Increased use of AI in IoT analytics is enabling businesses to derive more
value from their data and be more selective in the data they have to process.
Internet of Things has attracted significant interest across many industry verticals during the past few years. On the surface, it would seem the two elements in the technology’s name — “internet” and “things” — should be the focus of both organizations and their technology suppliers. However, focusing on the communications infrastructure or the endpoint technology alone would be akin to focusing on the scaffolding of a building and not the value it brings to its owner.
The business value of IoT lies in its data and its immediacy, in terms of both physical proximity (to the device, system or user) and temporal proximity (real time). Turning that data into meaningful insights to impact an organization’s operations is how IoT analytics realizes that value.
IoT data is driving digital business initiatives, such as enhancing operational efficiencies of assets (as shown in the oil rig example), improving customer engagement and creating new revenue streams. However, the velocity and volume of this new data source also threaten to flood these organizations’ technical infrastructure, which may lead to increases in operational expenses, security vulnerabilities and critical points of failure in mission-critical systems.
IoT analytics is the key mechanism for controlling this flood of data.
Data and analytics leaders are increasingly focused on IoT use cases, IoT data and the associated requirements for data management and analytics.
IoT causes organizations to revisit or reconsider whether they have the right set of capabilities in place. The new and unique characteristics of IoT solutions create pressure on various aspects of traditional information management infrastructure, and leaders of IoT initiatives need to be proactive in identifying gaps and weaknesses early in their initiatives.