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Last Updated on April 14, 2023 Logistics, put plainly, is the management of resources that are either in transit, or need to be. Resources, in this context, refer to anything of value. This includes people, tangible goods, materials used to produce something else, or again, practically anything of worth that is in transit or, eventually, will be.

As IoT (Internet of Things) device form factors have got smaller and batteries have got better, use cases for IoT applications have expanded into every sector. From trucks, to shipping containers, people, to cattle, an organisation’s assets can be fixed, mobile, autonomous and even sentient - relaying information that can deliver new business insights and competitive advantage. But these benefits only come if the information is able to get to the right place at the right time.

People spend a large part of their day on their mobile devices, mostly consuming video content. Short-form videos like TikTok, Instagram Reels, and YouTube Shorts have become the preferred form of content for viewers. This makes video content for microlearning on mobile devices a perfect pairing. Microlearning is a form of delivering course content in small bits so that the learner can easily understand and retain it.

The 5 Biggest Threats: You may have found your business is relying on mobile devices more and more. The computing power of phones and tablets can now equal that of your average desktop computer, but their portability makes them far more useful in a remote working environment. With the number of online communications options available to mobile devices, making a call on a mobile phone is almost an afterthought – but with a cloud business phone system, it is an option that is supported.

The days of taking an afternoon off from work to head over to the bank are long gone. Banks have rapidly digitized, offering the vast majority of their services to customers remotely. Now users can save, invest, borrow, and make other financial transactions from the comfort of their smartphone, wherever they are. Despite this consumer-facing digitization, many banks have clung to the status quo when it comes to their field operations.

Data pipelines are the backbone of Machine Learning projects. They are responsible for collecting, storing, and processing the data that is used to train and deploy machine learning models. Without a data pipeline, it would be very difficult to manage the large amounts of data that are required for machine learning projects.