Matt Holzapfel
Matt Holzapfel
Head of Corporate Strategy
April 26, 2024

How Is AI Changing Data Management?

How Is AI Changing Data Management?
  • Artificial intelligence (AI) is transforming data management by enabling organizations to find, enrich, and maintain reliable golden records for key business entities
  • Businesses are using AI in data management to improve data quality, bridge data silos, resolve entities, and enrich data
  • AI requires human oversight to ensure the quality and trustworthiness of the AI

Artificial intelligence is reshaping the world in ways we could never have imagined just a few decades ago. From AI-powered diagnostics in healthcare that hold the promise of earlier disease detection to powerful algorithms that fuel better investment strategies and fraud detection in financial services, AI is transforming the way businesses across industries use and interact with technology. And it's changing the way organizations manage their data, too.

With more than 328 million terabytes of data created daily, traditional data management solutions like rules-based master data management (MDM) struggle to keep pace with the increasing size and complexity of modern data. That's why many businesses are exploring new, AI-driven strategies to deliver the trustworthy insights decision-makers need to drive the business forward. But what, exactly, does AI in data management look like?

How Businesses Use AI in Data Management

AI holds the power to transform data management by accelerating the pace at which organizations can discover, enrich, and maintain trustworthy golden records. Using AI, organizations can:

Boost data quality

Dirty data is a pervasive problem in modern business. But improving data quality is hard work. For decades, businesses have relied on rules-based master data management to deliver the level of data integrity needed to create the proverbial "golden record," a single, authoritative, accurate version of a business entity’s data across multiple data sources and datasets. But because MDM solutions were built for static data, they fall short on their promises.
AI, on the other hand, is designed to work with dynamic, ever-evolving data. Powerful algorithms spot anomalies and automate processes that clean and validate data, enabling organizations to finally deliver on the promise of golden records.

Drive data integration

Data silos are another common challenge organizations face. When critical business data is trapped in disconnected, disparate systems, it's difficult for decision-makers to gain a comprehensive, 360-degree view of the entities that matter, such as customers, suppliers, or patients. Using AI, organizations can automate and streamline data integration, making it easier and faster to map data, resolve conflicts, and improve consistency and integrity.

Improve entity resolution

Many organizations rely on outdated technology to resolve records across various systems and sources, slowing down time to value because these solutions simply can't scale as data evolves. But when organizations use AI with human refinement for entity resolution, everything changes. AI provides the speed and scalability needed to match records by creating persistent, unique IDs, while humans provide critical oversight to ensure the matched entities are, in fact, the same.

Expedite data enrichment

Most organizations today recognize that the best version of their data may not live within their firewall. And in order to ensure that their data is accurate and up-to-date, they must enrich their data with trusted, external sources. When AI is part of the data enrichment process, enrichment moves faster, employing machine learning-driven referential matching that identifies matches that are impossible to spot without external data.

How Will AI Impact Data Management Roles?

While it's clear that AI improves business processes in ways that can lead to better business outcomes, what is the impact of AI on people within the organization?

First and foremost, effective AI requires human refinement. When AI is refined by humans, it not only acknowledges the valuable role that human expertise plays in ensuring the quality and trustworthiness of the AI, but also provides a critical layer of oversight and refinement that complements the AI's capabilities.

Next, AI can automate repetitive, mundane, time-consuming tasks, freeing up valuable resources to focus their time and energy on more strategic, value-add projects. In turn, this shift could drive higher levels of employee satisfaction and provide more opportunities for career growth.

Finally, AI is opening up new opportunities and new roles. Some forward-thinking organizations are adding Chief AI Officers to their leadership teams to help them capitalize on the business opportunities AI presents, navigate the complexities AI introduces, and avoid missteps along the way.

The Transformative Power of AI in Data Management

AI holds the power to revolutionize data management. By using AI to deliver accurate, comprehensive, and durable golden records that represent key business entities faster and more effectively than traditional MDM solutions, organizations will achieve greater levels of competitiveness and financial success than they have in the past.

Tamr uses AI and machine learning to tackle the hard problems in data management, including how to deliver high-quality data, eliminate data silos, and perform accurate, enterprise data entity resolution at scale.

To learn more about how we use AI in data management, download our latest e-bookFaster, Cheaper, Better: Why AI-Powered, Human-Refined Golden Records Outperform MDM. In it, you'll learn why leading organizations are embracing advanced AI and human refinement to propel their businesses forward.