The Data Scientist

Many enterprises believe they’re ready for AI once models are selected, platforms are deployed, and pilots are underway. But in practice, AI initiatives often stall before reaching production—or they underperform once they do. The root cause? The data isn’t actually ready.

In this thought-leadership session, Naveen Punjabi from Google Cloud and Jake Bengtson from Striim unpack four critical signs that your data foundation can actually support AI at scale. Drawing from real-world customer patterns, we’ll explore why centralized data isn’t enough, how invisible data errors derail AI outcomes, and why stale data threatens use cases like RAG and AI agents.

What we'll cover:

• What Google Cloud is seeing across enterprise AI adoption—and where data readiness tends to break down
• Four practical indicators your data is ready for trustworthy, production-grade AI
• Why validation and accuracy matter before any models get involved
• How real-time pipelines give AI access to operational truth
• How Google Cloud and Striim help enterprises move from AI experimentation to enterprise impact

This session is built for data, analytics, and AI leaders ready to move beyond AI experimentation—and build foundations that truly support scale.

What You'll Learn:

1️⃣ Spot the Warning Signs: Know when your data might silently sabotage your AI initiatives.

2️⃣ Go Beyond Centralization: Why a data lake isn’t the same as a data strategy.

3️⃣ Enable Live AI: How real-time pipelines can unlock true operational intelligence.

4️⃣ Validate Early, Scale Confidently: Why data quality must be addressed before model deployment.

5️⃣Learn from the Field: What Google Cloud and Striim have seen across real enterprise AI journeys.

As AI evolves from passive tools to active collaborators, enterprises face a pivotal shift: embracing systems that can not only respond but also take initiative. This webinar unpacks what it means to build and deploy agentic AI—models that possess goal-directed reasoning, dynamic planning capabilities, and the ability to autonomously execute tasks across complex workflows.

We’ll explore architectural and governance considerations for enterprise adoption, including how to integrate agentic systems into existing data ecosystems, ensure transparency and control, and maintain human-in-the-loop oversight. Join us for a practical, forward-looking discussion on how to prepare your organization for AI that doesn’t just assist—but acts.

What You'll Learn:

1️⃣ What Agentic AI Really Means: Understand the core components—reasoning, planning, and autonomous action—and how they differ from traditional AI assistants.

2️⃣ Enterprise Readiness: Learn what technical and organizational infrastructure you need to safely and effectively deploy agentic systems.

3️⃣ Designing for Oversight and Control: Explore observability, guardrails, and human-in-the-loop strategies for managing proactive AI in production environments.

Panelists to be announced soon.

Explore the real-world impact of Agentic AI in our fast-paced, insight-packed virtual showcase. Each 10-minute session features a live demo from a leading innovator in the Agentic AI space—highlighting practical applications, dynamic workflows, and tangible business outcomes.

This isn't just another pitch session. It’s a curated look at how agent-based systems are driving intelligent automation, adaptive planning, and cross-functional orchestration across industries. You’ll meet the minds behind these technologies and see firsthand how they’re redefining what's possible in enterprise AI.

What You'll Learn:

1️⃣ Autonomous Workflows: See how agentic systems plan, coordinate, and execute complex tasks across tools and teams.

2️⃣ Dynamic Decisioning: Explore examples of contextual reasoning, goal alignment, and memory-enhanced agents in action.

3️⃣Practical Integration: Understand what it takes to embed agentic AI into your stack—without overhauling everything.

Engage live with founders and builders shaping the future of autonomous enterprise solutions.

You’re short on time but deep on curiosity. This format respects both—cutting straight to the tech and the teams behind it. No filler, no fluff. Just signal.

As AI adoption accelerates, many enterprises are hitting the same wall: traditional approaches to data governance, metadata management, and cataloging were not designed to support AI and AI agents operating at scale. Fragmented tools, siloed semantics, and inconsistent definitions make it difficult for both people and AI systems to trust and act on data.

In this webinar, industry expert Mike Ferguson and Collate co-founder and CEO Suresh Srinivas explore how leading organizations are evolving from tool-centric data catalogs and governance toward semantic intelligence, a unified approach that brings governance, context, and meaning together to support analytics, automation, and AI at scale.

Collate co-founder and CTO Harsha Chintalapani will also provide a brief demonstration showing how semantic intelligence is applied in practice, including how data teams design and build AI agents on governed enterprise data.

What You'll Learn:

1️⃣ Why data discovery, governance, lineage, and data quality and observability must work together to support AI-driven use cases

2️⃣ How fragmented semantic layers and tool-specific context limit trust, scale, and reuse across analytics and AI

3️⃣What semantic intelligence means in practice and how it extends beyond traditional data catalogs

4️⃣How shared meaning enables secure, governed access to data for both people and AI agents

5️⃣How semantic intelligence supports the delivery of trusted, consumable data products with meaning, quality signals, and governance that are ready for analytics and AI agents

6️⃣What it takes to make data truly AI-ready without sacrificing control, compliance, or consistency

Designed for data leaders, enterprise architects, and governance teams, this session explains why existing catalogs and governance tools remain useful—but are no longer sufficient on their own—and how enterprises can operationalize shared meaning to support analytics, data engineering, and AI initiatives at scale.