Improving data quality is a top priority for Data Engineering teams. Identifying critical data issues and notifying the specific business stakeholders to fix are both critical steps in the data observability journey. Often data engineering teams are caught in the middle. This can drain data engineering resources and lead to delays in getting the data corrected.

Ed Presz, Director of Data at small business insurance leader Pie Insurance, shares how his team built an incident detection and notification workflow with Monte Carlo and Slack to drive domain-oriented ownership of data quality for business stakeholders across their company. He'll discuss the process, culture, and technologies necessary to introduce self-serve incident resolution and share the positive impact this shift has had on his team and the business at large.

Data without trust is worthless—because the people inside your organization won’t use data if they don’t trust it. And worse, if they’re using bad data, they could come to the wrong conclusion when making a critical decision.

For more than a decade, data teams have been constantly answering the same repetitive and annoying questions from business users. The worst of these are arguably “Does this data look right?” or “When was the data updated?” or “How do I use this?”

In this session, you’ll learn how to establish a culture of data knowledge powered by new investments in Monte Carlo, so your data teams can automate all of those annoying questions away. From streamlining communications with your business teams around incident detection and resolution to exposing data status pages for everyone, learn how to ensure your people can confidently leverage data to impact outcomes – without all the hand-holding.

Trust between data teams and business users is hard won and easily lost. Modern data observability tools play a well-established role in maintaining that trust by focusing on data, AI, and pipeline reliability. Learn how Semantic Layers help maintain this data trust by maintaining consistent metric definitions, business logic and security policies between data sources and downstream applications.

Gen AI isn't the solution, it’s the UI. LLMs are powered by the context of the world, but running AI on your own data needs much greater specificity. And with that comes great data quality responsibility - requiring a good data model and human refinement.

The rise of Gen AI lowers the barrier to accessing data, bringing a greater risk for bad data, decisions, and outcomes. While nearly every data tool has some AI, the devil is in the details. Hallucination, non-deterministic results, and nuanced analysis require human-in-the-loop user experience. There’s too much hype and not enough how. Join this session for tips and tactics to help you guard against bad data robots and drive real value with AI.

What’s next for the future of data? Will GenAI bots eat me? When will my stakeholders stop pinging me? Who knows! But one thing’s for sure: Monte Carlo’s Reliability Pioneers have some thoughts! Join us for a wide-ranging panel discussion with data leaders from HubSpot, Collaborative Imaging, and Dr. Squatch about their predictions for the future state of data and AI and what this means for the future of our work.

Topics to be discussed: building reliable data stacks that can support GenAI use cases, consolidated vs. best-of-breed stacks, operationalizing data observability, and much more!