Is Your Marketing Data Warehouse Ready for the Age of AI?

7/16/2025 Scott Bell

AI is rapidly reshaping how marketers interact with data, and your data warehouse could be the key to unlocking its full potential. 

As semantic query tools like Microsoft Copilot, Google Gemini, and Snowflake’s Cortex Analyst become more accessible, marketing teams no longer have to rely on SQL experts or dashboard builders to get insights. Instead, they can ask natural language questions to get clear, actionable answers in real time.

Semantic query tools enable users to query data using plain English rather than SQL or pre-built dashboards. They rely on large language models (LLMs) to interpret the intent behind a question and then map it to the right datasets and logic. For example:

• “What was our most profitable channel last quarter?”
• “How did email performance differ between segments A and B?”
• “Which products saw a spike in cart abandonment last week?”

Instead of routing these questions through an analyst, semantic tools generate answers by interpreting the meaning of your question and connecting it to data definitions, metrics, and logic within your warehouse. However, that interpretation only works if your data warehouse is structured to support it.

Traditional vs. AI-enhanced reporting workflows

Semantic query tools cut through the red tape. No more waiting days (or perhaps weeks) for a data analyst to pull a report or explain metrics. You can just ask a question in plain English and get instant, reliable insights. Here’s a look at how semantic queries can impact your workflows:

Traditional workflow

• Analysts write SQL Queries
• Reports built in Tableau, Power BI, or Looker
• Weeks to create new dashboards
• Limited to known metrics and KPIs

AI-enhanced workflow

• Marketers ask questions in plain English
• Responses returned instantly through LLM interfaces
• Minutes to generate insights from conversational queries
• Explore trends, anomalies, and patterns not pre-defined

This shift isn’t just a technical upgrade — it’s a cultural shift. It changes who can ask questions, how fast they get answers, and how decisions are made. Data becomes more usable and accessible, especially for marketers who previously relied on others to pull reports or interpret trends.

3 signs your marketing data isn’t ready for AI

Before you dive into semantic tools, check for these three signs that your marketing data warehouse may not be up to the task:

• Inconsistent or poor quality: If product, campaign, or audience data is full of gaps or duplicates, AI tools will provide incorrect insights or, worse, “hallucinate” answers
• Missing metadata and context: Semantic tools need context to interpret your questions. If tables, fields, and business logic aren’t properly documented or modeled, AI will struggle to generate the appropriate SQL
• Limited data accessibility and fragmentation: If data lives across disconnected systems, or requires manual stitching to create a complete view, natural language querying won’t get you very far

What AI-ready looks like

Here are the foundational elements that enable semantic query generation:

• Data quality and consistency: Clean, deduplicated records with enforced data standards
• Rich metadata and documentation: Data catalogs and semantic layers that describe what each table and field means, as well as how they relate to each other
• Access and discoverability: A centralized environment where business users can explore datasets without needing SQL
• Integrated systems: A unified customer, campaign, and engagement data mart across tools like CRMs, CDPs, and ad platforms
• Governance and compliance: Role-based access controls, audit logs, and policies that protect privacy while enabling exploration

Strategic questions to ask your data team

To kickstart your AI-readiness discussion, ask your team these three questions:

1. Can our marketing team access the data they need without asking engineering for help?
2. Have we defined the meaning behind our most-used metrics in a way AI tools could understand?
3. What’s stopping us from asking natural language questions today — tech, trust, or training?

AI won’t replace your marketing data team, but it will amplify their value. Semantic query tools open the door to faster insights, smarter testing, and more confident decisions. Getting your data warehouse ready is the first step.

Your data doesn’t need to be perfect. But it does need to be trusted, accessible, and well understood — by humans and machines.

Scott Bell works at the intersection of data strategy and technology at Iridio^℠ by RRD, enabling his clients to maximize their first-party data in order to connect more meaningfully with their customers. Interested in learning more? Contact our CX research team at research@RRD.com