Megan Lieu

Where’s data viz headed in 2025? New tools, new formats, and new expectations are reshaping how we design with data. These are some of the trends I’m watching closely (and already leaning into). 1) 𝗔𝗜-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗗𝗲𝘀𝗶𝗴𝗻: There’s lots of hype right around tools like bolt.new and Lovable right now. But the results still feel too basic and formulaic to me. I’m honestly more interested in tools like Relume and Visual Electric, which aim to enhance the design process in specific ways, not replace it. 2) 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝘃𝗲 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀: When Observable announced the debut of Data Canvases last week, I was stoked. We’ve had real-time collaboration in design tools like Figma for years. I’ve always wanted the equivalent for data teams and this feels like a big step. 3) 𝗔𝗻𝗶𝗺𝗮𝘁𝗲𝗱 𝗗𝗮𝘁𝗮 𝗦𝘁𝗼𝗿𝗶𝗲𝘀: More and more clients are asking us to create animated videos with their data. Think short GIFs (<30 seconds) featuring a slick chart and a single, sharp insight, designed to stand out in a crowded social feed. In a world driven by TikTok and short-form video, this makes sense creatively and strategically. 4) 𝗪𝗲𝗯𝗚𝗟 𝗮𝗻𝗱 𝗦𝗵𝗮𝗱𝗲𝗿𝘀: If you're looking to make your visuals pop, WebGL and custom shaders let you push the browser to its creative limits. We’re using them to build visuals that feel dynamic, immersive, and a little bit magical. 5) 𝗡𝗼-𝗖𝗼𝗱𝗲 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀: Tools like Webflow and Framer are democratizing web development and taking dev timelines from months to weeks. We’re leaning into this on specific projects more and more. 6) 𝗜𝗺𝗺𝗲𝗿𝘀𝗶𝘃𝗲 𝟯𝗗 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀: The rise of Three.js and Blender is helping 3D find its place in data storytelling. A few years ago, I would have dismissed this as a fad. But recent examples from NYT and Washington Post, where they’ve used 3D to great effect, have convinced me otherwise. Data people, what’s on your radar this year?

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Ever been told to just pick a lane?🚏 I’ve never really been a ‘one-tab-at-a-time’ kind of person. I work in Data, create Content, think in Product. I love structure- but also storytelling. Logic- but with a little intuition on the side. For a long time, I thought I had to choose just one to be taken seriously. But I’ve realized something: being multi-passionate isn’t about being scattered- it’s about being layered. It’s not chaos. It’s clarity, built across different domains. It’s empathy showing up in dashboards. Strategy told like a story. Design thinking applied to data. It’s not the typical “T-shaped” career path. It’s more like a well-organized mosaic. And somehow, it all fits. So if someone says “pick a lane,” maybe they’re just not used to roads with different designs! Still figuring it out. But finally, driving in the right direction.🍂

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Recruitonomics is on Substack! Check out Sam Kuhn's inaugural post (first link in comments) covering this morning's December jobs report. Going forward, all posts on the former Recruitionomics.com site will now live on Substack. Now, to the data: 50k jobs were added last month and the unemployment rate ticked down to 4.4%. But to abstract away from the effects of the government shutdown and the D.O.G.E.-related deferred resignations, private job growth is averaging just 29k over the last three months — less than half the pace seen in the Spring. Policy uncertainty (namely tariffs) and restricted immigration explain a lot of the decline in job growth. However, it doesn’t explain all of it. Bottom line: Hiring is really low outside of healthcare. For 2025 as a whole, about 600k jobs were added, the slowest pace (outside of recessions) since 2003. It's a "low-hire, low-fire" economy – not recessionary, and no major signs of imminent layoffs, but hiring is trending down, not up.

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Most data scientists are terrible at positioning. And it's quietly killing their careers... Last week, I cringingly looked back at my old email posts from 2022, as I prepared to migrate to my new email list provider. The experience has been eye-opening. What struck me most was that they all seemed so... random. For example: ✴️ What Robodebt Teaches Us About Responsible AI ✴️ Business Lessons From "Succession" ✴️ How to Avoid the Robopocalypse ✴️ Design Principles for Data Science (from the Victorian Government's Digital Strategy) It wasn't terrible content, by any means. But unless someone specifically needed an assessment of the Victorian Government's Digital Strategy, it was more "nice to have" than anything else. Looking back, it's unclear exactly what value I offered readers, because back then, I was unclear on what value I offered them - except, of course, "data science advice." 𝗛𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝘁𝗵𝗶𝗻𝗴... Many data scientists do the same in their careers. They position themselves as "data science experts" without articulating what specific value they bring. They say things like: ✴️ "I'm a machine learning specialist" ✴️ "I do predictive analytics" ✴️ "I'm passionate about data science" None of this tells a potential employer what problem you actually solve for them. Just like my old blog posts, it's "nice to have" information that doesn't clearly communicate value. The shift happened when I stopped thinking "what do I know?" and started thinking "what problems can I solve?" Instead of random data science commentary, I focused on helping data scientists create business impact and advance their careers. My audience doubled in 4 months. Instead of being "Genevieve the data science expert," I became "Genevieve who helps data scientists turn their technical skills into career growth." The same principle applies to your career: Don't just tell people you're a data scientist. Tell them what you can do for their business. Don't just list your technical skills. Frame them around the value they bring. What problems can you solve and who can you solve them for? Answer that question, and you'll become more than just "nice to have." #datascience #business #career --- 👋 If you enjoyed this, you'll enjoy my newsletter. Twice weekly, I share insights to help data scientists get noticed, promoted and valued. Click "Visit my website" under my name to join.

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I just analyzed 1,000+ job postings for data roles in 2026. Here's what I found. (The market has completely shifted.) Companies aren't hiring the way they used to. Welcome to the "precision hiring" era. Fewer roles, higher expectations, bigger paychecks for specialists. I used Superagent, a newly launched AI product from Airtable, to research this. Right away, multiple specialized AI agents worked simultaneously to pull from sources like LinkedIn Jobs, Glassdoor, and industry reports, to deliver an interactive 𝐒𝐮𝐩𝐞𝐫 𝐑𝐞𝐩𝐨𝐫𝐭 with salary benchmarks, skill trends, and hiring patterns. 𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐭𝐡𝐞 𝐝𝐚𝐭𝐚 𝐬𝐡𝐨𝐰𝐬: 𝟐𝟎𝟐𝟔 𝐒𝐚𝐥𝐚𝐫𝐲 𝐁𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤𝐬 (𝐌𝐢𝐝𝐩𝐨𝐢𝐧𝐭): ↳ Data Analyst: $117,250 (+3.3% YoY) ↳ Data Scientist: $153,750 (+4.1% YoY) ↳ Data Engineer: ~$145,000 ↳ ML Engineer: $170,750 (+4.4% YoY) 𝐓𝐡𝐞 𝐒𝐤𝐢𝐥𝐥𝐬 𝐒𝐡𝐢𝐟𝐭: ↳ 60% of Data Scientist postings now require AI skills ↳ LLM experience is the #1 demanded AI skill ↳ Python + SQL are still non-negotiable ↳ Cloud platforms (AWS, Snowflake) showing up in 41% of DS roles 𝐁𝐢𝐠𝐠𝐞𝐬𝐭 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬: → ML Engineer is the #1 fastest-growing role in the US (per LinkedIn) → Entry-level roles are shrinking. Companies want production-ready skills. → AI-skilled professionals command 5-12% salary premiums → The market isn't saturated, it's "misaligned". Employers want judgment, not just theory. 𝐌𝐲 𝟐 𝐜𝐞𝐧𝐭𝐬: The days of learning Python and landing a data job are fading. 2026 rewards specialists who can ship end-to-end, work with AI tools, and make decisions under uncertainty. If you're job hunting this year, focus on production experience over certifications. If you want to check out the full Super Report I generated, comment “𝐑𝐞𝐩𝐨𝐫𝐭” below and I'll send it to you. The full report includes interactive salary charts, geographic breakdowns, and top hiring companies. All with citations you can actually verify 𝐓𝐫𝐲 𝐢𝐭: https://lnkd.in/dBMd5wTU Use my code "VENKATA2FREE" to get 2 months free for Superagent! Which role are you targeting in 2026? 👇 ♻️ Repost to help someone plan their data career this year #SuperagentPartner Airtable Superagent

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I CLONED MYSELF WITH CHATGPT IN ONE HOUR Copy these 7 prompts and build your own: 1/ Define My AI Twin’s Role ↳ Prompt: “You’re now my AI twin. Your job is to handle [content writing, social media, client replies, etc.]. Define your daily tasks, tone of voice, and workflow.” 2/ Learn My Voice ↳ Prompt: “Here are 5 samples of my writing. Analyze them and mimic my tone, sentence structure, and personality. Always write like me from now on.” 3/ Handle Client Messages ↳ Prompt: “Respond to this client message in a way that’s friendly, professional, and keeps the conversation moving forward. Be me — but optimized.” 4/ Schedule Social Media Content ↳ Prompt: “Generate 7 social media posts in my voice for [insert niche]. Include hooks, call to actions, and keep each one engaging.” 5/ Write Emails Like Me ↳ Prompt: “Write an email to [client/prospect/team] in my tone. Make it feel personal but efficient, and always reflect my personality.” 6/ Handle Repetitive Tasks ↳ Prompt: “List all my repetitive digital tasks. Then tell me how you, my AI twin, can automate or streamline them.” 7/ Reply to Comments/DMS ↳ Prompt: “You’re managing my social media. Write 10 comment replies in my voice that are kind, witty, and boost engagement.” --- ✋ Want to Master ChatGPT? Here is “The Ultimate ChatGPT Prompt Collection”. Packed with 10000+ powerful prompts across 200+ categories to save you hours of work. Download Now → https://lnkd.in/gQJKYsuW --- 🎁 Bonus Alert... Stay ahead in the AI game with my newsletter, packed with the best: • AI news and updates • Tips, tutorials, and resources Subscribe now and unlock: ‣ 50+ AI courses ‣ 1200+ powerful prompts ‣ ChatGPT cheatsheets & more 100% FREE! 👉 https://lnkd.in/g2gcuR-T --- If you found this helpful, spare me a like/repost to support my content 🙏 Follow Saidul Islam for more resources like this 🔔

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I almost cheated on my own learning this week. I'm building my portfolio as a Business Analyst, and I decided to tackle a dynamic pricing analysis for a CPG brand. Real datasets, 30 stores, Excel-based scenario modeling. Everything was going well until I hit a roadblock. I was staring at "price elasticity coefficients" in my dataset: -0.4, -1.2, -0.8. I had read the theory. I understood the concept on paper. But when it came time to actually apply it to calculate demand changes, I froze. So I asked AI: "Can you just build the Excel workbook for me?" And then I caught myself. Earlier in the session, I had specifically said: "No solutions, let me try this myself." Why was I now asking for shortcuts? Here's what I learned after stepping back and working through it manually: - When I calculated demand for rural weekday customers (elasticity -0.4), a 10% price increase only dropped demand by 3 units. These customers are loyal. They won't leave. - When I tested urban weekend customers (elasticity -1.2), a 5% price decrease attracted 17 additional units. These customers are price-sensitive. Volume opportunity. The breakthrough wasn't in the formulas. It was in understanding human behavior through the math. But here's the uncomfortable truth: If I had taken that shortcut and let someone build it for me, I would have a polished Excel file but zero understanding.  And when an interviewer asks "Walk me through your thinking," I'd have nothing real to say. Portfolio projects aren't about having perfect outputs. They're about demonstrating how you think through messy, uncertain problems. The confusion is part of the learning. The mistakes are part of the story. I'm documenting this journey—the breakthroughs and the almost-shortcuts—because I think we need more honest conversations about what learning actually looks like in analytics. It's not always clean. It's not always confident. But it's real. For those building portfolios or switching into analytics, read my blog here : https://lnkd.in/gSzHw-zG Will be documenting more of my real learning experience to drive better learning RoI with AI. #LearningwithAI #DynamicPricing #BusinessAnalytics #PriceElasticity

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𝟗𝟎 𝐃𝐚𝐲𝐬 𝐢𝐬 𝐦𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐞𝐧𝐨𝐮𝐠𝐡 𝐟𝐨𝐫 𝐲𝐨𝐮 𝐭𝐨 𝐛𝐞𝐜𝐨𝐦𝐞 𝐚 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐬𝐭 𝐢𝐧 𝟐𝟎𝟐𝟔 (Yes, even if you’re starting from zero, non-tech, MBA, or career switcher background.) I’ve seen dozens of people go from “What’s SQL?” to landing interviews (and offers) in under 3 months. It’s intense, but realistic if you commit 2-4hrs/day consistently. The secret? Focus on high-impact skills recruiters actually test in 2026: • Excel + Advanced Power Query (your daily weapon) • SQL (Joins, Window Functions, CTEs – the #1 interview killer) • Power BI/Tableau (build dashboards that tell stories) • Real projects (3–5 business-focused ones) • Storytelling + STAR method (turn tech into business value) No need for Python mastery or ML right away – nail the foundations first. Here’s the quick 90-day sprint breakdown:
Days 1–30: Foundations (Excel + SQL basics to intermediate) – solve 100+ problems
Days 31–60: Visualization + Projects (Power BI mastery + build 3 end-to-end dashboards solving real problems like sales drop, churn analysis)
Days 61–90: Polish & Apply (portfolio on GitHub/LinkedIn, resume ATS-friendly, mock interviews, apply 10+ jobs/day) Many from non-tech backgrounds crack 15–25 LPA roles this way in 2026 – because companies want quick value, not degrees. If you’re serious, this is your year. No excuses. I’ve created a detailed video breaking down the complete 0–90 days roadmap: • Week-by-week plan • Free resources & datasets • Project ideas that actually impress recruiters • Interview tips to avoid common traps Watch it here: https://lnkd.in/dK6Tx-tE Like this post if you’re committing to the 90-day challenge ❤️
 #DataAnalytics #90DayChallenge #BecomeDataAnalyst #CareerSwitch #NonTechToData #SQL #PowerBI #DataAnalyst2026 #JobReady

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If your MMM flips every time you add a week of new data, it’s not a model. It’s a coin toss. Marketing Mix Models should adapt to new data, but they shouldn’t rewrite the story. Recast models use 2–3 years of historical data, plus the latest 7 days. When they’re refreshed, we quantify how much model outputs change. These stability checks measure the overlap in the interquartile range (25th to 75th percentile) for every parameter that impacts revenue or acquisition. Most weeks, we see ~85% overlap. That’s the sweet spot: ✅ New signal gets incorporated ✅ Confidence shifts modestly ❌ But the entire narrative doesn’t flip Because if it does flip, how can you make decisions? How do you go to your CMO and say: Our plan changed (again) because our model changed (again)? On the flip side of this: freezing coefficients is cheating. It hides volatility rather than fixing it. True model stability means evolving with new data, not breaking because of it. For more on the Recast model, check us out here: https://lnkd.in/eBTYYU2j

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In breaking news the new cycle volume is out +32.9% increase in applicants versus this time last year. But what does the data behind that data say? We take an early look: https://lnkd.in/g37fVbWn

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Condense your data table with this formatting hack 🤯 I watched my boss manually widening every column to fit long headers, which made the spreadsheet impossibly wide. Then I showed him this rotation trick that fits headers perfectly without expanding columns. Here's how to do it: 1️⃣ Select all your column headers 2️⃣ Go to Home > Alignment > Orientation 3️⃣ Choose "Angle Counterclockwise" to rotate headers at 45 degrees 4️⃣ Press Alt + H + O + I to autofit column widths 5️⃣ Watch your headers fit perfectly while keeping columns narrow This is perfect for data tables with long column names, dashboards with limited space, or any spreadsheet where you need maximum readability without excessive width. Instead of endless horizontal scrolling, everything fits on one screen. The best part? Your headers stay fully readable and your data stays compact—no more choosing between readability and screen space. Download my FREE Excel Shortcut Guide 👉🏼 https://shorturl.at/xGY8R #excel #exceltips #exceltricks #spreadsheets #corporate #accounting #finance #workhacks #tutorial #sheets

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