Internet of Things (IoT) Architecture for Actionable IoT Analytics

The value of IoT is found in the data that connects devices and sensors produce, but this data must make it into a cloud data warehouse before it can be mined for insights. IoT architecture enables IoT data to transit through a network, ultimately arriving in a data platform for processing, analysis, and storage. In this article, we examine why IoT data is so valuable to companies across a variety of industries, components of an IoT architecture, and best practices for IoT analytics. 

How IoT Works

Let’s start with an overview of how IoT works. IoT begins with sensors and devices that collect data from the environment they’re in. This data may include temperature readings, geographic location information, audio or video feeds, and more. IoT sensors and devices are connected to the cloud through an IoT gateway or edge device via a variety of different methods, including cellular, WiFi, low-power wide-area networks (LPWAN), and satellite. (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.

What Makes IoT Data So Valuable

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

Improving equipment management and maintenance 

Manufacturing companies are using 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 are using IoT devices to gather data that helps physicians make more-accurate diagnoses—in some cases, before significant symptoms present.

Informing product development

Companies in many different industries are using 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 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 for IoT Analytics

Setting up IoT architecture effectively will ensure you have access to actionable insights from IoT analytics when you need them. Consider each of the following elements of an IoT architecture.

Data generation: 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. 

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

Cloud object storage if needed: 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: Be sure your cloud data warehouse offers native support for JSON and other semi-structured data formats for easy ingestion of device data. 

IoT rules engine: An IoT rules engine hosts the business logic required by the application and operates on data available in the cloud data warehouse and in the message broker. The rules engine sends messages back to controls devices.

Best Practices for IoT Analytics Architecture

An effective analytics strategy for IoT requires a sound approach. Here are four best practices for getting the most out of your IoT data.

Take advantage of the cloud’s capabilities

Due to the massive amount of data that IoT generates and the fact that this data is typically unstructured or semi-structured, companies that want to benefit from IoT analytics must have access to significant compute power and storage. The cloud is much better suited to the needs of IoT than on-premises systems. Additionally, cloud solutions such as Snowflake provide built-in tools to connect, process, store, and analyze IoT data.

Deploy an architecture that supports IoT

Starting with the right IoT data architecture will 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.

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

Artificial intelligence dramatically increases what an organization can do with IoT data. For example, edge intelligence uses AI as an analytic method deployed at the network edge. IoT systems must increasingly be autonomous to manage the magnitude of data.

Snowflake for IoT Analytics

Snowflake is ideal for businesses that want to benefit from IoT analytics. Snowflake’s ease of scaling, unstructured and semi-structured data support, native data loading, and additional built-in services allow organizations to streamline IoT data extraction, transformation, and storage. 

To see Snowflake’s IoT data capabilities firsthand, sign up for a free trial.