It promises a major change in mobility and, although not just'built' for IoT, it is heralded as a major driver of the growth of IoT. It's mainly in the scope of IoT trends for 2018 and beyond that we tackle 5G in this overview with a brief explainer and forecasts for the usage and role of 5G in IoT and its expected impact on mobility in evolving connected business realities. When bridging digital and physical by leveraging IoT and cyber-physical systems and when striving towards ever more automation and autonomous decisions in environments such as the smart factories of Industry 4.0, autonomous vehicles, smart buildings, smart cities and connected industrial applications in IoT in manufacturing, to name a few, you do need quite some resources to deal with the resulting deluge of data that needs to be analyzed and gathered to begin with. It's why edge computing, advanced analytics and artificial intelligence become so important in IoT and why edge computing is certainly among the top'IoT evolutions'. In its Worldwide IoT Predictions 2018, announced in a November 1, 2017 webcast, IDC stated that by 2020, IT spend on edge Infrastructure will reach up to 18 percent of the total spend on IoT Infrastructure – and there is quite some infrastructure.

Mega-conference Mobile World Congress is happening this week in Barcelona, Spain, and I've been on-the-scene running some cool, live brand engagements (huge thanks to Ericsson for making this work possible!!). I was here conducting interviews and capturing video content that showcases 5 fabulous IoT use cases in telecom. In this video I speak with Ulrika Jagare, the Director of Analytics and Machine Intelligence at Ericsson. Ulrika is a data-driven, powerhouse of an analytics strategist at Ericsson. You're seriously not gonna want to miss this, folks!!

What is the Intelligence Revolution equivalent to the 1/4" bolt? One of the key capabilities of the Industrial and Information revolutions was the transition from labor-intensive, hand-crafted to mass manufactured solutions. In the Information Revolution, it was the creation of standardized database management systems, middleware and operating systems. For the Industrial Revolution, it was the creation of standardized parts – like the ¼" bolt – that could be used to assemble versus hand-craft solutions. So, what is the ¼" bolt equivalent for the AI Revolution?

What is the Intelligence Revolution equivalent to the 1/4" bolt? I asked this question in the blog "How History Can Prepare Us for Upcoming AI Revolution?" when trying to understand what history can teach us about technology-induced revolutions. One of the key capabilities of the Industrial and Information revolutions was the transition from labor-intensive, hand-crafted to mass manufactured solutions. In the Information Revolution, it was the creation of standardized database management systems, middleware and operating systems. For the Industrial Revolution, it was the creation of standardized parts – like the ¼" bolt – that could be used to assemble versus hand-craft solutions.

The first element of building a successful IoT strategy is that it should be based on some business value such as percentage increase in revenue or reduction in cost or improvement in productivity/efficiency. The second element is that the commitment should be top-down: What is the leadership's commitment to the success of the IoT project? Third, it needs to have an ecosystem play, involving all the key ecosystem participants while the strategy is being built. The fourth one is the right set of skills and the capability to consume IoT. And the fifth element is having an end-to-end perspective that touches everyone in the organization for whom the project is relevant.

Powered by sensors, connectivity and smart machines, the Internet of Things (IoT) is reshaping the manufacturing and industrial processes, effectively changing the paradigm from'repair and replace' to more of'predict and prevent'. For asset heavy industries, unplanned equipment downtime can mean big losses in revenues and productivity. For example some of the leading automotive manufacturers estimate that unplanned downtime can cost them as much as $15,000 – $20,000 per minute and a single downtime event can cost approximately $2 million. Given the business impact, it is not surprising that these industries have been focusing on driving predictive maintenance to minimize downtime and losses. Manufacturers and organizations in other asset heavy verticals cannot afford to wait till a machine or equipment breaks down in order to figure out what went wrong.

Even though predictive analytics has been around for quite some time, interest around this topic has increased over the last couple of years. It is no longer enough for a company to accurately record what has happened. Today, an organization's success depends on its ability to reliably predict what will happen – be it predictions about what a customer is likely to buy next, an asset that could require maintenance, or the best action to take next in a business process. Predictive analytics uses (big) data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data, enabling both optimization and innovation. Existing processes can be improved – for example by forecasting sales and spikes in demand and enabling the required adjustments to the production planning.