The internet of things (IoT) is the term given to the network of billions of devices that use sensors, software, and other technologies to collect and share data over the internet.
IoT devices can be as small as the smartwatch on your wrist collecting health and fitness data. They can also be much larger and more complex, such as a factory full of sensors and technologies that monitor the safety and efficiency of everyday operations around the clock.
The sheer number of IoT devices multiplied by the data points they collect on a near real-time basis makes IoT one of the biggest contributors to the rise of big data. Here’s a look at how big data and the internet of things are connected.
How IoT Data Gets Generated
IoT devices collect a multitude of data points in real time (or near-real time). This data can inform a number of operations, whether they occur autonomously (such as IoT-based traffic light control) or manually (like airport management rerouting foot traffic from a congested area).
IoT sensors can gather multiple types of data, such as:
Status data: Collects basic information like off/on and available/unavailable or other exact data like temperature
Location data: Tracks the movement of people or objects above, on, or below the earth’s surface
Automation data: Can be used to control the actions of automated operations or systems like an autonomous bus route
Once sensors collect the data, they send it to a central location using a data protocol.
What’s Making IoT Data Big
Because IoT sensors gather either real-time or near-real-time data, the amount of information they generate is massive. In fact, the International Data Corporation (IDC) projects that by 2025, there will be 55.7 billion IoT devices in use generating 73.1 zettabytes of data.
This results in IoT big data—and it has the potential to overwhelm traditional data processing and management tools.
Typically, IoT data is funneled to a central location where it’s available to analyze, interpret, and act on. Unfortunately, this isn’t always as easy as it sounds. When data sets become this large and complicated, drawing conclusions and making improvements from them becomes difficult. The data has essentially lost its utility to transform businesses into digital-first operations that leverage the full potential of AI-driven learning.
To get the most out of IoT data, you need data storage management and analytics tools built to support big data.
Big Data Storage and Analytics for IoT Data
To get a handle on big data and the internet of things, you'll want storage that's up to the task. The best big data platforms can not only store vast amounts of IoT big data but also support quick searching, indexing, and real-time analysis of your data. Modern high-throughput platforms input data quickly and scale to keep pace with your organization’s requirements. They can also quickly search and index your data, freeing up time that you’d otherwise spend on queries and data analysis.
When selecting a platform for managing big data and the internet of things, look for one that’s cloud-optimized. This enables you to perform analytics in the cloud and control access and permissions to your data on premises. This puts even more speed behind your IoT data analytics and shortens the time to make more informed decisions—the kind you expect to make after investing in an IoT infrastructure.
Learn more about gaining better insights faster with modern data analytics through Pure Storage®.
- Big Data vs. Traditional Data
- Structured Data vs. Unstructured Data
- 5 Ways Big Data Helps Companies Get Ahead
