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Employees with 3 or more different managers in a year are up to 75% more likely to leave your company Yesterday, we saw that only ~50% of employees in Pave’s dataset have kept the same manager for a full year. Today, let’s go one step deeper and look at the impact of “manager thrash” on employee attrition. _____________ As a quick caveat, the reported turnover rates include both voluntary and involuntary (as well as regrettable and non-regrettable) attrition. And yes, this includes layoffs too. _____________ 𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 𝗮𝗰𝗿𝗼𝘀𝘀 𝗣𝗮𝘃𝗲’𝘀 𝗱𝗮𝘁𝗮𝘀𝗲𝘁? • 𝗢𝗻𝗲 consistent manager over the past 12 months => Attrition rates between 𝟭𝟲% 𝗮𝗻𝗱 𝟮𝟬% depending on company stage. • 𝗧𝘄𝗼 managers over the past 12 months => Attrition rates between 𝟮𝟭% 𝗮𝗻𝗱 𝟮𝟳%. • 𝗧𝗵𝗿𝗲𝗲 𝗼𝗿 𝗺𝗼𝗿𝗲 managers over the past 12 months => Attrition rates between 𝟮𝟱% 𝗮𝗻𝗱 𝟯𝟰%. _____________ 𝗧𝘄𝗼 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝘁𝗼 𝗰𝗮𝗹𝗹 𝗼𝘂𝘁: 1️⃣ In general, the more managers an employee has, the more likely they are to leave the company. 2️⃣ Attrition rates are usually highest at early stage startups and gradually decrease as companies mature. ________________ 𝗔𝗰𝘁𝗶𝗼𝗻𝗮𝗯𝗹𝗲 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆 𝗳𝗼𝗿 𝗧𝗼𝘁𝗮𝗹 𝗥𝗲𝘄𝗮𝗿𝗱𝘀 𝗮𝗻𝗱 𝗛𝗥 𝗟𝗲𝗮𝗱𝗲𝗿𝘀: ✅ Re-orgs, performance management, and layoffs are somewhat inevitable in the world of company building. However, be cognizant of the tangible impact that “manager thrash” has on employee attrition. In particular, I encourage you to run a cohort analysis around how much manager thrash your top performers have undergone over the past 12 months as a way to proactively predict attrition risk org-by-org. I’d also call out that it’s important to consider whether or not attrition spikes caused by “manager thrash” are due to causation or correlation with other forces. Think critically here. Is it the disruption, inconsistent expectations, or something else? #pave #orgchart #benchmarks

You aren't losing top talent to your competitors. You are losing them to your own culture. 💡 New global research from the career space, iHire, reveals a disturbing paradox: despite massive corporate investments in well-being programs, toxic workplace behaviors are surging. ➡️ Employees are actively fleeing environments that tolerate poor management, lack of communication, and unchecked psychological friction. If you are only tracking your general attrition rate, you are blind to the real problem. You are just refilling a bucket with a massive hole in the bottom. To fix retention, we have to stop measuring total headcount loss and start measuring the toxicity premium. 📊 Introducing 𝘁𝗵𝗲 𝗖𝘂𝗹𝘁𝘂𝗿𝗮𝗹 𝗙𝗿𝗶𝗰𝘁𝗶𝗼𝗻 𝗜𝗻𝗱𝗲𝘅 (𝗖𝗙𝗜). This simple metric quantifies exactly how much of your talent attrition is being driven by internal cultural failure: 📐 CFI = (Exit Interviews Citing Toxic Culture)\ (Total Voluntary Resignations) X 100 🚩 If you have 100 voluntary resignations, and 45 of them cite bad management or a toxic environment in their exit data, your CFI is 45%. That means nearly half of your turnover is an entirely preventable, expensive self-inflicted wound. Is your HR team actively tracking the cost of cultural friction, or are you just guessing why people leave? Dave Ulrich #ToxicWorkplace #Leadership #RetentionStrategy

New in Chrome DevTools! Debug an app's full performance trace with Gemini! The DevTools Performance panel just got a major upgrade with a deeper integration of Gemini! Now you can analyze performance issues faster and more holistically than ever before. After recording a trace, you can now chat with Gemini about the entire trace, related Performance insights, and even connected field data - all without needing to select specific context beforehand! Get a full-picture analysis of your page's performance and identify potential bottlenecks: Let Gemini highlight areas of concern before you manually dive into the details. Once Gemini helps you spot a potential problem, the workflow is smooth: 1. Refine your focus: Easily select a more specific context item - like a single trace event, a specific Flame Chart block, or a Performance insight. 2. Continue the same chat: Keep the conversation going! Gemini will adjust its advice and analysis based on the new, narrow focus, helping you get to the root cause faster. The power of Gemini is now available for all insights in the Performance > Insights tab. If you see an insight, you can instantly ask Gemini about it for deeper context, explanations, and potential fixes. This new workflow is designed to help you move from a broad performance overview to a targeted deep dive without ever breaking your flow. Give it a try on your next performance recording! #ai #programming #softwareengineering

📌 Power BI Breakdown # 3: HR Analytics HR teams have more data than ever before. But are they using it effectively? Employee turnover, absenteeism, and engagement levels all hold critical insights that can shape the success of an organization. Yet, many HR teams still rely on fragmented reports and manual analysis. This is where a well-built HR Analytics Dashboard comes into play. In this 3rd post of the Power BI Breakdown series, I’m sharing a demo I’ve recently built for HR teams. The dashboard can help companies tackle key workforce challenges: ⤷ Why are employees leaving? ⤷ Which departments have the highest turnover? ⤷ What factors contribute to employee satisfaction? But realistically, what data do you need? Building a similar dashboard in Power BI requires integrating multiple data sources: 🔹 HRIS (e.g., Workday or SAP) → Employee records, tenure, salary, job position 🔹 Payroll System (ADP, Paycom, QuickBooks Payroll) → Compensation and salary trends 🔹 Engagement & Performance (SurveyMonkey, Lattice, Culture Amp) → Satisfaction scores, turnover risks 🔹 Recruitment Data (LinkedIn, Indeed, etc.) → Hiring sources, candidate pipeline Once you bring all these data sources into a centralized data warehouse, you can merge them and unlock critical insights such as: ☑ Turnover Rate by Department → Identify which teams struggle with retention ☑ Departure Reasons → Analyze why employees leave (salary, engagement, career growth) ☑ High-Risk Employees → Spot individuals with low satisfaction & high turnover risk ☑ Recruitment Effectiveness → Find out which hiring sources bring long-term employees Power BI can help you solve all these problems and truly leverage your HR data, but only if you do it properly :) 🟢 Live Demo Here: https://lnkd.in/egHAqBdg #PowerBI #DataAnalytics #BusinessIntelligence

HR Analysis & Prediction Dashboard in Power BI 👥 People are the biggest asset of every organization, but without data, it's impossible to make the right HR decisions. Here's one of my latest HR Analysis & Prediction Dashboard projects built in Power BI, designed to help HR teams monitor workforce performance and predict future trends. Key insights included: • Total Employees, Active Employees & New Hires • Attrition Rate Tracking & Monthly Trend Analysis • Employee Distribution by Department • AI-Based Employee Attrition Prediction • Employee Performance Distribution • Top Skills in Demand for 2026 • Employee Satisfaction Scorecard • High-Risk Employee Identification • Department Performance Overview • Employee Growth by Job Level • Predictive HR Insights for Better Decision Making • Interactive Filters for Department, Location, Job Level & Date Range This dashboard enables organizations to: ✅ Identify departments with high employee turnover ✅ Predict future attrition before it happens ✅ Measure employee satisfaction and engagement ✅ Analyze workforce performance in real time ✅ Support data-driven hiring and retention strategies Building dashboards is not just about creating charts, it's about transforming raw HR data into meaningful business decisions. If you'd like to learn how to build professional dashboards like this from scratch using Power BI, Excel, SQL, and Python, with real business datasets, DAX, Power Query, data modeling, and end-to-end projects, comment INTERESTED, and we'll reach out with all the details. #PowerBI #HRAnalytics #DashboardDesign #BusinessIntelligence #DataAnalytics #PeopleAnalytics #HumanResources #DAX #PowerQuery #Excel #SQL #Python #DataVisualization #CareerGrowth #AliDataAnalytics

𝟵𝟯% 𝗼𝗳 𝗳𝗿𝗼𝗻𝘁𝗲𝗻𝗱 𝗰𝗮𝗻𝗱𝗶𝗱𝗮𝘁𝗲𝘀 𝗰𝗮𝗻’𝘁 𝗲𝘅𝗽𝗹𝗮𝗶𝗻 𝗥𝗲𝗮𝗰𝘁 𝗣𝗿𝗼𝗳𝗶𝗹𝗲𝗿 𝘃𝘀 𝗟𝗶𝗴𝗵𝘁𝗵𝗼𝘂𝘀𝗲. (𝗯𝗮𝘀𝗲𝗱 𝗼𝗻 𝟱𝟬+ 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀) The best candidates don’t just know tools — they know when to use them. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗶𝗺𝗽𝗿𝗲𝘀𝘀𝗲𝘀 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿𝘀: 𝟭.𝗥𝗲𝗮𝗰𝘁 𝗣𝗿𝗼𝗳𝗶𝗹𝗲𝗿  • When to mention: "I'd use Profiler when components are re-rendering unnecessarily. It helps me identify which components are slow and why."  • Real scenario: Debugging why your dashboard feels sluggish even though Lighthouse scores are green. 𝟮. 𝗟𝗶𝗴𝗵𝘁𝗵𝗼𝘂𝘀𝗲  • When to mention: "Lighthouse is my go-to for auditing overall page performance, accessibility, and SEO before production."  • Real scenario: You need hard metrics on Core Web Vitals to justify optimization work to stakeholders. 𝟯. 𝗖𝗵𝗿𝗼𝗺𝗲 𝗗𝗲𝘃𝗧𝗼𝗼𝗹𝘀 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗧𝗮𝗯  • When to mention: "When I need to analyze the main thread and see exactly what's blocking the UI during interactions."  • Real scenario: Users report janky scrolling, and you need to find the long-running JavaScript tasks. 𝟰. 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗧𝗮𝗯  • When to mention: "I check this first when investigating slow load times—waterfall analysis shows me bundling issues and slow API calls."  • Real scenario: Your app loads fine locally but crawls in production. 𝟱. 𝗕𝘂𝗻𝗱𝗹𝗲 𝗔𝗻𝗮𝗹𝘆𝘇𝗲𝗿  • When to mention: "Before optimizing, I run webpack-bundle-analyzer to see which dependencies are bloating my bundle."  • Real scenario: Your initial bundle is 500KB and you need to get it under 200KB. 𝗪𝗵𝗲𝗿𝗲 𝗺𝗼𝘀𝘁 𝗰𝗮𝗻𝗱𝗶𝗱𝗮𝘁𝗲𝘀 𝗳𝗮𝗶𝗹 They list tools with zero context. The ones who get offers : 𝗧𝗵𝗲𝘆 𝗱𝗼𝗻'𝘁 𝗷𝘂𝘀𝘁 𝗹𝗶𝘀𝘁 𝘁𝗼𝗼𝗹𝘀, 𝗳𝗲𝘄 𝗰𝗮𝗻 𝘂𝘀𝗲 𝘁𝗵𝗲𝗺 𝗮𝘁 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗺𝗼𝗺𝗲𝗻𝘁. That’s the real signal!! What tool saved you in your last performance optimization? Drop it below Link in comment 👇

Workforce planning has always been an incredibly complex and difficult task. Despite valiant efforts to improve these models, they have remained relatively static and simplistic, relying predominantly on small teams crunching data or on predictions from the hiring manager community. In an ideal world, we would shift from a static, once-a-year exercise to a dynamic, more proactive model. We would stop reacting to what's happening now and start anticipating what's likely to happen next. Last week, I had the pleasure of spending time with our enterprise data and analytics team, a group that services over 800 customers. The most exciting topic we discussed was three pilots we're running with customers right now that aim to make this a reality: using a digital twin for work planning. It works by connecting vast amounts of external market data with a company's many internal data sources, some they typically wouldn't consider, such as ERP, CRM (sales), LMS, and Time and Attendance systems. This allows us to run scenarios and model future talent needs. Here’s a concrete example: By analyzing Salesforce, HRIS, and ATS data, we can predict that when multiple prospect opportunities reach a specific stage in our customer’s sales cycle, there is a high likelihood of winning at least one of them. We can then analyze the consistent skill sets across all of those prospect opportunities, allowing us to confidently and proactively start a recruitment process for those skills. The goal being that we have candidates at the final stages of the process, before an official requisition has been raised, positively impacting time to hire. We’ve also been able to replicate a similar model based on website sales activity. The question to ask is: what data is generated in what system that allows you to get ahead of the hiring process today. 

Debuggers show what was rendered. Profilers show how long it took. This is how I profile GPUs: GPU debuggers help me to see how rendering works in detail - used shaders, meshes, and targets. GPU profilers provide timings and performance insights. They are not the same. A minimal profiler, at the very least, provides some timing statistics about each draw call. Now, think about the tools you're using to improve the GPU performance in your game. Do they at least provide timings of each draw call? For me, those tools are debuggers, NOT profilers. I don't use them for GPU optimization: ❌ Frame Debugger in Unity - no timings at all. ❌ Unity Profiler - no timings for GPU draw calls. ❌ RenderDoc - very limited timings (if any), hard to interpret When I need to improve the rendering performance, I use the GPU profilers provided by the GPU vendors. Those tools show me detailed timings and performance counters: ✔️ Nvidia Nsight Graphics (my favourite) ✔️ AMD Radeon GPU Profiler ✔️ Intel GPA ✔️ PIX - profiler for DirectX 12 My optimization routine usually goes like this: 1. I find a place in the game that struggles and prepare the project to have a consistent reproduction of the same scenario. 2. I analyze the rendering using the debugger, usually Nsight Frame Debugger, which can also show some basic timings. 3. I analyze the performance using the profiler, usually Nsight GPU Trace Profiler. 4. I plan the optimization work based on the gathered data (no guessing here). 5. After optimization is done, I test in the same scenario to avoid testing bias. If you're interested in GPU profiling, I've created an article that explains the basics of GPU profiling using Nvidia Nsight Graphics. You can find the links in the comments.