Cloud-Age Prediction and Proactive Actions

The IoT creates more data and at a faster rate than ever before. The only way we can really cope with this sensory overload is to use machines that remove the noise and clutter, providing a more rational and simplified view of ourselves as humans. Artificial intelligence is a mandatory capability for enabling the automation of these processes and creating prediction capabilities.

Analytics and artificial intelligence help form a truly wearbased maintenance model, with enough prediction capabilities to accommodate the slowness of the repair process. Instead of fixedtime or schedule-based maintenance, things can be maintained based solely on their true conditions. SLAs and other legal agreements benefit from this, as overload situations would be relatively easy to pick up.


Using a cloud-based analytics engine to predict machine failure based on real-time data is an important foundational element of the predictive maintenance scheme. A system that learns about data and uses it to become even more accurate in its predictions helps users optimise their operations and maintenance, providing timely service with fewer production cuts and problems.

The sensor-collected data of the maintained devices, machines, turbines, servers and other things in the IoT can be collected in the cloud for this kind of analysis. The maintenance predictions can then be executed on-site or in the cloud platform using a Big Data Analytics-aaS solution. The latter contributes to a faster time-to-service, and provides the same power of analytics as on-site solutions without the need for additional and extensive resources.

Building a Seamless Delivery Model

All of the above relies on the foundations of connectivity and cloud. Telecommunications, as an industry, has been very good at providing bulk services to the masses in a very reliable and trustworthy way. This strength is an asset in the IoT, assuming digital and communications service providers can cope with the IoT revolution more rapidly than in the past. The services needed for the IoT are constantly evolving, and as a result applying new pres to sales and delivery process automation, impacting how telecommunications products are defined, billed for, managed and supported.

Though not all of the IoT’s connections are based on wireless (3G, LTE) or physical cable (xDSL, FTTx or HFC), digital and communications service providers are certainly essential players in the IoT game. Many use cases will leverage the infrastructure operators have in place and will either piggyback off of existing connection (e.g. smartwatch using smartphone) or use more dedicated connections using the operator’s assets. Depending on the digital and communications service provider’s business model, it can either be a more active part of this process (i.e. a platform provider) or a more indirect player (i.e. a data wholesaler / pipe).

Assuming the digital and communication service provider has more to offer than pure connectivity through partnerships, or through building capabilities themselves, it can be more or less relevant as a channel or as a provider of solutions to customers — be it in retail, enterprise or wholesale. Regardless, buying services through a seamless, integrated set of processes is the only way forward for any enterprise that benefits from the IoT, which sets high requirements for sales and delivery automation.

Service orchestration needs to support a myriad of services and technologies, while enabling the bundling of said services and technologies for commercial propositions in a flexible, adaptive way – incorporating cloud-based services offered by digital and communications service providers and their partners. Even if the partner is the actual seller, the operator is able to offer a seamless buying and delivery experience through that partner, meeting the customer’s needs and redefining the way sales and delivery automation is executed today.

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