you can read more here or get in touch with our team .

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The vast network of IoT devices are bringing new levels of automation, connectivity, and intelligence. The huge amount of real-time data that they produce are transforming many industries, including considerable improvements to service delivery and customer experience. When integrated into data operations, IoT data analytics can bring many advantages.