Augmented master data management applies ML and AI techniques to the management of master data. This enables companies to refine master data to achieve two key objectives: to optimize their business operations to run more efficiently and transform their businesses to drive growth.

Augmented Data Management (ADM) offers a paradigm shift by leveraging AI, ML, and automation to enhance efficiency, accuracy, and scalability. By addressing key challenges such as data quality, security, and integration, ADM empowers businesses to derive actionable insights faster and more reliably, all while reducing manual efforts and costs.

Add to that, that more and more data will become part of MDM solutions. So, here augmented master data management comes with a promise of automating these tasks by using machine learning and artificial intelligence components that where feasible can rely on graph approaches that are able to picture complex relationships between master data.

Augmented Data Management (ADM) refers to the use of artificial intelligence (AI), machine learning (ML), and automation to streamline data handling. Unlike traditional data-management methods, which often depend on manual inputs and rigid protocols, ADM introduces a layer of smart, adaptive systems that continuously learn and evolve.

Augmented data management is the process of applying artificial intelligence (AI), machine learning (ML) models, and automation for managing data management tasks efficiently. According to Gartner, manual data management tasks can be reduced by 45 percent using machine learning and automation.