5 Ways AI Supports Data Leaders

Artificial intelligence (AI) continues to take the world by storm. And it’s sparking plenty of conversations among business, IT, and data leaders alike. Some conversations are filled with excitement and optimism about the future of innovation. Others are laced with concern, and a little bit of fear, with many worrying that AI will take over their jobs and make human expertise all but obsolete. 

The reality, however, is far less dramatic and far more promising. AI is a powerful tool designed to support and elevate the work of data leaders, not take it away. And when it comes to master data management (MDM), there are five key ways AI supports data leaders and enables them to deliver value from their data faster.

‍AI in Master Data Management: 5 Ways AI Supports Data Leaders

Managing data can be overwhelming. But as AI gains traction, a growing number of data leaders are embracing AI as a way to work faster, smarter, and more efficiently. Conversations at Big Data London late last year underscored this shift in perception, with many data leaders evolving their stance from one of fear to one of excitement. And the 2025 Data & AI Data Leadership Executive Benchmark Survey indicated the same. Of the data and AI leaders surveyed:

• 98% indicated they are increasing their investment in data and AI, up from 82% last year
• 91% of respondents said investing in data and AI is a top priority for their companies, up slightly from 88% last year 

Instead of viewing AI as a technology that will replace humans, conversations amongst data leaders are shifting to the myriad of ways that AI improves MDM by assuming responsibility for tasks that historically diverted focus from more strategic initiatives. 

Here are five examples of how AI supports data leaders as they work to improve their data mastering efforts and gain more value from their data. 

1. Entity resolution

From inconsistent data quality and issues of scale to changing business contexts and missing external data, resolving entities across systems and sources has been a persistent challenge for data leaders. Historically, data leaders relied on traditional, rules-based, MDM systems to help them resolve entities and create golden records. But this approach was flawed. Not only was it time-consuming and required a lot of human effort, but it couldn’t scale as the volume of data increased. 

AI changes the game when it comes to entity resolution. Advanced machine-learning-based AI algorithms can detect similarities hidden deep within the data that humans and rules can't see. And they can do so at a pace that is far faster and more efficient than humans or rules alone. And while AI can accelerate the process of resolving entities, AI alone isn't enough. Organizations using AI to power entity resolution must also involve humans to fix errors, make judgment calls on ambiguous cases, and provide additional context that the AI cannot confidently resolve. But those are just the exceptions; AI can handle the vast majority of the work. It’s the perfect example of how AI improves the work of data professionals, not entirely replace it.

2. Data governance 

Data governance is the foundation of effective data management, ensuring that an organization’s data remains accurate, secure, accessible, and trustworthy. By establishing clear policies, roles, and processes, data governance helps to maintain data integrity while enabling compliance with regulatory standards. But keeping up with the rapid growth and complexity of data is a challenge for data leaders. That’s where AI can help. 

Using AI, data leaders can automate data governance policies, helping to ensure compliance and improve overall data quality. It helps them to control data access by classifying, tracking, and protecting sensitive information so they can ensure that the right people have access to the right information at the right time. And, it can quickly and effectively spot anomalies and identify risks, even when hidden deep within the data and outside of the purview of a human being. Then, it can alert humans so they can apply oversight, expertise, and ethical judgment to decide if the anomalies and risks AI flagged are a cause for concern. 

3. Data quality

Delivering accurate, trustworthy data remains a persistent challenge for many organizations. Too often, organizations rely on manual processes and teams of people writing rules upon rules to keep the data clean and complete. This approach, while once effective, is no longer efficient. 

However, when data leaders embrace AI using tools like AI-native MDM, everything changes. Not only can data leaders finally deliver the high-quality, reliable, and accessible data that people across the business need to solve business challenges, but they can do so at scale. By employing pre-designed schemas, configurable data cleaning workflows, and pretrained ML models, businesses can quickly and easily master the entities that matter most – and they can do so in real time, ensuring that everyone has immediate access to accurate, trustworthy golden records. But that’s not all. AI-native MDM also acknowledges the critical role of human judgment and domain expertise when it comes to reviewing and refining the AI results, another example of where AI is a partner to – not a replacement for – human beings.

4. Data discovery

Data discovery is challenging for many organizations. From messy, incomplete data and data access issues to a lack of integration caused by data silos, organizations face an uphill battle when it comes to helping everyone find, access, and understand the data they need to power analytics and make better decisions. 

Using advanced AI algorithms, data leaders can enhance data discovery by making it quicker and easier for users to not just find the data they need, but also to understand critical context including its lineage, provenance, and appropriate use. And because data discovery is faster, users can quickly shift their focus to the analysis and use of data to enhance customer experiences, identify risks, and uncover new opportunities for revenue growth.

5. Data integration 

Data silos prohibit organizations from gaining a unified view of their data. They cause data to become fragmented and disconnected, resulting in inefficiencies, inaccuracies, and missed opportunities for collaboration. But there is a reason why data silos exist. It’s because integrating data from disparate sources is difficult. It requires organizations to overcome technical complexities and standardize formats all while maintaining data quality and security. 

Without a streamlined integration process, many organizations struggle to gain a holistic view of their data, which hinders decision-making and limits their potential to uncover deeper insights. That’s why data leaders need to embrace AI. Using AI, organizations can master data across systems and sources in days, not weeks or months, accelerating their ability to put golden records into the hands of users so they can use them to make better decisions. Further, by employing real-time APIs, solutions like AI-native MDM can master data in real time, providing a deeper, more immediate understanding of critical business entities and eliminating data silos once and for all. 

Clearly, AI is transforming modern business. But it’s not replacing human expertise – it’s amplifying it. From automating time-consuming, tedious tasks to surfacing insights faster, AI saves time, boosts efficiency, and empowers data leaders to focus on what truly matters. And as AI continues to evolve, data leaders who embrace its potential will be better positioned to stay ahead of the curve and drive meaningful outcomes throughout the business. 

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