The Analyst Role Is Being Reborn. Here's What the New One Looks Like.
Senior Data Analyst
Alliance Health
Thais Cooke is a Senior Data Analyst and LinkedIn Learning instructor who transitioned into data after more than a decade in clinical healthcare. Her work sits at the intersection of analytics, governance, and AI adoption, with a focus on the human decisions that determine whether data gets used, trusted, or ignored.
She writes the newsletter "Journal of a Data Analyst", where she explores how AI is reshaping analytical work and how the analyst role is evolving alongside it. Her current focus is helping analysts integrate AI into their workflows in ways that improve decision-making, not just speed.
You got a request this week. You know the data, you have the tools, and with AI you could have something ready by end of day.
But speed didn't fix the real problem. It exposed it.
Right now, AI is handling the execution layer of analytics work: querying, cleaning, transformation logic, dashboard generation. That part of the job is shrinking.
But the work that happens before you open any tool, turning a request into the right request, is expanding and moving to the front of the line. That’s where analyst value is moving.
Built from real analytics requests and AI-assisted workflows, this session explores how the analyst role is shifting from execution-centered work to decision-centered work. Attendees will learn practical ways to:
- Use AI to research and prepare before the stakeholder conversation, so you walk in with the right questions instead of a blank page
- Identify which parts of any analytics request should stay human and which should be delegated to AI
- Interpret stakeholder feedback and use AI to iterate faster, without losing the judgment that makes the output trustworthy
The speed is not the problem. Building the wrong thing faster is.