What is AI-powered Data Quality?

AI is reshaping industries and dominating conversations across organizations. Yet, with innovations emerging rapidly, many struggle to understand the nuances between popular AI technologies—and even fewer fully grasp how crucial data quality is for achieving AI success. This blog post defines key AI terms and explains why high-quality data, supported by robust data governance, is foundational for realizing AI’s true potential.

Key Takeaways

• Clearly understand AI, generative AI, and machine learning and how each relies directly on data quality.

• Recognize data quality as essential for AI to meet business expectations, requiring solid data governance.

• Learn how AI-powered tools significantly simplify data quality management, empowering organizations to deliver trusted data for AI initiatives.

Defining AI, generative AI, and machine learning

In one 2024 calendar quarter, over 40% of S&P 500 companies mentioned “AI” in earnings calls. Outside of AI’s many business benefits, simply uttering the term has value: two-thirds of companies mentioning “AI” in earnings calls saw a stock price increase.

Regardless of AI’s buzzworthy status, many professionals remain behind the hype curve on what AI is and how it works. Below is a quick AI primer to help bring readers up to speed before it’s time to see how AI and data quality are so intertwined. But, to make it interesting, it was up to AI to craft these definitions.

What is artificial intelligence (AI)?

Microsoft Copilot answered this question by saying, “Artificial Intelligence refers to the simulation of human intelligence in machines that are designed to think and learn like humans.”

AI covers a wide range of computing, including machine learning, natural language processing, computer vision, and robotics. An example of AI is the autocorrect function on your smartphone, which corrects spelling and grammar errors.

What is machine learning (ML)?

Google Gemini answered this question by saying, “Machine learning is a subfield of AI that focuses on enabling computers to learn from data without being explicitly programmed.” 

ML uses algorithms to analyze data, identify patterns, and make predictions based on that data. It can also learn from its output and become better over time. An example of ML is image recognition, which identifies objects or people in images, like how a doorbell camera recognizes a person at the door.

What is generative AI? 

Anthropic Claude answered this question by saying, “Generative AI refers to artificial intelligence systems that can create new content - including text, images, code, music, and videos - based on patterns learned from training data.”

Generative AI uses ML to generate new content based on training data. An example of generative AI is any one of the tools used above to develop answers to natural language questions. 

Why data quality is crucial to AI success

AI's effectiveness depends directly on the quality of data it receives. Poor data quality—data that’s incomplete, outdated, or incorrect—can severely impact AI outcomes. Imagine training an AI-driven recommendation engine on inaccurate sales data; the results would be misleading at best, and potentially harmful to business decisions.

Data quality extends beyond accuracy, encompassing completeness, consistency, integrity, timeliness, and uniqueness. Achieving high data quality requires a comprehensive data governance framework, which guides how data is captured, used, and maintained.

For AI to deliver trusted outcomes, it must have access to high-quality data that's aligned to the use case. If AI developers—and data scientists, data analysts, and workers across the company—use low-quality data, they risk crafting AI models that will make bad decisions and waste resources, lead to missed opportunities, or worse.

How data governance contributes to AI success

Data governance is how organizations create and manage standards and policies for collecting, storing, and sharing information. It covers everything from data privacy and security to data access and regulatory compliance. Key roles are also assigned to create responsibility for setting, managing, and improving data governance efforts.

Data governance has four pillars: data quality, data privacy/security, data management, and data governance frameworks and policies. Data quality is a core component of data governance, where the latter provides the data governance framework and infrastructure for setting data quality standards and evaluations, establishing procedures for cleaning data, and influencing data management practices to meet data quality standards.

Now the story comes full circle:

• AI relies on data to deliver beneficial outcomes. 

• That data must be of high quality for AI to be successful. 

• High-quality data relies on sound data governance efforts. 

Therefore, AI success requires a robust data governance program. 

Introducing Alation’s AI-powered Data Quality Agent

To help organizations effectively tackle their data quality challenges, Alation recently introduced its AI-powered Data Quality (DQ) Agent. This innovative solution leverages AI and metadata intelligence to proactively identify, prioritize, and remediate critical data quality issues.

How Alation Data Quality addresses top data quality challenges

Organizations commonly face three primary data quality challenges:

1. Prioritizing critical data amid high volumes. Massive data volumes often overwhelm teams, making it difficult to identify which data assets deserve immediate attention. Alation’s DQ Agent leverages metadata insights—like usage frequency, lineage, and governance context—to automatically prioritize the most impactful datasets, significantly reducing alert fatigue and ensuring teams focus on data that matters most.

2. Restoring trust through reliable data. Poor data quality undermines trust between data producers and consumers, causing delays and inefficiencies. Alation’s DQ Agent rebuilds confidence by continuously assessing data quality through automated accuracy, completeness, and consistency checks. Real-time insights integrated directly into user workflows empower confident decision-making.

3. Streamlining data quality management across silos Traditional data quality management often involves manual processes, complex configurations, and siloed tools. Alation eliminates these inefficiencies by embedding data quality into its unified platform, automating rule generation, and providing seamless oversight of governance, lineage, and quality—ensuring comprehensive, cost-effective monitoring.

Alation’s Data Quality Agent is designed to complement, not replace, existing data quality tools. Through the Open Data Quality Initiative (ODQI), Alation enables seamless integration with best-of-breed observability solutions like Anomalo, Monte Carlo, and Soda. Organizations benefit from Alation’s native capabilities while maintaining the flexibility to expand their data quality ecosystem as needed.

The future is AI-driven, metadata-powered, and integrated

With AI potentially disrupting large workflows across every organization, it’s clear that governed, accurate data is a prerequisite for AI success. Using AI to manage, govern, and improve data quality ensures trusted data is accessible to AI development. Even more so, effective data governance ensures AI development and innovation are done responsibly, with trusted data and active AI governance.

Learn more about how Alation brings trust to AI initiatives with a foundation of governed, accurate data.