Glossary of Terms

Less than 2 years

• IoT Integration
  • Integrating sensors that monitor equipment into existing business processes, either explicitly or implicitly, but on the lower end of the integration scale. For example, all of our customers are using sensors to monitor physical objects but these are still standalone applications within the business. We’re not talking enterprise integration here, just adding sensors into a business’s existing operations.

2 to 5 years

• Managed IoT Connectivity Services
  • Communication level infrastructure typically provided by the big mobile operators of today. Smaller M2M-SIM [virtual-]providers offering services specifically for IoT; e.g., Wherever, 1nce, etc.
  • Note: Everyware is not planning to provide GSM capabilities “as a service” (or any other RF-based telecoms capabilities) but it is an infrastructure component for enabling remote device communications that Everyware relies on.
• Internet of Things
  • Interconnected networks of physical objects in the real-world with a state representation (“digital twin”) in the virtual. Typically, people think of IoT as referring to connected appliances, equipment, phones, etc.; there is a broader meaning but will be out of reach for most laypeople at the moment.
• IoT Platform
  • An IoT platform is an on-premises software suite or a cloud service (IoT platform as a service [PaaS]) that monitors and may manage and control various types of endpoints, often via applications business units deploy on the platform. The IoT platform usually provides (or provisions) Web-scale infrastructure capabilities to support basic and advanced IoT solutions and digital business operations.
• IoT Edge Architecture
  • The “edge” is where sensors and local computation exists in a distributed network at the point of data collection (or nearby). Increasingly complex computation can be carried out at the edges of sensor networks to reduce the computation needed in the IoT platforms.
• Event Stream Processing
  • Event stream processing is the processing or analysing of continuous streams of events (a “sample” or higher-order “business event”). Event stream processing platforms process the inbound data while it is in flight. An event is essentially a data point captured in a business system. An event stream is a sequence of business events ordered by time.
• Digital Twin
  • A digital twin is a virtual representation that serves as the real-time digital counterpart of a physical object or process. Ultimately, this should exist from the point of creation of a physical object and serve as a historical record beyond the lifetime of the physical object.
• IoT Services
  • Internet of Things (IoT) services represents a set of end-to-end services in which businesses contract with external providers to design, build, install and operate IoT solutions, including advisory consulting for IoT planning. IoT service providers represent a range of small, midsize and large service firms that build and deploy IoT solution applications across industries. The focus of this market is on the medium and large service providers supporting key vertical markets for IoT adoption such as manufacturing, healthcare, transportation and retail. This market’s IoT service focus aligns with the design, build and install of an IoT solution and includes IoT planning services for an IoT-enabled digital business environment.
• Digital Business Technology Platform
  • A platform is a product that serves or enables other products or services. Platforms (in the context of digital business) exist at many levels. They range from high-level platforms that enable a platform business model to low-level platforms that provide a collection of business and/or technology capabilities that other products or services consume to deliver their own business capabilities. Platforms that enable a platform business model have associated business ecosystems. They typically expose their capabilities to members of those ecosystems via APIs. Internal platforms also typically expose their capabilities via APIs. But they may offer other mechanisms, such as direct data access, as required by the products that consume them.
• Edge Analytics
  • The definition of edge analytics is simply the process of collecting, analysing, and creating actionable insights in real-time, directly from the IoT devices generating the data.
• IoT-Enabled Applications
  • Applications that are built on the connectivity layers of IoT provide more specific functionality and features relevant to a particular niche or sector. For example, a bee monitoring app might use data analytics from IoT platforms to provide users with real-time information and insight with specific visualisations or reports or functions for managing bees.

5 to 10 years

• Asset Performance Management
  • Asset performance management (APM) encompasses the capabilities of data capture, integration, visualization and analytics tied together for the explicit purpose of improving the reliability and availability of physical assets.
• IoT Security
  • Methods for securing and authenticating IoT devices and networks.
• Model-Based Systems
  • A methodology that focuses on creating and exploiting domain models as the primary means of information exchange, rather than on document-based information exchange. A domain model is a conceptual model of the domain that incorporates both behaviour and data. A domain model is a formal representation of a knowledge domain with concepts, roles, datatypes, individuals, and rules, typically grounded in a description logic.
• IoT in Healthcare
  • Enabling the remote monitoring of patient wellbeing in healthcare settings and (via wearables) in everyday life, e.g., fitbit.
• Indoor Location for People Tracking
  • Locating people inside buildings in real-time. Possibly using wearables or AI-powered computer-vision systems.
• IT/OT/ET Alignment
  • Convergence and alignment of information, operational and engineering technology. Implies a level of “digital business technology platforms”.
• Blockchain [DLT] and IoT
  • A blockchain is a specific implementation of a DLT, so we will use DLT but be aware of the public consciousness (and pre-conceptions) around blockchain. Connecting devices and platforms with DLT for data veracity, authentication and security, disintermediation and decentralisation.
• Information Products
  • Products where the most important or valuable part of the value proposition is the information and knowledge they can convey. These products may exist entirely digitally, such as a whole database or pay-per-access to datasets and analytics via APIs.
• Digital Twin of the Person
  • Self-defining. Like a Facebook profile but combining personal, biometric and health data. Likely to be combined in the near future with AI and neural interfaces to provide humans with the ability to interact with digital networks and AI directly.
• Governance of Digital Twins
  • Methodologies and interoperable standards for managing decentralised digital twins across distributed networks.
• MDM of Thing Data
  • Globally decentralised, unified data models for unique objects/persons.

More than 10 years

• Autonomous Vehicles
  • Not just self-driving cars, but anything that can move under its own power. Already seen in fulfilment warehouses, for example. Autonomous also suggests a level of self-direction; a vehicle that would potentially set its own goals or work to achieve predefined goals without human intervention.
• Things as Customers
  • A thing could be self-owned and operated as a “Decentralised Autonomous Organisation” (DAO) with its own goals and priorities.
• IoT-Enabled Product as a Service
  • Selling outcomes of a product rather than the product itself but also IoT-enabled.