The world is more interconnected than ever, which is blurring the lines between work and personal connectivity. For organizations, taking a strategic approach toward digital trust is necessary, including building standards, overseeing compliance/operations, and addressing growing security risks due to the massive number of connected devices.
Complexity in enterprise expense streams is growing exponentially every year and much of it has to do with telecom, mobile, and cloud.
On the fixed side, going to multiprotocol label switching (MPLS) and software-defined wide area networking (SD-WAN) is a major transition. Applications have moved from on-prem to the cloud and workers have moved to a hybrid model, which increases the amount of broadband and mobile being consumed. The technology needs to be managed, processed, and controlled by teams with specialized skills – which adds still more costs.
It’s only recently that digital twin technology has been implemented in software testing – and it’s offering some significant advantages.
The simplest definition of a digital twin is a virtual representation of an object or a system that uses real world data to create simulations, typically with the help of artificial intelligence and machine learning.
The IT pro’s job today is significantly more difficult than it was just a few years ago. IT complexity has grown disproportionately, even as we’ve seen slower budget and staffing increases. At the same time, digital transformation projects need staff time.
Many technology vendors have used artificial intelligence to transform their products. This is evident in areas like unified communications, contact centers and cybersecurity.
Yet one area of IT that has yet to see AI play a significant impact is the network. AI has been used to improve basic management functions but has yet to transform network operations.