Agentic Data Management Glossary: AI Terms for Data Leaders
A practical guide demystifying essential AI terminology for data leaders, focusing on real-world applications rather than technical complexity, helping you separate genuine innovation from empty buzzwords in the rapidly evolving data management landscape.
Use algorithms to process the image and extract important features from it
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Use machine learning to classify the image into different categories
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Filter the images based on a variety of criteria, such as color, texture, and keywords
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Automatically group similar images together and apply a common label across them
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Convert the extracted features into a vector representation of the image
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Let's be honest — keeping up with AI terminology is exhausting. Every week seems to bring a new acronym or buzzword everyone suddenly expects you to understand. As a data leader, you're already juggling a thousand responsibilities. You're also supposed to know the difference between RAG and LLMs or explain why your company needs "agentic systems" in your next board meeting.
The AI Jargon Problem
We've all been there: in meetings where everyone nods confidently at terms like "retrieval-augmented generation" while frantically Googling under the table. Or trying to evaluate vendor claims about their "generative AI capabilities" and wondering if they're genuinely innovative or just fancy packaging for the same old tech (a previous team member used to call this putting lipstick on a pig).
The truth is, beneath the intimidating terminology are concepts that are becoming increasingly important to our daily work – the technology industry is being disrupted at a rapid pace. So, let's break down a few key terms which are a necessity in the new world we are entering…
Let's Break Down a Few Terms You'll Actually Care About
Artificial Intelligence (AI)
We hear this everywhere, but what does it mean for data management? At its core, AI is about systems that can perform tasks typically requiring human intelligence.
What this means for you: Instead of needing dozens of data professionals to catalogue sources, identify sensitive information, and apply governance policies, AI can handle these tasks at scale—letting your team focus on strategy rather than manual work.
Large Language Models (LLMs)
These are the engines powering many of today's AI applications—sophisticated systems trained on vast amounts of text that can understand and generate human language.
What this means for you: Your business users can now ask questions in plain English like "Show me customer churn trends by region last quarter" and get meaningful answers without needing to know SQL or bothering your data team for yet another report.
Generative AI
This is AI that creates new content—whether that's text, images, code, or data—based on patterns it's learned rather than following explicit programming.
What this means for you: When launching a new database, the system can automatically generate comprehensive documentation, relationship diagrams, and usage guides tailored to different audiences—saving weeks of manual documentation work.
Prompt Engineering
This is the art of crafting instructions for AI systems to get the specific outputs you want—essentially, learning how to "talk" to AI effectively.
What this means for you: Well-designed prompts enable your business users to get consistent, accurate answers from data systems without technical expertise, democratizing data access across your organization.
Retrieval-Augmented Generation (RAG)
This connects AI systems to your enterprise data sources, enabling them to access organizational knowledge when generating responses or performing tasks.
What this means for you: When analyzing a data quality issue, the system can pull relevant information from your governance policies, historical patterns, and technical documentation before recommending appropriate fixes—ensuring AI responses are grounded in your business reality, not generic information.
Agentic Systems
Strip away the jargon, and this is about software that can work independently—not just analyzing data but actually taking actions based on what it finds.
What this means for you: Imagine connecting to a new data source and walking away. The system discovers tables, figures out how they relate to your existing data, spots sensitive information, and sets up proper documentation—all while you're focusing on more strategic work (or finally taking that lunch break).
Agentic Data Management
This brings everything together—using autonomous AI agents to independently handle complex data operations including discovery, documentation, governance, and quality management with minimal human intervention.
What this means for you: Your small team can effectively manage thousands of data sources, as autonomous agents automatically profile datasets, identify sensitive information, document metadata, and implement governance policies without requiring constant manual effort.
Why I Created This Glossary
I built this glossary after one too many meetings where I kept seeing management or other stakeholders look at me like I was speaking a different language. I wanted something that would:
- Cut through the hype to explain what these terms mean.
- Focus on why they matter for real-world data management challenges.
- Help teams to evaluate vendor claims without getting lost in technical jargon.
The terms above are just a starting point. The full glossary covers 40+ terms that are reshaping how we manage data—from "Chain of Thought" to "Agent Guardrails" to "Knowledge Graphs."
Get the Complete Glossary
If you're tired of nodding along while secretly wondering what everyone's talking about, download the complete Agentic Data Management Glossary.
No marketing fluff. No technical jargon that requires a PhD to decode. Just practical explanations for data leaders who need to separate genuine innovation from empty buzzwords.
