Ensuring Your Data is AI-Ready at move(data) 2025
AI is revolutionizing industries, but behind every successful AI model is one critical factor: continuous data preparation. High-quality, well-prepared data turns AI potential into reality. Too often, organizations rush into AI projects without ensuring their data is truly up to the task, leading to inconsistent and unreliable outcomes.
At Data Layer, we’ve seen this challenge firsthand. Companies eager to implement AI often struggle with outdated, inconsistent, or incomplete data, which leads to unreliable results and missed opportunities. That’s why we’re thrilled to share that our CTO, Robert Konarskis, will be speaking at move(data) 2025, a conference dedicated to the hard work and innovation of data and AI practitioners.
What You’ll Learn
Robert’s session, “Everybody Loves AI, But Is Your Data Up to the Task? Practical Tips for Continuous Data Prep,” will focus on one of the biggest bottlenecks in AI adoption and data readiness. AI models are only as strong as the data they are trained on, and too often, that data is messy, unstructured, and full of gaps.
This session will dive into:
✅ Automated ETL processes to streamline data flow
✅ Data normalization to ensure consistency across sources
✅ Real-time validation to maintain accuracy and security
✅ How to tackle privacy and compliance challenges in AI-driven environments
Robert will break down practical, real-world strategies that data teams can implement to ensure continuous data preparation—so your AI initiatives don’t get stuck in endless troubleshooting.
Why move(data) Matters
Move(data) is one of the most relevant events for data and AI professionals, bringing together thousands of experts to discuss the future of data infrastructure, pipelines, and AI adoption. If you’re working in data engineering, AI, or analytics, this is the place to be.
📅 Date: March 20, 2025
⏰ Time: 11AM PT/ 7PM CET
🔗 Register here, it’s free: movedata.airbyte.com
Let’s talk about turning AI ambitions into real-world success by getting data preparation right.
