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What is Internet of Things (IoT)?

The Internet of Things, also known as IoT, is a concept that describes the connection of everyday physical objects and products to the Internet so that they are recognizable by (through unique identifiers) and can relate to other devices. The term is closely identified with machine-to-machine communications and the development of, for example, “smart grids” for utilities, remote monitoring and other innovations. Gartner estimates 26 billion devices will be connected by 2020, including cars, coffee makers.

At Teradata the prevailing thinking is that big data will only get bigger in the future and the IOT will be a major driver. The connectivity from wearables and sensors means bigger volumes, more variety, and higher-velocity feeds.

For companies lacking a clear business case and specific plans for sensor data and device data, the IoT will be more talk than action. Because when devices are talking and won’t shut up, big data threatens to deluge IT and analytics groups. Businesses that design and integrate the right data architectures, establish robust IoT data analytics platforms, and form the right teams will find that the IoT is not just the next big thing, but a necessity for staying competitive in the fast-moving marketplace.
 
Key Considerations with IoT
The IoT refers to the enormous network of devices and physical objects ("things") that can connect to the Internet, recognize other devices and objects, and communicate with them. But without the scale to capture, manage, and see the data in context, sensor data reveals only a small fraction of its value. To ensure data can be used for strategic decisions four key areas need to be considered:
Internet of Things (IoT): More devices fitted with sensors generate increasing amounts of data
Analytics of Things (AoT): IoT raw data streams require AoT analytics for context and insights
IoT Strategy: Rely on IoT experts to help generate value from IoT data
Industry Expertise: Corporate information increases usefulness of IoT data for data scientists
 
How are Companies using IoT?
According to Gartner, 80% of companies have unclear IoT goals, narrow use cases, and limited analytics scope. Companies must evolve from standalone or narrowly focused IoT analytics projects to highly integrated, business-driven IoT operations to derive sustainable value from IoT investments. Top companies are already finding relevant insights and deeply embedded value in their sensor data and putting it to work to drive innovation and performance gains. From retail and financial services to high-tech manufacturing and energy, IoT leaders are leveraging effective IoT applications, solutions, architectures and platforms for value today.
 
IoT Categories
The IoT market can be broken up into two broad categories -- industrial IoT and consumer IoT. The former includes heavy machinery, factory production lines, transportation, energy and smart cities. The latter includes wearables, phones, televisions and appliances that enable assisted living, home monitoring and home automation.
 
In industry, deployed sensors emit data to enable measurement of everything from temperature, light, vibration, movement, pressure, chemicals, airflow, liquid flow, location (e.g. GPS in smart phones, vehicles) and more with manufacturing, oil and gas, logistics, retail and utilities heavy investors. In manufacturing, analyzing industrial sensor data can help companies create ‘digital twins’ (an exact digital representation of a physical system) where real-time simulation and modelling of a system can prevent equipment from failing, predict problems and optimize performance on a continuous basis. This means companies can optimize operations and reduce risk. On the consumer side, sensor data can help monitor health and fitness, location, home energy consumption and product usage – all of which help deepen understanding of consumers while also helping product managers understand how to improve product design and development.
 
Squeezing maximum business impact out of IoT data requires overcoming new challenges, meeting new requirements, and extending existing architecture to accommodate IoT data collection, preparation, and analysis.