Data platforms were designed for people, but they are increasingly being driven by AI agents. This shift is becoming a major data governance challenge.

Recent security incidents have highlighted how quickly AI-powered activity can scale, sometimes compromising hundreds of organizations simultaneously. While the headlines usually focus on cybersecurity, data leaders need to address the governance side.

How is your team adapting to this?

• Access Rules: Are you creating separate credentialing and schema restrictions specifically for AI agents?

• Auditing: How are you monitoring autonomous queries versus human-led analytics?

• Resource Management: Have you implemented specific rate limits for agentic workflows to prevent system strain?

I’d love to hear how other data leaders are managing this transition!

As robotics deployments grow, advanced analytics is moving beyond basic monitoring.

Which use case is creating the biggest impact in your experience, and where do you see the next major opportunity for analytics in robotics? 

I’m working on an advanced analytics project where the goal is to estimate the impact of a business intervention (pricing changes, targeted campaigns, and operational improvements). The challenge is that there’s no randomized control group, and historical data is affected by seasonality, market trends, and multiple simultaneous interventions.

I’ve experimented with propensity score matching and difference-in-differences, but I’m struggling to determine whether the estimated treatment effect is genuinely causal or simply capturing hidden confounding variables.

For those who’ve worked on large-scale causal inference problems:

• How do you validate your model when a true counterfactual doesn’t exist?
• Do you rely on synthetic controls, placebo tests, sensitivity analysis, or another validation framework?
• How do you communicate confidence in your results to stakeholders when you can’t directly verify the “ground truth”?

I’m interested in hearing how experienced data scientists approach this in production environments, particularly when the results drive high-value business decisions.

Mine was building a forecasting model for a retail dataset. I went in thinking the model would be the hard part, but I ended up spending 80% of my time cleaning data, engineering features, and figuring out why my assumptions were wrong.

Curious to hear yours. Was it forecasting, churn prediction, anomaly detection, recommendation systems, causal inference, or something else? What was the biggest lesson you took away?