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IDC Techscape IDC Techscape: Internet of Things Analytics and Information Management IDC TechScape IDC TechScape: Internet of Things Analytics and Information Management Maureen Fleming Stewart Bond Carl W. Olofson David Schubmehl Dan Vesset Chandana Gopal Carrie Solinger IDC TECHSCAPE FIGURE FIGURE 1 IDC TechScape: Internet of Things Analytics and Information Management — Current Adoption Patterns Note: The IDC TechScape represents a snapshot of various technology adoption life cycles, given IDC's current market analysis. Expect, over time, for these technologies to follow the adoption curve on which they are currently mapped. Source: IDC, 2016 December 2016, IDC #US41841116 IN THIS STUDY Implementing the analytics and information management (AIM) tier of an Internet of Things (IoT) initiative is about the delivery and processing of sensor data, the insights that can be derived from that data and, at the moment of insight, initiating actions that should then be taken to respond as rapidly as possible. To achieve value, insight to action must fall within a useful time window. That means the IoT AIM tier needs to be designed for the shortest time window of IoT workloads running through the end- to-end system. It is also critical that the correct type of analytics is used to arrive at the insight. Over time, AIM technology adopted for IoT will be different from an organization's existing technology investments that perform a similar but less time-sensitive or data volume–intensive function. Enterprises will want to leverage as much of their existing AIM investments as possible, especially initially, but will want to adopt IoT-aligned technology as they operationalize and identify functionality gaps in how data is moved and managed, how analytics are applied, and how actions are defined and triggered at the moment of insight. This IDC TechScape covering IoT AIM is designed to help: . Enterprises learn more about the newer AIM technologies that support IoT . Align these technologies with an enterprise's technology risk profile to determine what is ready to adopt and what should be monitored . Gain a better understanding of where an IoT team will need to create skills and competencies as it plans to adopt newer AIM technologies TECHNOLOGY MARKERS OF MOMENTUM The AIM tier of IoT encompasses the following: . Model discovery, training and design, and the appropriate infrastructure for managing the data associated with these major activities . Software used in production to collect and deliver data reliably to processing targets . Integration to ensure data is in a format useful to target environments . Database options to support ancillary functions not included in most IoT platforms as well as used by enterprises to build their own capabilities as needed . Analytical software . Thing registration, state, and device management . Operational intelligence (OI) and monitoring to manage the larger systems and processes of things and related assets . Low-code environments to describe the relationship of events to conditions and actions and to support IoT application development Refer back to Figure 1, which fits the IoT AIM technologies into the appropriate curves. IoT is an emerging opportunity, and adoption of both IoT-specific and IoT-generalized AIM technologies for IoT is also early. We positioned each technology on the curves as an optimization of market adoption and technology maturity to show relative position as opposed to pure market adoption. If we looked only at market adoption, the labels would generally be too concentrated in the early sections of the curve to be legible. ©2016 IDC #US41841116 2 Table 1 organizes AIM technologies into functional areas, the type of curve, and IDC's assessment of stage of adoption, risk level, speed of adoption, and years to market adoption maturity. IoT AIM consists of generalized AIM useful for IoT as well as IoT-specific technologies organized into the following categories: . IoT data collection . IoT data transport . IoT data event services . IoT data services . IoT value-added data services . IoT analytics . IoT conditions and actions . IoT visibility The descriptions of each technology are listed in the same order as they are presented in Table 1. TABLE 1 IDC TechScape Technology Markers of Momentum Years to Stage of Speed of Market Full Technology Curve Type Adoption Adoption Risk Level Buzz Adoption IoT platform Incremental Evaluate Fast Medium Medium 7 IoT edge data collection Sensor data collection Incremental Deploy Fast Medium High 3 Historian Incremental Evaluate Medium Low Low 5 IoT data transport Managed data transport Incremental Test Fast Low Low 2 Streaming data Transformational Test Fast Medium Medium 5 Streaming integration Transformational Evaluate Medium High Low 8 IoT data event services Thing event store Opportunistic Evaluate Medium Medium Medium 5 Thing registry and device Incremental Deploy Fast Low Medium 3 management Thing state machine Transformational Test Fast Medium Medium 5 IoT data services Dynamic data management Incremental Deploy Fast Medium Medium 5 Graph database Transformational Test Slow Medium Low 10 ©2016 IDC #US41841116 3 TABLE 1 IDC TechScape Technology Markers of Momentum Years to Stage of Speed of Market Full Technology Curve Type Adoption Adoption Risk Level Buzz Adoption Hadoop Incremental Deploy Medium Medium Medium 5 In-memory data processing Transformational Deploy Fast High High 5 In-memory relational Incremental Deploy Medium Low Medium 5 Open data platform Incremental Evaluate Medium High Medium 6 IoT value-added data services Blockchain Transformational Evaluate Slow High High 10 Data as a service Transformational Evaluate Fast Medium Medium 5 IoT analytics Rich media analytics Opportunistic Deploy Fast Medium High 10 Statistical analysis Incremental Deploy Fast Medium Low 5 Streaming analytics Transformational Evaluate Medium Medium Medium 5 Supervised machine learning Incremental Evaluate Fast Medium High 10 Unsupervised machine learning Transformational Evaluate Medium Medium Medium 15 IoT conditions and actions Low-code rules Incremental Deploy Medium Medium Low 7 Low-code app platform Opportunistic Evaluate Medium Low Medium 5 IoT visibility Operational intelligence Opportunistic Evaluate Medium Medium Low 7 Source: IDC. 2016 ©2016 IDC #US41841116 4 IoT Platform FIGURE 2 IoT Platform Markers of Momentum Source: IDC, 2016 IoT platforms are a collection of core software components required to support IoT workloads. This includes: . Registering and connecting devices to the network . Maintaining sensor state data associated with each device . Analytics . Device management . Application development . Security Many of the IoT platforms are offered as cloud software or related sets of IoT services, while others can be deployed on-premises in a datacenter or at the edge. Examples of products include Amazon's AWS IoT Platform, Bosch IoT Suite, Cisco Jasper, GE Digital's Predix, IBM Watson IoT Platform, Microsoft Azure IoT Suite, Oracle IoT Cloud, PTC's ThingWorx, and SAP Hana Cloud Platform IoT services. Pros: . Is a relatively straightforward way to launch an IoT experiment or initiative . Speeds up the process of operationalizing IoT workloads . Cons: . Locks into a single vendor for core IoT workload functions . Is not comprehensive and will require interoperability with missing pieces of an end-to-end middle tier ©2016 IDC #US41841116 5 IoT Edge Technology Sensor Data Collection FIGURE 3 Sensor Data Collection Markers of Momentum Source: IDC, 2016 Sensor data collection edge technology does exactly what its name implies: collects data from sensors. The data collected is persisted in memory or on disk until such time as it is converted as needed, analyzed, filtered, and forwarded via data transport technology. If a historian is also in use, the data may be persisted for a longer period of time to facilitate transaction management and/or replay capabilities. Sensor data collection software — whether it is embedded or installed in a gateway device or offered as standalone server software or virtual machine software — requires the ability to capture data transmitted by sensors over a variety of protocols, transform into a format that can be transmitted over the internet or back into the originating protocol, and provide reliability mechanisms to request sensor data retransmission and security to prevent unauthorized access and untrusted delivery of data and may require filtering to reduce outbound data volumes. While there are IoT cloud services that directly collect sensor data, they require that transmission uses open application or messaging protocols, such as MQTT, HTTP, or AMQP. For that reason, we classify them as IoT data streaming services in the data transport section. Depending on requirements, there is often a need to collect sensor data from a mobile edge, such as vehicles. When the edge is mobile, data may be collected using specialized embedded devices, such as National Semiconductor cRIO, or purpose built by the manufacturer. In that case, communications between the embedded device and a central aggregation source may require different networking or purpose-built communications systems. Embedded sensor data collection and specialized network communications are outside the scope of this IDC TechScape. Examples of sensor data collectors include Intel's Wind River Intelligent Device Platform XT, MathWorks' ThingSpeak, PTC Kepware's KEPServerEX, and MuleSoft's Anypoint. Pros: . Decouples sensors from central data processing applications . Provides a level of data persistence at the edge on which edge analytics
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