No enterprise software operates in isolation; it all has to be integrated. However, keeping integrations up to date can be a burden. Now, there’s a simpler, more effective way of doing it — enterprise mesh.
Mesh is a term usually associated with Wi-Fi. It refers to connecting multiple base stations so users get a seamless experience as they move from one place to another. Enterprise mesh takes this concept and applies it to an organization’s suite of software.
Just as companies must transform to meet today’s challenges, the software vendors they rely on must also mutate their products to become a better fit for the next generation of organizations. And that’s happening with ERP. From my perspective, here are the seven ways ERP is evolving, along with my tip for selecting an ERP vendor.
Low-code/no-code software is on the rise. Gartner, Inc. estimates that the worldwide low-code development technologies market will grow 23%, to $13.8 billion, in 2021. So, what is it? And why is everyone so interested?
“Low-code” and “no-code” refer to software that doesn’t require a qualified programmer to set up and change. Implementing this type of software still involves configuration, but far less actual coding.
Every ERP vendor will tell you their software is ‘future-proof’. But what characteristics really make enterprise software a smart investment in an unpredictable future? I believe there are four main qualities CIOs should look for.
There’s a lot of hype around machine learning, but what does it really mean in the context of enterprise software? How does it work, where is it adding business value today, and what should we expect from it in the future?
Let’s start with some definitions. Artificial intelligence (AI) is an umbrella term that includes machine learning (ML), deep learning and cognitive learning. The part most relevant to enterprise software is ML, which in this context is the ability to create automation through AI algorithms.
Good decisions require good data, but pulling it all together and getting it into a format you can analyze isn’t easy. One of the biggest factors isn’t technology, it’s people. Here are three steps for getting data into a form you can analyze, including the technology you need to process it and the data culture necessary to make it happen.
It’s trendy to move as much software to the cloud as possible, and there appear to be great benefits. But is it automatically the case that you should move your precious enterprise software to the cloud, too? What are the different ways of doing it, and what should you look for in the partners you ask to help you?
Think for a moment about how organizations are evolving. They all want to be lean, fast and specialized. They’re trying to become more adaptable, intelligent and resilient. Wherever you look, it’s a pretty consistent pattern.
Now, think about the main trends in enterprise software. It seems to be evolving in 20 directions at once: AI, ML, SaaS, PaaS—it feels like you’re drowning in alphabet soup. Even when you spot a recognizable word like “cloud,” there are half a dozen different flavors.
Edge computing means processing data near where it’s produced and consumed. It’s another dimension to that age-old workload placement question, “Where’s the best place to do this?” The trend this century has been to move computing into the data center or the cloud — now we have to consider the edge as well.
Necessity is the mother of invention — and the pandemic has certainly triggered some surprising innovations. “First up in the next normal,” says a recent McKinsey briefing on Covid-19’s implications for business, “is ‘revenge shopping,’ as many consumers open their wallets for goods and services they’ve done without recently.”
Claus Jepsen 