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IDC TechScape: IoT Analytics and Information Management

The IoT will substantially change the mix of enterprise analytics and information management (AIM) investments over the next 24 months to meet the scale, speed, flexibility and automation required to support sensor-based initiatives.

IDC TechScape report on Internet of Things analytics and information management technology Analyst Report - 745 KB - 37 Pages

The Internet of Things (IoT) will substantially change the mix of enterprise analytics and information management (AIM) investments over the next 24 months to meet the scale, speed, flexibility and automation required to support sensor-based initiatives.

This new 37-page report from International Data Corporation (IDC) provides an assessment of 25 AIM technologies that support the IoT and provides guidance about maturity and adoption risk for each of the technologies:

  • IoT platform
  • IoT edge data collection
  • Sensor data collection
  • Historian
  • IoT data transport
  • Managed data transport 
  • Streaming data
  • Streaming integration
  • IoT data event services
  • Thing event store 
  • Thing registry and device management
  • Thing state machine 
  • IoT data services
  • Dynamic data management 
  • Graph database
  • Hadoop
  • In-memory data processing
  • In-memory relational 
  • Open data platform 
  • IoT value-added data services
  • Blockchain 
  • Data as a service
  • IoT analytics
  • Rich media analytics 
  • Statistical analysis
  • Streaming analytics 
  • Supervised machine learning 
  • Unsupervised machine learning 
  • IoT conditions and actions
  • Low-code rules
  • Low-code app platform
  • IoT visibility
  • Operational intelligence

Insight to action

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.

IDC TechScape: Internet of Things Analytics and Information Management — Current Adoption PatternsThat 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 37-page 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

As enterprises assess their existing AIM infrastructure, they are finding gaps in capabilities, in both skills and technology adoption. There is a focus on data-at-rest where sensor-based initiatives require data-in-motion supporting continuous analysts. Download this comprehensive report to learn more.

Related link:

IoT Data: Reducing Unplanned Downtime through Predictive Analytics