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A lot of work that once took analysts hours, from writing SQL queries to preparing reports and explaining trends, can now be assisted by AI.

That raises a genuine concern for people entering the field, and even for experienced analysts.

Is learning Excel, SQL, Python, and Power BI still enough? Or should analysts spend more time developing business understanding, statistical thinking, and the ability to question AI-generated insights?

I don’t think the traditional skills are disappearing, but expectations are clearly changing.

If you were starting your data analytics career in 2027, which three skills would you prioritize, and why?

AI agents are moving beyond answering questions. They’re increasingly being designed to trigger workflows, interact with business systems, and take actions with less human intervention.

That creates a difficult question: when an agent makes a decision based on incomplete data or misinterprets a situation, where should accountability sit?

Is it with the team that built the agent, the business owner who deployed it, or the person who approved its actions?

I’m curious how organizations are handling this in practice:

• Which actions require human approval?

• Who reviews decisions after an agent acts independently?

• Should the level of oversight depend on the potential impact of an error?

AI tools can now help with SQL queries, data cleaning, visualizations, and report generation. That changes the learning curve for people entering analytics, but it doesn’t necessarily remove the need to understand the data or the business problem. I’m curious how experienced analysts would approach this today. Would you prioritize SQL fundamentals, Python, business understanding, data modeling, or learning to validate AI-generated analysis?
If you were mentoring a junior analyst starting in 2026, which three skills would you ask them to develop first, and why?

Hi everyone,

I’m a student working on a small forecasting project where I only have around 18–24 months of monthly demand data. Since the dataset is quite limited, I’m unsure which forecasting approach would be the most reliable.

I’ve read about moving averages, exponential smoothing, ARIMA, Prophet, and even machine learning models, but I’m not sure if complex models make sense with so few observations.

Should I stick to traditional statistical methods, or is there a better approach for small datasets? Also, how do you validate the model when there isn’t much historical data available?

I’d really appreciate any advice or resources that could help me understand the best practices for this kind of problem. Thanks!