IoT & Industrial IoT 101: Understanding the New-age Enterprise in a Measurable Way

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IoT & Industrial IoT 101: Understanding the New-age Enterprise in a Measurable Way

The Internet of Things (IoT) is the internet connectivity between physical devices. It is one of the widely used technical development. Every organization has a massive amount of data which are in different formats, primarily time-series data. They need to study these data to derive some valuable insights.

Modern enterprises use IoT applications and sensors to optimize manufacturing and supply chain practices, develop smart cities, and more. The sensors embedded in the physical devices collect the data and transmit it to the following connected devices. Such a large volume of data requires Big data technologies to parse, integrate and prepare the reports to detect, measure, and obtain real-world information on time. Using Artificial Intelligence (AI) and Machine Learning (ML) the data can be analyzed thoroughly by creating automated models and finding anomalies.

The goal of IoT is to help us understand the world in a measurable way.

On the other hand, the Industrial Internet of Things (IIoT) makes use of smart sensors and actuators to improve manufacturing and industrial processes. IIoT, also known as the Industrial Internet or Industry 4.0, uses intelligent machines and real-time analytics to leverage data generated by machines in the industry over the years.

IIoT supports the use of sensors to capture images or read data from these sensors, analyze the data, and apply the data engineering and AI processes, thereby helping enterprises make informed decisions and improve their RoI.

There are many real-time applications of IIoT, such as:

  1. Building smart cities
  2. Fleet management
  3. Self-driven cars
  4. Air quality applications
  5. Monitoring temperature in CoVid like situations
  6. Image processing
  7. Capturing images and analyzing for social distancing
  8. Renewable industry

IIoT Environment

Every Industrial IoT environment consists of:

  • Sensors for communication and storage of information,
  • Edge data communication infrastructure,
  • Data engineering required for applications and analytics,
  • Analytics and applications that generate business information,
  • And business leads to sell it well.

IIoT Infrastructure

Let’s take a deep dive into all the subtleties of the IIoT.

IoT/Sensor Team: Edge devices and intelligent assets transmit information directly to the data communications infrastructure. It is converted into actionable information on how a specific piece of machinery is operating. Then, the IoT Team handles understanding the sensor type as per the application and communicating to the next team.

Edge Team: Each sensor communicates in a particular protocol. The software or the embedded platform reads the sensor data supporting the protocol. It is usually in C++, though Java and Python can also be used. This team ensures the data in some of the databases referenced by the Data Engineering team and the analytics team is transferred in raw format.

Data Engineering Team: Once the data is in raw form, any application or analytics will no longer read the data correctly. So, it must be parsed/converted to some consumable format like aggregations, business-related KPIs, etc. The data engineering team handles it through all the Big Data and technical stacks.

Application and analytics Team: The application and analytics team helps create dashboards that show all KPIs and business value by creating models supporting customer business growth and applying analytics to the data.

Business Team: The business team’s job is to establish customer relationships, gather requirements, and transform them into products, making customers happy and satisfied.

The adoption of IoT & IIoT is entrenched in how the world will evolve in the next decade. Though it has become popular, parallel developments in AI and ML will make IoT & IIoT more practical and widespread.  Its usage and real-time applications will set the stage for the new-age enterprises.


Tredence is a Data Science and AI Engineering company dedicated to solving the last-mile challenges in adopting analytics. We define the “last mile” as the gap between creating insights and realizing the value and help our clients bridge it through end-to-end knowledge transfer, including transformative technology and transact. We have more than 800 employees and have offices in Palo Alto, Chicago, Toronto, and Bangalore. Our clients include some well-known names in the Retail, CPG, high-tech, Telecommunications, tourism, and industrial sector.


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