One of the hottest topics in the Master Data Management (MDM) realm right now is how Artificial Intelligence (AI), Machine Learning (ML) and Master Data Management stick together.
The MDM platform vendors are thus also busy with promoting the AI and ML capabilities within their offerings.
Some examples are:
Reltio: The Reltio page on AI, ML and Deep Learning (DL) focusses on two main scenarios:
- Continuously improving consistency, accuracy, and manageability for better data quality (DQ), uncovering patterns, anomaly detection and streamlining data-driven jobs.
- Using data-driven applications to enable a seamless foundation for the generation of relevant insights and contextual recommended actions.
Riversand: In a post called Cloud multi-domain MDM as the foundation for Digital Transformation Riversand CEO Upen Veranasi emphasized that enterprises want to better understand their customers (utilizing customer insights through AI), create/sell products and services that meet and exceed customer expectations (product positioning through AI), meet their customers across all touch points (channel management), secure their customer’s information (privacy), and interact with trading partners to creating cutting edge product differentiation (cloud and AI).
Enterworks: In a recent Enterworks product announcement it was stressed that “future proofing” investments mean that core data management hubs can provide the data to harness the advances in other technologies, such as AI and machine learning.
Artificial Intelligence is also a frequent topic on MDM conferences around. The MDM Summit Europe 2019 in London is no exception with multiple sessions touching on the connection between AI and MDM – with the presentation from yours truly as an example. You can view the entire agenda for this event here.
Combining traditional information systems with ML requires a twofold integration:
a. Between truth-preserving operations and modal logic, statistics, or other heuristics.
b. Between explicit and implicit knowledge.