How Machine Learning Will Impact MDM Posted on : Apr 24 - 2017

The traditional “system of control“ approach to master data management (MDM) — where the focus is on centralizing and controlling data to better distribute information via a system of control —  is no longer sufficient for an organization to be in command of its data. Thanks to the internet of things (IoT), machine learning (ML) and artificial intelligence (AI), the world is becoming increasingly digital. Just ask Gartner analyst Frank Buytendijk, who predicted in a March 2017 presentation that “In 2016, spending on IoT hardware will exceed $2.5 million … every minute.”

As a result, the challenge of creating a trusted view of all available data and meeting the demands of business users is about to get much more demanding. Today, it is even more critical that companies become data agile so they can adapt to ever-changing demands and be enabled rather than hindered by data. The demand for MDM is moving toward the creation of a “system of engagement” where the emphasis is placed on creating next-generation communication and collaboration capabilities.

The growing concept of IoT and its expansion will become even more pervasive due to the persistent development of Wi-Fi availability. In fact Forrester predicts, “IoT will be distributed across edge and cloud, boosted by AI and containers.” In other words, not only will Wi-Fi availability assist the growth of IoT, but also new machine learning. Both factors will enable nearly any device that can be connected to the internet to be connected to each other as well. Machines and devices will increasingly be driven by data and, therefore, will also become part of the over-all IoT equation.

What ML Means for MDM

Instead of being used as a system of control, the market will soon demand that MDM solutions adapt and react to data demands quicker, whether internally or externally. Defined by some as a kind of artificial intelligence that provides computers with the ability to learn without being explicitly programmed, there is great opportunity to leverage machine learning to adapt data from a source to a consumer faster. Rather than focusing on enforcing format and meaning to facilitate exchanges, ML will enable organizations to discover patterns in data, as well as propose associations, correlations, and adaptation. View More