Data Insights Utilization

Introducing Insights in Chrome DevTools Performance panel! Many web developers know the power of the Chrome DevTools Performance panel, but navigating its wealth of data to pinpoint issues can be daunting. While tools like Lighthouse provide great summaries, they often lack the context of when and where issues occur within a full performance trace. On the Chrome team we're bridging this gap with the new "Insights sidebar" directly within the Performance panel. Read all about it: https://lnkd.in/gGd3bkPw This exciting feature integrates Lighthouse-style analysis right into your workflow. After recording a performance trace, the Insights sidebar appears, offering actionable recommendations. Crucially, it doesn't just list potential problems but highlights relevant events and overlays explanations directly on the performance timeline. Hover over an insight like "LCP by phase," "Render blocking requests" or "Layout shift culprits" to visually connect the suggestion to the specific moments in your trace. The sidebar covers key areas like Largest Contentful Paint (LCP) optimization (including phase breakdowns and request discovery), Interaction to Next Paint (INP) analysis (like DOM size impact and forced reflows), Cumulative Layout Shift (CLS) culprits, and general page load issues such as third-party impact and image optimization. It's designed to make performance debugging more intuitive by linking high-level insights to the granular data, helping you improve Core Web Vitals and overall user experience more effectively. Check out the Insights sidebar in the latest Chrome versions (it's been evolving since Chrome 131!). It’s a fantastic step towards making complex performance analysis more accessible. Give it a try on your next performance audit! #softwareengineering #programming #ai

Our research center in Princeton has become a magnet for healthcare AI expertise. Every time I catch up with Dorin Comaniciu and the team there, conversations quickly move from what’s possible to what really matters in healthcare delivery. Take for instance, our work on what we call the Operational Twin, an advisory service. It starts with creating a virtual representation of a clinical department, reflecting how patients, staff, and equipment interact in everyday operations so that different scenarios can be explored more safely and at scale. By simulating billions of scenarios representing dynamic conditions, AI agents learn how operational decisions shape outcomes. They can begin to anticipate bottlenecks and understand the long-term impact of short-term choices. The goal is more efficient planning of patient schedules, staffing, and equipment use, aligning daily decisions with broader clinical and organizational priorities. This becomes even more relevant as clinical innovations accelerate workflows. Faster scanning technologies such as Deep Resolve can shorten patient timeslots and an Operational Twin can help organizations adapt by optimizing schedules and resources to fully realize gains in speed and throughput. At its core, this work is about creating clarity in complex systems so that action becomes more precise and more purposeful. We see a similar principle in clinical innovation. With photon counting CT, we can visualize the heart in extraordinary detail, including structures inside the left ventricle that were previously difficult to see clearly. That deeper insight is captured by a Foundation Model that could help physicians guide ablation therapies with greater precision and confidence, especially when combined with live ultrasound to support real-time decision making in the procedure room. In both cases, whether in clinical imaging or in operations, the ambition is the same: better insight leading to better decisions at the moments that matter most for patients. 𝘋𝘪𝘴𝘤𝘭𝘢𝘪𝘮𝘦𝘳: 𝘛𝘩𝘦 𝘱𝘳𝘰𝘥𝘶𝘤𝘵𝘴/𝘧𝘦𝘢𝘵𝘶𝘳𝘦𝘴 𝘢𝘯𝘥/𝘰𝘳 𝘴𝘦𝘳𝘷𝘪𝘤𝘦 𝘰𝘧𝘧𝘦𝘳𝘪𝘯𝘨𝘴 𝘮𝘦𝘯𝘵𝘪𝘰𝘯𝘦𝘥 𝘩𝘦𝘳𝘦 𝘢𝘳𝘦 𝘯𝘰𝘵 𝘺𝘦𝘵 𝘢𝘷𝘢𝘪𝘭𝘢𝘣𝘭𝘦 𝘪𝘯 𝘢𝘭𝘭 𝘤𝘰𝘶𝘯𝘵𝘳𝘪𝘦𝘴. 𝘐𝘧 𝘵𝘩𝘦𝘴𝘦 𝘴𝘦𝘳𝘷𝘪𝘤𝘦𝘴 𝘢𝘳𝘦 𝘯𝘰𝘵 𝘮𝘢𝘳𝘬𝘦𝘵𝘦𝘥 𝘪𝘯 𝘤𝘦𝘳𝘵𝘢𝘪𝘯 𝘤𝘰𝘶𝘯𝘵𝘳𝘪𝘦𝘴 𝘧𝘰𝘳 𝘭𝘦𝘨𝘢𝘭 𝘰𝘳 𝘰𝘵𝘩𝘦𝘳 𝘳𝘦𝘢𝘴𝘰𝘯𝘴, 𝘵𝘩𝘦 𝘴𝘦𝘳𝘷𝘪𝘤𝘦 𝘰𝘧𝘧𝘦𝘳𝘪𝘯𝘨𝘴 𝘤𝘢𝘯𝘯𝘰𝘵 𝘣𝘦 𝘨𝘶𝘢𝘳𝘢𝘯𝘵𝘦𝘦𝘥. 𝘍𝘰𝘳 𝘮𝘰𝘳𝘦 𝘪𝘯𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘰𝘯, 𝘱𝘭𝘦𝘢𝘴𝘦 𝘤𝘰𝘯𝘵𝘢𝘤𝘵 𝘺𝘰𝘶𝘳 𝘭𝘰𝘤𝘢𝘭 𝘚𝘪𝘦𝘮𝘦𝘯𝘴 𝘏𝘦𝘢𝘭𝘵𝘩𝘪𝘯𝘦𝘦𝘳𝘴 𝘳𝘦𝘱𝘳𝘦𝘴𝘦𝘯𝘵𝘢𝘵𝘪𝘷𝘦.

Most problems in GTM can be solved with one thing: a better understanding of your customers. Sounds simple, right? It’s not. Especially if your systems are siloed and you don’t have an analyst on your team. Here’s the good news: It’s something AI is phenomenally good at – if it has the right data. If you’ve used ChatGPT’s deep research feature, you know how good it is at analyzing vast amounts of data and producing detailed insights. If you’ve used HubSpot, you know that it unifies structured and unstructured data and has full context across the customer journey. Today, I’m super excited to share that HubSpot is the first CRM to launch a deep research connector with ChatGPT. That means you can now do advanced analysis of context-rich customer data – and immediately take action on those insights. The result? Better experiences for your customers. Better outcomes for your business. Here are some use cases: - Marketers, you could ask for a list of 'VP' and 'C-Level' leads who opened an email in the past week but have no next activity scheduled – then run a hyper-targeted follow-up campaign. - Sales leaders and reps, you could pull up a summary of why your high-potential deals are stalling – then create an email sequence that addresses the most common objections. - Customer success leaders, you could generate a list of at-risk customers and a unique renewal plan for each one – then take proactive steps to retain them. I can’t wait to see what our customers achieve with the power of deep research and unified data, right in their flow of work. If you’re a HubSpot customer with a paid ChatGPT plan (Enterprise, Team, Pro, Plus, or Edu), you can get started now!

Before you say “send users to Japan,” that is not a valid option. xD Jokes apart, If the same backend and same code give 90 ms in Japan and 500 ms in India, my first assumption is: This is probably not an app code problem. It is usually a distance / network / edge / data path problem. Here is how I would answer it in an interview: Step 1: Break latency into parts I would ask: - How much is TTFB vs backend processing time? - Is the API server only in one region? - Where is the database? - Are we doing cross-region DB reads/writes? - Is TLS handshake / DNS / CDN / WAF different by geography? Because if backend compute is only 40–50 ms and Indian users still see 500 ms, the real issue is the path, not the handler. Step 2: Most likely fixes 1. Serve users from a closer region - Put app servers in India or a nearby APAC region - Use geo-DNS / Anycast / global load balancer to route users to the nearest healthy region 2. Move read-heavy data closer to users - Add regional read replicas - Cache aggressively at the edge for static or semi-static API responses - Use Redis in-region for hot keys 3. Reduce network round-trip - Avoid chatty APIs - Bundle dependent calls - Use connection reuse / keep-alive / HTTP/2 or HTTP/3 where possible 4. Put a CDN / edge layer in front - Good for auth-less content, metadata, configs, images, feature flags - Even dynamic APIs can benefit from edge caching if responses are cacheable Step 3: Be careful about the database This is where people mess up. If your app is in India but every request still writes to a DB in Japan, you just moved the bottleneck. So I would call out: - If reads dominate → regional replicas help a lot - If writes dominate → now we need to talk about multi-region write complexity, consistency, and whether the product can tolerate eventual consistency Step 4: How I would summarize it “My first move is not to change code blindly. I would measure where the 500 ms comes from. If the gap is due to geography, I would place compute and read paths closer to India using geo-routing, regional app servers, read replicas, Redis, and edge caching. If the database remains remote, that becomes the next bottleneck. So the real fix is reducing cross-region hops, not tuning one API handler.” That answer shows you understand: - latency decomposition - global architecture - caching - routing - consistency tradeoffs And that is what the interviewer usually wants.

🔥 Most people talk about healthcare innovation. Almost nobody talks about what actually drives it: information coherence. Let me break it down for you 👇 Information isn’t “just data.” It’s the architecture of your reality. And when that architecture is broken, everything downstream—your body, your business, your community—breaks too. Now imagine this: ➡️ A health practitioner who doesn’t just react to symptoms… but builds systems. ➡️ A patient who doesn’t wait for direction… but understands their own biology. ➡️ A practice that’s not surviving on volume… but thriving on transformation. That’s what happens when information becomes coherent. That’s what happens with Culminate H Labs’ Elevate Care Program (ECP) and the Coherent Information Thesis. Here’s the punchline: 💡 Precision + Empowerment: AI-driven dashboards from INTRINSICA cut decision time in half. DNA-guided care replaces cookie-cutter protocols. Patients get clarity—and control. 💰 Economic Elevation: Practices integrating ECP see up to 27% revenue growth and stronger retention. Because trust + transformation = growth. 🧠 Patient Transformation: Biosensor data. Personalized nutrition. Real autonomy. Results? 46% faster pain reduction, deeper sleep, and a mindset shift from “patient” to scientist of their own health. 🌍 Community Impact: Every touchpoint in ECP forges empowered partnerships. Health literacy rises. Autonomy spreads. Communities flourish. When practitioners become information architects, they don’t just heal individuals—they rebuild systems. When patients understand their own data, they don’t just live longer—they live freer. This isn’t marketing hype. This is the next era of human-centered healthcare. And it starts with one question: 👉 Are you ready to architect elevated care through information mastery? → Follow CULMINATE H LABS for exclusive insights, frameworks, and transformation stories. Join the movement that redefines health autonomy, fulfillment, and professional identity. Every statistic, every real story, every protocol: the common denominator is always coherent, personalized information—the lever for lasting change

🚀 After years of waiting, Google has finally provided the ability to import cost data from Facebook, Instagram and TikTok ads into Google Analytics. You'll now be able to import clicks, impressions and cost data from your campaigns and automatically merge the data into your GA4 reports. This means you'll be able to see a unified view of your paid search and paid social ad performance across channels, and calculate ROI and ROAS from a single view. This is a long time coming and significantly helps performance marketers make smarter business decisions using GA4. Imports can take up to 30 minutes to complete, and up to 24 hours for the data to be available. 💡 A note of caution: Meta or TikTok data imports will attempt to pull at least 24 months of historical data into your GA4 property. If you have existing cost data that would overlap, delete those earlier datasets before importing. #GA4 #MetaAds #TikTokAds

"SQL is easy." Until someone asks you: What’s our 3-month rolling average? How did we grow year over year? What’s the best-performing weekday? Suddenly, SELECT * isn’t enough. If you're working with time-series or sales data, here are 7 SQL queries that turn raw data into real insights: 1. Time-Based Aggregation ⤷ Group your data by day, month, quarter, or year to observe trends. 2. Moving Average ⤷ Smooths short-term fluctuations to reveal the bigger picture. 3. Year-over-Year Growth ⤷ Shows how performance compares to the previous year. 4. Month-over-Month Change ⤷ Highlights recent shifts in growth or decline. 5. Cumulative Total ⤷ Tracks how sales or users are building up over time. 6. Same Day Last Year Comparison ⤷ Helps identify performance on specific dates year over year. 7. Day-of-Week Breakdown ⤷ Reveals which days consistently perform better. You don’t need a new tool. You just need to ask better questions—and know how to write the right SQL. Because in analytics, knowing when something happened matters as much as what happened. -- 👋 I’m Jayen T. , Dedicated to helping aspiring data analysts thrive in their careers. ➕ Follow MetricMinds.in for more tips, insights, and support on your data journey!

Stop calling it an insight. If it’s just something the user said or did, it’s a finding. Here’s the difference: When you confuse findings for insights: ↳ Your reports fall flat ↳ Your stakeholders don’t engage ↳ Your impact disappears Let me tell you what changed everything for me: A finding is: - Descriptive - A single data point - Often interesting, but not explanatory A true insight is: - Behavioral truth - Rooted in patterns - Connects what happened, why, and the consequence Example: ↳ Finding: “Users disliked the onboarding.” ↳ Insight: “Users skip onboarding because they expect to explore freely. This causes later confusion and drop-off, leading to a lower conversion rate.” The moment I learned this my research started influencing roadmaps, not just decks. If your work isn’t moving the needle, ask yourself: Am I sharing findings, or am I delivering insights? Want help turning raw data into real insight? Check out the steps and frameworks below.

New research from Meta. Building synthetic training data has stayed a fixed pipeline that you hand-tune and then freeze. Autodata casts an AI agent as a data scientist that builds training and evaluation data, with an implementation called Agentic Self-Instruct that extends classic Self-Instruct with agentic planning and tool use. Think of it as meta-optimization, where the data scientist agent is itself trained to produce stronger data, so the pipeline keeps improving instead of staying static. Across computer science research, legal reasoning, and reasoning over mathematical objects, it beats classical synthetic-data methods, and meta-optimizing the agent delivers an even larger uplift.

🧪 Useful Calculators For UX Research. Helpful tools and guides to estimate the right number of participants for surveys, card sorting and usability testing. 🤔 Testing isn’t about finding universal truths, but key blockers. 🚫 Scale ≠ clarity: we must know what we’re trying to learn first. ✅ Iterate with 5 people at a time: test, adjust, test again. ✅ Surveys: aim for confidence level 95%, margin of error 2–5%. ✅ With 10.000 users, you will need ≥567 answers to reduce bias. ✅ Assume the response rate of 20–30% (incl. no-show-rate). ✅ To get reliable survey results, we need to invite 2835 people. ✅ For card sorting, get 15–30+ people to sort independently. ✅ For tree testing, invite at least 25 (better: 50) participants. ✅ Nothing matters more than targeted and diverse sample. I absolutely love Nikki Anderson’s point about big sample sizes often wrongfully viewed as the “safest” way to discover insights. They can’t fix vague goals, at times answer wrong or misleading questions, often skip the difficult part of framing the problem first. Scale sounds impressive, but it doesn’t map to clarity. Nikki suggests to start with a decision of what we’re trying to learn first, then the question, then the method and then the sample size. But we must know — and be committed — what want to know first. And very often we don’t need statistically significant results to notice and act on critical blockers. Research doesn’t have to be expensive or time-consuming. In the worst case, I start with 5×45 mins interviews to spot critical blockers and unmet user needs. As we run sessions, I mark critical areas and record short screen share snippets — with consent — and make them visible in the company. Once you’ve built enough confidence in the work that you are doing, it will be much easier to ask for bigger commitments — in fact, you might be surprised by how quickly your research work will be requested, rather than merely applied to design work. --- Useful resources: 👥 Qualitative Sample Size Calculator, by UserInterviews https://lnkd.in/enNhnjmZ 💸 Research Incentive Calculator https://lnkd.in/dZim2YSq 🔘 Survey Sample Size Calculator https://lnkd.in/e8htudk3 💯 Bonus: Design System ROI Calculator, by Knapsack https://lnkd.in/eYCxBTGt 📱 UX Work ROI Calculator, by Paul Boag https://lnkd.in/eAXtXWf5 🚀 UX Research Project Launch Kits https://lnkd.in/eX2zt88x 🌲 UX Research Field Guide, by UserInterviews https://lnkd.in/e4Ygsyuu --- Useful articles: How Many Participants For a UX Interview?, by Maria Rosala https://lnkd.in/eAEq6amb Sample-Size Recommendations, by Raluca Budiu, Kate Moran https://lnkd.in/erRN2RsW #ux #research