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Extracting Insights from IoT Data

To maximize the value of Internet of Things (IoT) data, organizations need a cloud architecture and an effective analytics strategy.

  • Overview
  • How IoT Works
  • The Value of IoT Data
  • Elements of IoT Architecture for IoT Analytics
  • Best Practices for IoT Analytics Architecture
  • Resources

Overview

IoT begins with sensors and devices that collect data from their environment. This data may include temperature readings, geographic location information, audio or video feeds and more. Edge computing, a strategy for computing on location where data is collected or used, allows IoT data to be gathered and processed at the edge, rather than sending the data back to a data center or cloud. The method used will depend on the specific application based on needs for power consumption, range and bandwidth. When IoT data arrives in the cloud platform, it can then be processed and analyzed using a variety of methods including machine learning and AI algorithms.

How IoT works

IoT begins with sensors and devices that collect data from their environment. This data may include temperature readings, geographic location information, audio or video feeds, and more. Edge computing, a strategy for computing on location where data is collected or used, allows IoT data to be gathered and processed at the edge, rather than sending the data back to a data center or cloud. The method used will depend on the specific application based on needs for power consumption, range and bandwidth. When IoT data arrives in the cloud platform, it can then be processed and analyzed using a variety of methods including machine learning and AI algorithms.

The value of IoT data

IoT devices and sensors can be used to gather data in a variety of contexts. As a result, companies across industries are using IoT data to drive business growth. Here are just a few examples of the value this data can deliver.

Improving equipment management and maintenance: Manufacturing companies use IoT sensors and analytics to measure vibration, heat and other important metrics to identify when equipment needs maintenance. IoT-enabled equipment can also transmit messages related to wear and pending problems, informing predictive maintenance.

Inventory tracking and warehouse operations: IoT sensors track inventory location, reducing the amount of time it takes for employees to find products. Smart shelves and bins identify stock levels in real time. Additionally, IoT devices can track patterns to streamline warehouse operations. 

Accelerating disease diagnosis: Healthcare organizations and hospitals use IoT devices to gather data that helps physicians make more-accurate diagnoses – in some cases, before significant symptoms present.

Informing product development: Companies in various industries use data from IoT devices to spot opportunities for improving existing products or developing new ones. Data related to usage and customer engagement provide valuable insights into market demand.

Improving city and utility services: IoT sensors can be put to use in a variety of ways to improve city services. For example, sensors can tell waste management services when a trash bin needs to be emptied. Water levels and quality can be monitored and managed remotely. And energy can be used more efficiently when smart streetlights are deployed.

Elements of IoT architecture

Setting up a proper architecture will effectively help ensure you have access to actionable insights from data when you need it. Consider each of the following elements.

Data sources: Smart devices, sensors and other IoT devices generate continuous data.

MQTT protocol and an IoT message broker: Due to frequently unreliable internet connectivity, IoT devices communicate using the MQTT protocol and an IoT message broker. The message broker uses a publish and subscribe mechanism to interact with other services, which subscribe to specific topics within the broker to access device data. 

Streaming service: A streaming service is used to ingest and buffer real-time device data, thus enabling reliable ingestion and delivery to a staging table in the cloud data warehouse.

Cloud object storage: In cases where the application requires it, cloud object storage is used to stage batch data prior to ingestion. For example, minute-by-minute data may be stored in cloud object storage, whereas aggregated data over a longer period may be stored in the cloud data warehouse. 

Streaming data support: Your cloud data warehouse should offer native support for JSON and other semi-structured data formats for easy ingestion of device data. 

Best practices for IoT leveraging IoT data

Here are five best practices for getting the most out of your IoT data.

Take advantage of the cloud’s capabilities: IoT generates a massive amount of data that is typically unstructured or semi-structured. This requires companies to leverage the significant compute power and storage that the cloud offers. Additionally, cloud solutions often provide built-in tools to connect, process and analyze IoT data.

One such capability that can exist in the cloud is for using time series data effectively. It enables a manufacturer to get a continuous stream of information about various processes, equipment and outputs over time. These can deliver valuable insights, optimize operations and help fuel data-driven decisions.

Deploy an architecture that supports IoT: Starting with a proper IoT data architecture will help ensure you can efficiently manage IoT data and mine it for insights as your organization grows and your needs evolve. Data coming from IoT presents challenges, including sometimes unreliable network access and devices that are often distributed across great geographic distances and that require multiple protocols. Additionally, your IoT architecture must support data mining techniques required to analyze the massive amounts of data that IoT produces.

Combine IoT data with other enterprise data: Looking at IoT and enterprise data can holistically unlock significant value by providing a wider view of operations, customers and assets. Integrating real-time insights from connected devices with existing CRM, ERP and supply chain data enables businesses to identify patterns, predict trends and make more informed decisions. This convergence of data streams fosters greater operational efficiency, enhances customer experiences and ultimately drives innovation and competitive advantage.

Prioritize security and governance: Governance, security and privacy mechanisms are crucial for IoT data because much of this data is sensitive or proprietary. Consider IoT data risks based on privacy, confidentiality and retention requirements, and seek out IoT solutions that include robust security and governance features.

Consider AI: AI can significantly enhance the value of IoT data; in the least, it is often required to analyze and make decisions derived from it and can enable other use cases.