George Earl

We're facing more and more questions about the use of synthetic data in market research. I'd recommend this blog post, which really resonated with me. So much of the conversation is "how closely can this replicate human data?" But there are so many other considerations researchers should be thinking about. https://lnkd.in/eSQfSTPj

31

3 Comments

A plea for data literacy education! 🧑‍🏫 In this interview, Dr. Joe Perez explains why companies can no longer avoid investing in their employees’ data skills and outlines how to approach this effectively. 👉 https://lnkd.in/eEAxPpJM #tgoa #thegroupofanalysts #knowledgeworkers #dataliteracy

2

Excited to be hosting a free webinar with NACM (National Association of Credit Management) on AI prompt engineering for modern credit teams Tuesday, March 31 at 3:00 PM ET. The session is based on a whitepaper Handle and NACM co-developed: "Turning One-Off AI Chats into Reliable Credit Workflows." Here's the thing...most credit professionals are already using ChatGPT or Claude. They open a tab, ask a question, get an answer that ranges from useful to generic, and close the tab. Nothing saved. Nothing repeatable. In this session, I'll walk through: ❓ The four-question framework that turns generic AI output into near-ready drafts 📄 Six named prompt patterns mapped to everyday credit tasks (collections, financial analysis, contract review, disputes) 📅 A 90-day plan for building a shared prompt library across your team (no IT involvement, no new software) If you manage credit risk, write collection letters, review contracts, or prepare escalation memos...this is practical, immediately usable content. Free Registration: https://lnkd.in/gxEFj8Ac

4

A recent Amazon paper on Insight Agents is an example of the agentic AI patterns and agentic analytics systems I’ve been writing about recently. The system follows an orchestrator–worker pattern: a manager agent handles intent detection, routing, and control; specialized agents perform data retrieval and insight generation; and LLMs are used primarily for reasoning and synthesis, not execution. Analytics is treated as a plan-and-execute workflow, with governance built directly into the architecture through out-of-domain detection, API-based data access, and explicit control flow. Evaluated via human review, the system achieves ~90% accuracy with interactive latency (~14s P90)—showing that agentic analytics can be both reliable and operational at scale when orchestration and constraints are first-class design decisions. Paper: https://lnkd.in/eE_tDkGc #AgenticAI #AgenticAnalytics #AIArchitecture

11

1 Comment

I’m moving from theory to practice on my #SyntheticDataJourney, developing benchmarks for synthetic outputs across established ML models and emerging LLM-based approaches. The first barrier we’ve uncovered isn’t model performance. It’s data structure. Data readiness is directly tied to the type of synthetic model you plan to apply. For traditional ML approaches, this is simpler. Structured numeric inputs often work, even when representing categorical variables. LLMs are different. They require context – A LOT of context. Not just metadata, but clarity around what each variable represents, how respondents interpret questions, how scales function, and how elements of the questionnaire connect. We can’t assume the model will “figure it out.” A clear mapping of question intent, response meaning, and structural relationships is table stakes. This has parallels to best practices with data governance and communication - if humans can quickly understand the data as a stand-alone product, LLMs are likely able to do the same. If you haven’t evaluated your organization’s data readiness for synthetic models, that may be the real starting point. Check out Tony Seale's post (link in comments) on AI readiness for another perspective. #MarketResearch #DecisionScience #DataQuality

16

2 Comments

Last week, I spoke at the Data Behind the Scenes conference #DataBS It's a conference run by Randy Au for data practitioners who are tired of polished case studies that skip over the actual work. Instead you get honest takes on project realities: the messy bits, the trial and error, the things that go wrong. I spoke about the 10 red flags that your multi-market research data went sideways. We've all had that moment, staring at survey results that look technically correct but feel fundamentally wrong, then having to explain why three countries' worth of data is unusable. Links to my talk, as well as the full playlist, are in the comments

16

5 Comments

Underpinning the mortgage industry with needle moving ai powered software and insights. Making the appraiser an AI superhero This new process will also drive better dynamic capture of data, data understanding resiliency, energy efficiency and making the process more streamlined

21

Warehouses were built for humans running reports—not agents firing evolving queries at ms cadence. We break down stateful scaling limits, manual tuning traps, and how agent-native engines close the gap (with MCP + StarRocks). Dive into the details: https://hubs.la/Q03DL_7D0 #Lakehouse #DataInfrastructure #DataAnalytics #DataEngineering

11

Tailored information awareness that highlights the edge we have at Y2. The dark leverage and Blue Owl liquidity mismatch help us stay aware of what is happening in the markets, using the cyber discipline of OSINT. Build your own tailored tracking, or subscribe to free global and community profiles, and more. Give it a spin. I’m grateful for all feedback on how we can improve or further tailor high-fidelity intelligence flows for you and your network. https://y2.dev

3

"AI developers bear obligations to improve attribution capabilities: efforts to trace and cite training data sources could help to preserve intellectual credit chains." https://lnkd.in/eKkp5u4s

1

Services as a Product (SaaP) is becoming central to how IT services create repeatable value and scale outcomes. Looking forward to the conversations at IDC Directions on how IT services continue to evolve in an AI‑driven economy. #IDCDirections

8

I had the pleasure of interviewing Bryan H. Kelly for the Dongmicus Blog — and now, he’s been featured in CDO Magazine as a thought leader in data literacy. Bryan is the author of Cass Freeman: Decision Trees, a compelling novel that fuses STEM education, ethics, and storytelling to inspire data leaders toward inclusive and accountable decision-making. It’s exciting to see his insights gaining global recognition. You can read my original interview with him on the Dongmicus Blog — link in the comments!

24

3 Comments