Alice Zhao

The data analysts checklist posted by Esther Anagu, MBA was very helpful. here's another inghtful piece for data scientists.

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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

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At my recent talk at the DataConnect Conference in Ohio, I ran a quick live poll asking attendees: 👉 “What one word best describes how you feel about AI’s future?” The most common responses like “Hopeful,” “Yes, definitely,” “Bright,” and “Curious” were joined by others like “Wary,” “Skeptical,” and “Scary.” It was a fascinating snapshot of how even among data and tech professionals, optimism about AI’s potential sits right alongside caution about its direction. That mix of curiosity and concern feels especially relevant right now. Recently, the Future of Life Institute released a new open letter calling on governments to pause or prohibit artificial superintelligence (ASI) development until it can be proven safe and democratically approved. The letter, signed by 865 people, including Nobel laureates Geoffrey Hinton and Steve Wozniak, warns of risks such as loss of control, erosion of rights, and economic disruption. It urges enforceable governance before AI advances any further. 💬 We’re collectively navigating the tension between excitement and responsibility. The future of AI will depend not just on what we can build, but what we choose to safeguard along the way. So I’ll pass the question to you: What one word best describes your feeling about AI’s future? #AI #ArtificialIntelligence #DataConnect #ResponsibleAI #EthicsInAI #TechLeadership #TrustInTech

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This resonates with what I've seen supporting state and federal agencies: culture consistently outpaces technology as the driver of analytical maturity. The finding about system upgrades temporarily reducing analytical capacity is particularly sharp — implementation energy crowds out the insight work the new infrastructure was supposed to enable. What I'd add from the field: the communication gap isn't just between analysts and leadership — it often lives between the people who define the data and the people who query it. When those groups aren't in regular conversation, you get technically correct outputs answering the wrong questions. That disconnect is quiet and slow, and rarely surfaces until something downstream breaks.

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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

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Jason's done a fabulous summary of the panel I was proud to be part of today. Why should higher ed. institutions consider creating a Chief Data Officer role? Because it will help them to get value from their data.

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Network - I need some advice on the best AI coding/utilization for analytics courses (credentials, short term/long term) for a high level data analyst looking to skill up. Any ideas would be appreciated!

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