Building a Better ERP Architecture to Support Machine Learning

Users and developers agree on one thing in relation to enterprise resource planning (ERP) software: it’s boring.

For users, ERP means a lot of data entry added to their daily workload. While ERP is becoming more sophisticated with support for mobile devices and cloud functionality now commonplace, most users would prefer to use systems that are quicker and more responsive.

Developers can see this is a pain point and are trying to innovate past it, but that’s not always possible with the kind of monolithic architecture that can be found in contemporary ERP installations. ERP is a challenge to developers because ERP systems communicate with so many other systems on the network and each ERP installation is so massively configurable. Each individual instance of an ERP can feel like a unique piece of software in itself.

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Author: cvjepsen

Claus Jepsen is a technology expert who has been fascinated by the micro-computer revolution ever since he received a Tandy TRS model 1 at the age of 14. Since then, Claus has spent the last few decades developing and architecting software solutions, most recently at Unit4, where he is the CTO leading the ERP vendor’s focus on enabling the post-modern enterprise. At Unit4, Claus is building cloud-based, super-scalable solutions and bringing innovative technologies such as AI, chatbots, and predictive analytics to ERP. Claus is a strong believer that having access to vast amounts of data allows us to construct better, non-invasive and pervasive solutions to improve our experiences, relieve us from tedious chores, and allow us to focus on what we as individuals really love doing.

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