AI takes digitalisation the necessary step beyond
Connecting the physical to the digital enables AI to interpret what it learns to help humans overcome our greatest emerging challenges.
The fourth industrial revolution is firmly upon us, like it or not, and just about every business and organisation on the planet is affected.
Among the many impacts is the super-connectedness of devices or things, the wired and wireless networks that connect them, and ultimately the people via the networks and devices.
This hyper-connectivity was a real problem at first. Businesses found they could connect just about anything from digital pens for signing delivery notes, all the way to legacy mainframes that had already put in years of service.
But the biggest problem was getting everything to communicate with one another in order to create some form of structured, strategic approach to all of this connected technology so that businesses could deliver some value for customers.
That's increasingly a non-problem today. Open source software and cloud services and solutions have largely eliminated system and solution disparity. Even farmers, in remote areas, have integrated telematics to control farm equipment and data they can interrogate to make informed decisions about their farming methods and activities.
What's missing now is the integration of the data this smorgasbord of connectivity sends streaming through the Internet and its network veins into an intelligent platform that can put it to good use.
Connecting these systems to an intelligent platform provides integrated capabilities that multiply the potential advantages.
Farming telematics data by itself is useful, but only to one farm. Add weather data from many small stations around the country, the data from thousands of other farms, interpreted satellite imagery of dams, lakes and other water resources, combined with rainfall figures, transport infrastructure, fleet management systems, import schedules, supplier systems and others, and you can build a picture that determines whether you have the means to feed a nation for the coming year or not.
There are many ways to use data, connectivity, integration and intelligence platforms. Cities can gather data from specialised sensors to help them manage urbanising populations. It enables evidence-based planning for deployment of resources and future urban design.
Cities gather street-level environmental measures such as temperature, humidity, barometric pressure, carbon dioxide and luminosity. They add calibrated particulate matter air quality measures. And they complement it with citywide crowd counts and flow insights using sensors and sophisticated, artificial intelligence (AI)-driven analytics.
The same machine learning or AI back-end platform also uses existing or new CCTV and acoustics sensors to provide real-time human and wildlife counts, trends and patterns so cities can respond quicker and better to any emergencies as they occur.
This is already reality. It has been used to create a digital city model for Wellington in New Zealand, for example, that combines natural, built and social environments into a virtual city. It lays resilience datasets over the data to develop scenarios such as the impacts of climate change. It gives citizens and city stakeholders an informed view that helps them engage on various topics for better decisions in running the city.
In Argentina, an urban surveillance system reduced by 80% vehicle accidents and crime, a landslide detection system gives 10 to 60 minutes of warning, electricity demand forecasting has slashed power consumption by 20%, better route information has cut taxi vacancies by 12% to 16% for one business, a predictive analytics solution has reduced the value of another company's parts inventory by 20%, fresh food waste was cut by a massive 40% using predictive analytics at a retailer, and NEC improved production efficiency at one of its factories through effective operational status analysis.
Connecting the physical world to the digital world is one thing but connecting the physical world to an integrated and intelligent platform that can interpret what it learns to help humans overcome our greatest emerging challenges is quite another.
Connecting these systems to an intelligent platform provides integrated capabilities that multiply the potential advantages. Machine learning and AI capabilities are helping organisations take the fourth industrial revolution to the next level to derive the ultimate in competitive advantage and innovate the future.
Mark Harris is chief marketing officer of NEC XON and has 30 years of experience in the industry leading and maturing the business development capabilities of ICT operations. He is the fulcrum of the marketing operations of NEC Africa and XON after the two organisations came together in 2018 to provide consulting, technical, support services and fully managed services to help keep customers relevant in the digital economy. Harris was marketing director of XON for three years, national sales manager for three years prior to that, and headed up the solutions architect team, as key account manager, and as sales manager for key accounts, for 11 years at a major South African Internet services organisation. The business enables African organisations to fully explore opportunities for safe city, energy storage and generation, cyber security, telecommunications, retail, managed services, cyber defence services, and cloud (both public and private), among others in Sub-Sahara Africa. The business now has hundreds of employees, with offices across nine provinces of SA and in 16 Sub-Sahara Africa countries.