“The significance of the Internet of Things is not that more and more devices, people and systems are ‘connected’ with one another. It is that the data generated from these ‘things’ is shared, processed, analysed and acted upon through new and innovative applications, applying completely new analysis methods and within significantly altered timeframes. The Internet of Things will drive big data, providing more information, from many different sources, in real-time, and allow us to gain completely new perspectives on the environments around us.”
M2M applications are well established in a range of industrial applications: They are typically characterized by managing well defined data sets from specific classes of devices, encoded in vertically integrated technology stacks designed to enable monitoring and alerts.
The evolution from M2M to IoT fundamentally changes these characteristics. Data needs to be aggregated from multiple, disparate hardware elements and devices in addition to data from other physical assets and even from enterprise systems. The data arrives at higher speeds, in greater volumes and variability of structure. The data must be analyzed in real time to deliver richer applications enabling enhanced operational insight. The arrival of IoT places new demands on all components of the technology stack – and especially in the underlying databases used to store, manage, process and analyze the data. For those of you who want to dive deeper into the challenges of IoT data management, I recommend reading the Machina Research report “Why NoSQL Databases are Needed for the Internet of Things”.
1. Creating rich, functional applications
Data management must support the development of functionally rich applications with complex data and algorithms, with fast time to market and at low cost.
2. Unlocking business agility
The ability to support many new and frequently changing business requirements, causing fast and continuous evolution of the underlying data model.
3. Enabling a single point of truth & business convergence
Aggregate multiple views of related data from multiple systems into one consistent version of the data.
4. Real-time operational insight
Support both operational as well as analytical applications from the same data source.
5. Enterprise-grade platform
Provide highly scalable, cloud-based, robust and secure applications.
For a detailed discussion of the five key capabilities in the context of retail & logistics, manufacturing and the telematics & mobility industries, please download our whitepaper “IoT and Big Data”.