Kate Strachnyi

If your datasets are poorly labeled, you’re inviting a host of issues like noise, bias, and inconsistencies that can seriously hurt your model's accuracy and reliability when it’s out in the wild. That's why data labeling has evolved from being just a preliminary step into a vital part of engineering and operations in the AI lifecycle.

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Don't let your data and AI sit on the sidelines—it's time to put them into the game and leverage the power of agentic AI. 🏈 Looking to adopt and scale trusted AI in your analytics workflows? Our new 12-step guide provides a checklist for data leaders to help you transform your role and ignite value-driven AI use cases. 🔍 A new era of analytics has arrived, and it's time to lead the transformation. Download the guide to get the playbook for a winning data strategy. https://lnkd.in/efV6KuPG

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🚨 Survey fraud is getting smarter. Are you? Steven Snell, PhD, Head of Research at Rep Data, explores what 4.1 billion survey attempts reveal in the upcoming State of Fraud 2025 report. Steven and Karen Lynch discuss how fraudsters manipulate data, why inattention isn’t the same as fraud, and what every researcher can do to stay ahead with smarter design, stronger prevention, and cleaner data. 🎧 Watch now → https://hubs.ly/Q03QWhD20

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How do you turn data into direction? Sometimes, it starts with the right question. And at Artemis, you have more than just a partner. You have an extension of your team. Principal Analytic Advisor and Director of Analytic Consulting, Danielle Barrieau, shares about how she gets to help clients uncover their real needs and move forward with clarity 💙

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If you’re feeding your analytics and AI tools fragmented demographic data, you’re not building equity—you’re building risk. One of our healthcare clients had multiple sources of race and ethnicity data that didn’t align with CDC standards. That’s a compliance risk. It’s also an AI risk. We helped them clean, map, and prioritize their data across lines of business—making their HEDIS reporting not just compliant, but trustworthy. Outcome: Better reporting. Greater accuracy. A real foundation for personalization and AI. 👉 See how they did it: https://hubs.la/Q03CxC_20

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Want to understand the data quality crisis in market research? We've been covering this critical issue since day one. Here's your listening guide: Ep 13 - Verisoul's Fraud Solution: Banking-grade fraud detection that protects data quality without blocking real respondents Ep 12 - Research Rant: Misusing Fraud Platforms: Why overly-aggressive fraud settings damage data quality and Brian's "Eight Steps to Winning" framework Ep 4 - Does Data Quality Even Matter?: The massive waste from fraud prevention, commoditization of sample, and whether brands should even care Ep 2 - The Slow Burn of Data Quality: Why researchers are tossing out nearly half their data and why middle-tier sample might be on its last legs Ep 1 - Taking the Industry by Storm: Setting the stage for honest conversations about industry challenges The data quality crisis isn't going away. These episodes break down what's broken and what needs to change.

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Generic AI chatbots are impressive. They’re quick, fluent and great for drafting a first pass of… almost anything. But market research summaries aren’t “almost anything.” Insight teams need something more: a way to turn complex research into consistent, decision-ready narratives, inside the workflow, at scale, with governance baked in. That’s what Enlyta’s Insights AI is built to do—help research teams accelerate discovery, improve understanding and simplify for insights to travel farther and influence more decisions. In a recent blog post, Abhinav Kothari shares six things to consider when choosing between purpose-built market research AI and using a generic chat bot: https://lnkd.in/giMeVSv9 #AIInMarketResearch #InsightsAI #MarketResearch #Insights #Enlyta #CSpace Escalent | Hall & Partners

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