Caitlin Hudon

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

The Databricks Startups team answers founders’ most common AI questions—where to start without hype, how to stay compliant with sensitive data, what to build versus buy, how to cut hallucinations, and how to keep costs in check. 💡 Get step‑by‑step starting points, governance patterns for sensitive data, a pragmatic build‑versus‑buy lens, tactics to curb hallucinations, and cost controls that scale. 🔗 Full post: https://lnkd.in/ehRa3Yp5 #Databricks #DatabricksForStartups #AI #GenAI #MLOps

63

Your Data Stack Isn’t Broken. It’s Bloated Most data teams didn’t fail. They accumulated. More tools. More layers. More friction. What if the answer isn’t adding another platform but resetting the architecture entirely? Over the next 30 days, I’m breaking down why Microsoft Data Fabric is forcing a serious rethink. If your data stack feels heavy, this series is for you. 📩 datafabric@quadranttechnologies.com 📍 FabCon | Atlanta | March 16–20 Ram Paluri, MBA | Vamshi Reddy | Bhaskar Gangipamula | James Kass | Prakash Nagarajan | Lavina DSilva | Mithun P N | Dr. Madhavi Gundavajyala | Siva Kanuru | Varsha Panguluri | Ajay kumar Erukulla | Sivani Pamidi Microsoft Microsoft Fabric Quadrant Technologies #MicrosoftFabric #FabCon #DataPlatform #DataArchitecture #EnterpriseData #ModernDataStack #Analytics #DigitalTransformation

120

1 Comment

❄️❄️❄️Did you know that, once you've created a TASK in Snowflake, the default state is "suspended"?❄️❄️❄️ This is a built-in safety feature. A suspended task is inactive and won't run on its schedule, which prevents it from executing accidentally before you're ready. For a task to run on the desired schedule, you have to explicitly activate it using the ALTER TASK statement. If you don't run the ALTER TASK ... RESUME; command, your task will never run! If you're a data professional, this is a key detail to remember. What other surprising Snowflake features have you encountered? #Snowflake #DataEngineering #SQL #DataWarehouse Here’s a simple code example:

7

Dr. Ramla Jarrar, an MMM expert, shares an important perspective every marketer using MMM should read. The customer journey is complex, and while MMM is a powerful tool for understanding how spend impacts that journey, its limitations are often overlooked and nuances brushed over once results are packaged in polished decks. The tension I often see: marketers need one strong KPI to track large marketing investments, but the complexity of measurement means no single framework provides that holy grail metric. Measurement methodologies are sophisticated and technically robust, yet each has constraints. Triangulating across multiple frameworks—MMM, incrementality testing, attribution, brand tracking—provides better insights, though interpretation remains as much art as science. This requires partnership. Marketers need to understand what each methodology can and cannot deliver. Analytics partners, whether internal teams or external consultancies, have the responsibility to explain assumptions, acknowledge limitations, and help interpret results responsibly. When both sides engage with this complexity honestly, we make better decisions. A question for marketers: where do you find the biggest gaps between marketing performance measurement and the questions you need answered? #MarketingMixModeling #MMM #MarketingAnalytics #MarketingMeasurement #MarketingROI #DataDrivenMarketing #MarketingStrategy

1

I struggle to write this post because the content is self-referential. It’s genuinely difficult to succinctly capture the potential of agentic workflows. But here is the breakthrough: Today I integrated Mixpanel’s MCP into Claude Code. On its own? A nice utility. Combined with my other agents? A closed-loop product team. My current stack: • Design System Agent: Reads/writes to Figma. • Design Reviewer Agent: Audits the output. • Product Manager Agent: Writes user stories and understands briefs. With the Mixpanel integration, I can now build a Measurement Agent that quantitatively tracks what we release. The Loop: The Measurement Agent feeds data to the PM Agent → The PM identifies friction → The PM writes targeted design tickets → The Design Agent executes. The hardest part isn’t the tech, rather, it’s not trying to do everything at once. The "surface area" of what's possible grows every single day. What a time to be alive. #AgenticAI #BuildInPublic #ProductManagement #MCP #FutureOfWork

9

4 Comments

Is your data team building custom analytics? Chances are, another team is building the exact same thing. This redundant effort wastes time and money. It’s time to think about who else needs to know and shift left with data. In this context, "shifting left" with data means you take your powerful, reusable analytical logic and partner with upstream data providers to build it directly into the source data product. Instead of 10 teams cleaning the same data, the logic is embedded once, for everyone. This requires a new way of thinking for both sides: 🔹 For Data Analysts: Your next great deliverable isn't just a dashboard, it's a business case to improve the source data for ALL users. You're not just a consumer; you're a data product innovator. 🔹 For Data Providers: Your most valuable R&D comes from your users. Create clear channels for feedback and actively seek out the high-value logic being built downstream. The result? 🎯 Higher-quality, more reliable data 💸 Less redundant work and more efficient use of funds ⚡ Faster time to critical insights It's a cultural shift from consuming data to co-creating it. #DataStrategy #FederalGovernment #DataAnalytics #DigitalTransformation #ShiftLeft #DataProducts

10

1 Comment

data engineering weekly just dropped issue 251. i've been reading this newsletter for years. it's one of the few sources that cuts through vendor noise. the data engineering space moves fast. new tools launch weekly. most die within 18 months. keeping up matters but filtering signal from noise matters more. that's why curated sources beat random twitter scrolling. here's what i've learned tracking this field: the fundamentals stay constant. batch vs streaming trade-offs haven't changed. schema management still breaks pipelines. orchestration remains the hardest problem nobody talks about. tools evolve but patterns repeat. i built my first production pipeline in 2012. spark was new. hadoop was king. everyone predicted sql would die. twelve years later sql runs 80% of analytics workloads. the hype cycle lies constantly. newsletters like this serve a specific purpose. they aggregate what practitioners actually use. not what vendors pitch at conferences. not what vcs fund this quarter. what teams deploy and maintain for years. the real value comes from consistency. reading weekly builds pattern recognition. you spot trends before they hit mainstream. you avoid tools that flame out. you catch migration paths before they become urgent. my advice: pick two or three curated sources. read them weekly. ignore the rest. depth beats breadth. understanding one tool deeply beats knowing ten superficially. subscribe to sources written by practitioners. skip anything that reads like a press release. your future self will thank you when the next shiny tool fails in production. https://lnkd.in/eXJ9ptZp

2