AI plays a crucial role in Master Data Management (MDM), which involves maintaining a central repository of an organization’s critical data. This data includes customer information, product details, and other important data entities. MDM helps organizations consolidate and effectively manage this data, often spread across various systems and applications. With the increasing focus on digital transformation, organizations require a reliable supply of high-quality, accurate, and easily accessible data. However, MDM has often fallen short of delivering on this promise.

LIKE PEAS AND CARROTS – AI AND MDM WORKING TOGETHER
Augmented data management techniques such as zero modeling and eventual connectivity have significantly addressed long-standing issues with master data management (MDM). These techniques allow business users to directly manipulate data without relying on IT support. Upfront data modeling and analysis are no longer necessary as many platforms now automate these tasks once data is ingested. These platforms can also automate data integration from various sources, leading to faster insights and data science initiatives.

AI AND MDM
AI plays a significant role in MDM by enhancing the efficiency and effectiveness of data preparation for widespread use within an organization. Despite the sophistication of MDM platforms, AI serves as a catalyst for optimizing data management. Some potential applications of AI in MDM include improving data processing speed, reducing costs, and simplifying data preparation.  

• Data quality is a significant issue in Master Data Management, as the data often lacks completeness, consistency, or accuracy. Advanced platforms automate a large portion of the data cleaning and enrichment process. However, artificial intelligence takes this to a new level by utilizing machine learning algorithms to quickly and easily identify and fix data errors, such as duplicate entries or inconsistent data formats. 
• Data governance is a difficult task for all organizations as it requires the establishment and implementation of policies and procedures to manage and protect data. Many organizations struggle with effectively enforcing these policies. However, with the help of AI, policies and rules can be automatically enforced as soon as they are inputted into the system.  
• Data democratization refers to the process of making data accessible and usable by non-technical users. Traditional master data management (MDM) systems have relied heavily on IT teams for deployment and ongoing use, which can be a barrier for non-technical users. Some platforms use a low/no-code approach to make MDM systems more accessible. The potential for artificial intelligence (AI) and low/no-code to democratize access to data is significant. 
• Data validation is essentially an MDM “guide” that provides explanations for the decisions it makes when requested but also instinctively supports or questions your decisions to steer you toward better outcomes and insights.  
• Data maintenance involves keeping your data current and readily available, which can be a time-consuming task. AI can assist in automating data maintenance by using machine learning algorithms to detect changes in data records and automatically update them. This approach has the advantage of allowing the AI model to continuously improve its reliability and accuracy based on the data in the master data management (MDM) system.   

WHAT’S NEXT?
The future of AI and MDM holds great promise. AI will revolutionize the way we manage data. Currently, we have only scratched the surface of AI’s capabilities in data management. However, once its full potential is realized, it will completely transform the way we prepare and utilize data to gain valuable insights. This transformation will have a lasting impact on the way we master and control data.