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IoT Analytics

The overwhelming growth of the Internet of Things and IoT analytics has generated so much data that there's an arms race to house, secure and analyze all of it effectively.

IoT devices are expected to exceed 20 billion by 2020 and 40 billion by 2025, with spending to reach $745 billion in 2019 and exceed $1 trillion in 2022, according to the IDC.

Manufacturing, transportation and utilities sectors pace the spending, while insurance, government and healthcare sectors are expected to enjoy the most rapid growth.

What is IoT?

The Internet of Things (IoT) is the enormous network of billions of interrelated smart devices, their users, technologies and the data they collect and share.

The IoT is made up of devices that connect to a wireless network and transmit and share data. More obvious examples include wearables, smart cars and smart TVs. Security devices and smart thermostats are everyday examples of the IoT at home.

With every set of data produced, there's a new opportunity for IoT analytics.

Enter IoT Analytics

Businesses rely on IoT analytics more and more each day.

Cities and municipalities rely on the IoT for traffic management and energy conservation initiatives.

There are several supply chain data science use cases that demonstrate the impact of IoT analytics on modern business. Supply chain forecasting involves understanding the variability and volatility of demand.

Supply chain risk inherently lies throughout the pipeline. With IoT analytics providing real-time feedback, manufactures can adjust production accordingly.

The same industries that are moving traditional on-premise data warehouses to the cloud -- government, health care, finance and banking -- are turning to the IoT for deeper and faster insights.

IoT Demands

In order to optimize the collected data from IoT devices, the analytics platform must evolve.

IoT analytics applies the familiar data analytics process to the connected sources of the IoT. When posed with the question of "What is data analytics," the answer must grow to accommodate the constant and enormous volume of streaming data.

Additionally, the IoT analytics platform must ingest structured, semi-structured and unstructured data sets, and perform related tasks within strict time constraints. Many existing infrastructures can't handle the demands of IoT data volume and velocity.

IoT analytics empower companies to make decisions based off data from connected devices. But with billions of IoT devices, the sheer volume of data demands specialized analytics.

Snowflake and IoT Analytics

Snowflake's built-for-the-cloud data platform and data warehouse provide the scalable architecture and ability to ingest diverse data sets needed to handle the processing and analyzing of IoT data. It empowers users with the data analytics applications to uncover actionable insights quickly.