I need to say something that makes some data analysts uncomfortable: the job is changing. Not disappearing — changing. And the analysts who understand the change will thrive. The ones who don’t will spend the next five years fighting it.
Here’s what I’ve learned building analytics agents at Cloudflare.
What’s Actually Changing #
The fear is usually: “AI will write SQL, so SQL skills become worthless.”
That’s wrong. Here’s what’s actually happening:
The volume of SQL is exploding. Ten times more queries will be run by organizations in 2027 than in 2024. Most of those will be generated by agents, not humans. But someone has to:
- Define what the queries should look like
- Verify that the answers are correct
- Encode the domain knowledge that makes answers trustworthy
- Build the evaluation systems that catch errors
- Explain what the data means to stakeholders
That’s the data analyst’s job. It just got more important, not less.
The New Skills That Matter #
1. Evaluation design
The most valuable skill in analytics agent development is building evaluation datasets. This means curating real questions, verifying correct answers, and designing metrics that distinguish good responses from bad ones. It requires deep data knowledge. It’s not automatable. And most teams are terrible at it.
If you build evaluation datasets well, you become indispensable.
2. Data contract design
Agents need to know what data is reliable, what’s sampled, what’s experimental, and what the business definitions are. Defining and documenting these data contracts — the “analytical truth layer” — is the domain of the data analyst. Do this well and every agent in your organization depends on your work.
3. Prompt and tool architecture
Writing agent system prompts is a new craft that combines:
- Deep domain knowledge (what does your data mean?)
- Clear technical specification (what can the agent do?)
- Edge case awareness (what will go wrong?)
Data analysts have the domain knowledge. They just need to learn the new syntax.
4. Stakeholder translation
An analytics agent that produces a technically correct answer but can’t explain it to a PM is useless. Someone needs to translate between the agent’s output and business decisions. That’s a data analyst job.
The Mindset Shift #
The analysts I’ve seen struggle with agents share a common mindset: they see the agent as a replacement for their skills. Write SQL → agent writes SQL. Create analysis → agent creates analysis. Therefore: I’m obsolete.
The analysts who thrive have a different mindset: the agent is a multiplier on my domain knowledge. I know what our traffic data means. The agent is fast and tireless. Together, we can answer 10x more questions than I could alone — and the hard questions still need me.
Your value is your judgment, your domain knowledge, and your ability to catch the agent when it’s wrong. Those things are not automatable. They become more valuable as agents handle routine work.
The Concrete Action Plan #
This week:
- Pick one analytics question you answer repeatedly — manually, weekly, quarterly
- Write down the exact query you use and why
- Note every “gotcha” — the sampling issues, the definitions, the edge cases
This month:
- Build a 10-question mini-evaluation dataset for that question type
- Try one open-source analytics agent framework (smolagents, LangChain, Cloudflare Agents SDK)
- Run your 10 questions through it. Measure accuracy.
This quarter:
- Identify your organization’s most common analytics questions
- Document them in a structured format: question, expected answer type, required data, known gotchas
- Present a proposal: “Here’s what an analytics agent for our team could answer automatically.”
This year:
- Own the evaluation system for your team’s analytics agent
- Become the person who catches agent errors before they reach stakeholders
- Write the internal cookbook — the documented patterns that work
The era of analytics agents is not coming. It’s here. The question is whether you’ll lead the transition or follow it.