Overall job satisfaction is closely linked to the efficient management of office space. According to Gartner, employees who are satisfied with their physical workplace are 16 percent more productive, 18 percent more likely to stay, and 30 percent more attracted to the company over competitors (report available to clients).
Small and midsize businesses must rethink how they use space by designing workplaces that improve productivity and retain talent.
Many people think of a digital twin as a 3D rendering of a physical object. And while that might be part of it, we’ve had computer-aided design (CAD) models for decades. Why the hype now?
It’s because the digital twin concept involves far more than that.
Digital twin technology helps businesses visualize assets and optimize operations by synchronizing the virtual world with the real world. Internet of things (IoT) sensors instantly transmit assorted data from an object to its digital twin. As the conditions of the object change, so too do those of its digital twin.
A digital twin is not simply a 3D rendering; it is a dynamic digital representation of a real-world object in real time.
If you’re an accountant, your job is more likely to be automated than is a retail salesperson’s or a technical writer’s.
Last year, entrepreneur and author Shelly Palmer named accountants and bookkeepers as two key candidates for automation. And speaking at MIT last year, Kai-Fu Lee, the founding president of Google China who also built Microsoft’s research lab in China, said that he believed white collar work (including accounting) will succumb to robots before blue collar tasks.
But this news comes with a caveat: Automation isn’t synonymous with replacing humans.
Data is the currency of choice for companies looking to cash in on customers. In a world of start-ups and seed funds, being cash poor matters less when you’re data rich. Data is the basis for the effective use of machine-learning, AI, and one of this year’s hottest trends in customer relationship management—predictive analytics.
From pricing to forecasting to lead management, predictive analytics uses technologies that leverage customer data to be able to make smarter predictions about business outcomes.
Yet, knowing how to effectively translate data into actionable insights is challenging for companies with little to no experience in data analysis.
Throughout the history and evolution of accounting—from tablet record keeping in ancient Mesopotamia to blockchain in the 21st century—technology has been a key enabler in addressing accounting challenges.
However, in our current age of digital transformation, small businesses owners and accountants may be slow to adopt accounting tools or new technology because of factors such as: