Data Science ATL

Data quality is the backbone of effective AI implementation, yet errors, biases, and inconsistencies in datasets often go unnoticed until they derail key projects. Join this illuminating webinar to uncover how poor data quality affects AI accuracy, scalability, and ROI.

Our panel of experts will share actionable insights into recognizing hidden pitfalls, establishing robust data governance frameworks, and ensuring your AI initiatives yield impactful results. This session is essential for professionals aiming to maximize the value of their AI investments while navigating common data challenges.

Key Takeaways:

• Understanding Data Quality Risks: Learn how errors and biases in data undermine AI model accuracy and decision-making.
• Strategies for Robust Data Governance: Discover practical approaches to ensuring data consistency and reliability.
• Optimizing ROI from AI Initiatives: Explore methods to enhance AI performance and scalability by improving input data quality.

Panelists ti be announced soon

Join us for GenAI Demo Day—an exclusive virtual event showcasing the most innovative Generative AI solutions through concise 10-minute demos.

At DSC, we value your time and have designed this event to provide impactful demos without any unnecessary fluff. Quickly identify solutions that meet your business needs and connect directly with the founders and top leaders of pioneering GenAI companies.

What You'll Learn:

1️⃣ Innovative Solutions: Discover the latest advancements in Generative AI through targeted, high-impact demos.

2️⃣ Direct Engagement: Gain valuable insights and ask questions directly to the founders and top leadership of cutting-edge GenAI companies.

3️⃣ Enhanced Capabilities: Explore how these technologies can enhance your organization’s capabilities and drive innovation.

Don't miss this opportunity to experience the best of GenAI in a focused, efficient format tailored for decision-makers like you. We understand that everyone loves discovering new solutions, but nobody likes the hard sell.

Mark your calendars and get ready to dive into the future of AI!

As AI systems continue to scale, observability has become an essential component for success. This webinar explores two key dimensions: data observability, which focuses on monitoring and improving the quality of data that drives AI systems, and AI observability, which provides transparency and accountability for model performance and decision-making.

Learn practical strategies for building "glass box" AI systems where insights are clear, issues are easy to diagnose, and scalability is seamless.

Key Takeaways:

• Understand Data Observability: Learn how to implement monitoring systems to ensure high-quality, reliable data pipelines for AI systems.
• Explore AI Observability: Discover tools and techniques for monitoring AI models, ensuring they are transparent, unbiased, and explainable.
• Bridge the Gap: Integrate data and AI observability practices to achieve robust, scalable, and trustworthy AI deployments.

Panelists to be announced soon

Most conversations about automating data workflows center on the engineering stack—but that’s only part of the story. The real payoff comes downstream, where data scientists, analysts, and business leaders need fast, reliable access to data to make informed decisions. In this session, the Matillion team will demonstrate how an agentic system like Maia shifts repetitive data tasks into autonomous execution, freeing up teams to focus on high-impact analysis and modeling.

We’ll walk through how this shift reduces backlog, accelerates time-to-insight, and increases trust in the data that drives both machine learning pipelines and executive dashboards. Through real-world examples, attendees will see how agentic workflows empower the entire data organization—not just engineering.

Key Takeaways:

1️⃣ Workload Lift for Analysts & Scientists: How automation reduces manual prep and speeds up modeling cycles

2️⃣ From Backlog to Business Value: How agentic orchestration gets more data into production, faster

3️⃣ Organizational Trust in Data: Why agentic systems improve confidence in the data used for decision-making

4️⃣ Cross-Functional Impact: How these changes improve collaboration between data engineering, analytics, and leadership teams

PANELISTS TO BE ANNOUNCED SOON