IoT and AoT - What are They and
What Can They do for Your Business?

What is the Internet of Things (IoT)?

The Internet of Things 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.

IoT devices have sensors that collect and transmit staggering amounts of data every second. A few examples include:

  • Vehicles equipped with sensors to track driving patterns, speed, distance and routes
  • Manufacturing machines where sensors monitor efficiency, safety and quality
  • Transportation tracking and monitoring systems on airplanes and locomotives
  • Smart meters, thermostats and security systems in homes and workplaces
  • Wind-turbines, solar panels and other components of "smart" grids, that monitor and react to usage patterns
  • Wearable technology, like fitness trackers, pacemakers or "smart" clothing
  • GPS devices and other systems that provide geographical, topographical and climatological data

With so many of these "things," it is no wonder Gartner estimates 26 billion devices will be part of the IoT by 2020.

As more devices are fitted with sensors, more data is generated. And all this data from the IoT is already showing how it can transform both business and society. It helps us understand how the digital, physical and human components of highly complex systems relate and depend on each other.

But unless organizations can harness this data and do something smart with it, so-called smart "things" are just "things." Without the scale to capture, manage, and see the data in context, sensor data reveals only a small fraction of its value. It's basically useless and can't be used for strategic decisions. That brings us to AoT.

Defining the IoT  

What is the Analytics of Things (AoT)?

The sheer volume of IoT data makes the tasks of identifying trends and interpreting the data hugely challenging and complex. This is where Analytics of Things comes in. AoT is the “thinking” part of IoT. It’s analytics that can be applied to IoT data so it becomes meaningful and actionable to businesses. That means AoT can help businesses gain a better understanding of the relationships between the digital and physical worlds, and many different types of complex systems that comprise the IoT.

AoT refers mainly to analytics applied to sensor data throughout the IoT ecosystem. This includes edge analytics, operational analytics, and cogitative deep analytics. Technology environments such as discovery platforms provide business and data analysts the tools they need to analyze and explore raw sensor data. Sensor data is typically stored in data lakes or data warehouses, both on-premises and in the cloud.  

"It's useful to remember that The Internet of Things is only useful if those things are smart, and that will happen through The Analytics of Things."

- Professor Thomas H. Davenport, The Wall Street Journal

If the definition of AoT seems new, examples of AoT applications are probably familiar:

  • Smart grids in the energy and utility business that continuously monitor power flows
  • Mobile apps that provide real-time traffic alerts with recommendations on the best routes
  • Predictive maintenance alerts on transportation fleets, manufacturing lines, ATMs and other equipment
  • Self-driving cars, which are basically maneuvered by analytics processes running on multiple sensor data streams.

For businesses, AoT is an essential part of the IoT solution. Without it, companies cannot make sense of sensor data. AoT is where returns on big data investments come from. Further, it's clear that early adopters are already discovering the link between AoT and business value.  

How to Get Started: Getting to IoT and AoT Value

Many corporations have skills and experience in analytics at scale. Few have skills and experience with the complexity of sensor data. Teradata can help on both fronts. To generate the most value from the combination of IoT data and AoT insights requires both a range of data and analytics capabilities, including:

  • Data capture and management at large scale, including the management of multiple data sources
  • Predictive analytics that go beyond the basic metrics to determine when machines are about to break down, if shipments will arrive on time and whether a disease or illness is likely to strike an individual
  • Automated recommendations and actions, which will help prevent your people from being overwhelmed by the fast and furious flow of IoT data
  • Machine learning to create analytical models, which is a proven way to spot trends, causation, and anomalies.
  • Horizontally integrated data stores that enable cross-referencing and comparative analysis between divisions and product lines within a single company.

Existing data management and analytic tools are applicable to the Analytics of Things. However, sensor data is radically different from other kinds of data. Finding signals in all the noise of sensor data requires new skills and tools. At minimum, companies must be capable of handling enormous sensor data volumes and conducting complex analytics at scale. Those are baseline requirements for driving business value from investments in IoT solutions.

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