Implementing a Learning Management System

🍱 How To Design Effective Dashboard UX (+ Figma Kits). With practical techniques to drive accurate decisions with the right data. 🤔 Business decisions need reliable insights to support them. ✅ Good dashboards deliver relevant and unbiased insights. ✅ They require clean, well-organized, well-formatted data. ✅ Often packed in a tight grid, with little whitespace (if any). 🚫 Scrolling is inefficient in dashboards: makes comparing hard. ✅ Start with the audience and decisions they need to make. ✅ Study where, when and how the dashboard will be used. ✅ Study what metrics/data would support user’s decisions. ✅ Explore how to aggregate, organize and filter this data. ✅ More data → more filters/views, less data → single values. 🚫 Simpler ≠ better: match user expertise when choosing charts. ✅ Prioritize metrics: key insights → top left, rest → bottom right. ✅ Then set layout density: open, table, grouped or schematic. ✅ Add customizable presets, layouts, views + guides, videos. ✅ Next, sketch dashboards on paper, get feedback, iterate. When designing dashboards, the most damaging thing we can do is to oversimplify a complex domain, or mislead the audience. Our data must be complete and unbiased, our insights accurate and up-to-date, and our UI must match users’ varying levels of data literacy. Dashboard value is measured by useful actions it prompts. So invest most of the design time scrutinizing metrics needed to drive relevant insights. Bring data owners and developers early in the process. You will need their support to find sources, but also clean, verify, aggregate, organize and filter data. Good questions to ask: 🧭 What decisions do you want to be more informed on? (Purpose) 😤 What’s the hardest thing about these decisions? (Frustrations) 📊 Describe how you are making these decisions? (Sources) 🗃️ What data helps you make these decisions? (Metrics) 🧠 How much detail is needed for each metric? (Data literacy) 🚀 How often will you be using this dashboard? (Value) 🎲 What constraints should we know about? (Risks) And, most importantly, test dashboards repeatedly with actual users. Choose key tasks and see how successful users are. It won’t be right at first, but once you get beyond 80% success rate, your users might never leave your dashboard again. ✤ Dashboard Patterns + Figma Kits: Data Dashboards UX: https://lnkd.in/eticxU-N 👍 dYdX: https://lnkd.in/eUBScaHp 👍 Ethr: https://lnkd.in/eSTzcN7V Orange: https://lnkd.in/ewBJZcgC 👍 Semrush: https://lnkd.in/dUgWtwnu 👍 UKO: https://lnkd.in/eNFv2p_a 👍 Wireframing Kit: https://lnkd.in/esqRdDyi 👍 [continues in comments ↓]

𝐖𝐡𝐚𝐭 𝐆𝐨𝐭 𝐘𝐨𝐮 𝐇𝐞𝐫𝐞 𝐖𝐨𝐧’𝐭 𝐆𝐞𝐭 𝐘𝐨𝐮 𝐓𝐡𝐞𝐫𝐞 What worked for you in the past, may not work in the future. As problems evolve, the solutions for them have to evolve as well In my conversations with mid-management professionals, one recurring theme stands out: a reliance on what worked in the past. They often stick to familiar processes or methodologies, even when the landscape and challenges have evolved. It’s not about a lack of awareness. It’s about comfort. Familiar solutions come with predictable outcomes, and even when they fall short, people know how to manage the fallout. The truth: 𝐰𝐡𝐚𝐭 𝐛𝐫𝐨𝐮𝐠𝐡𝐭 𝐬𝐮𝐜𝐜𝐞𝐬𝐬 𝐲𝐞𝐬𝐭𝐞𝐫𝐝𝐚𝐲 𝐦𝐢𝐠𝐡𝐭 𝐧𝐨𝐭 𝐠𝐮𝐚𝐫𝐚𝐧𝐭𝐞𝐞 𝐢𝐭 𝐭𝐨𝐦𝐨𝐫𝐫𝐨𝐰. The problems of the future demand new perspectives, new skills, and innovative solutions. As knowledge professionals, we must adopt this mindset: “𝐓𝐡𝐞 𝐩𝐚𝐬𝐭 𝐛𝐮𝐢𝐥𝐝𝐬 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞, 𝐛𝐮𝐭 𝐭𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞 𝐝𝐞𝐦𝐚𝐧𝐝𝐬 𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧.” To stay relevant: 🔹 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 𝐲𝐨𝐮𝐫 𝐡𝐚𝐛𝐢𝐭𝐬 — don’t let past successes define your playbook. 🔹 𝐒𝐭𝐚𝐲 𝐜𝐮𝐫𝐢𝐨𝐮𝐬 — continuously learn, adapt, and explore new methodologies. 🔹 𝐁𝐞 𝐟𝐮𝐭𝐮𝐫𝐞-𝐫𝐞𝐚𝐝𝐲 — equip yourself with skills for the problems yet to come. The world is changing fast, and so must we. Keep learning, keep growing, and stay ahead of the curve. 🚀 #Leadership #Reskilling #FutureOfWork #ContinuousLearning

If I had to start learning SAP again in 2025, here’s exactly how I would do it. Over the years, I’ve seen many people struggle with where to begin, what to focus on, and how to prepare for interviews. So today, I’m sharing the path I would personally follow if I had to start from scratch in 2025 — and it’s the same roadmap I suggest to my mentees: 1. Understand what SAP actually is — and why companies use it. I would begin by understanding the business side first. Not the technical screens, not the transaction codes — but what problem SAP solves. What is an ERP? What happens in a company’s Sales, Procurement, Finance, or Manufacturing process? 📘 That’s exactly why I wrote a beginner-friendly book “Let’s Talk About SAP Basics” to simplify this part. 2. Pick one module — and stick to it. In my case, I chose SAP SD (Sales & Distribution) — because I found it relatable to real-world business. If you’re from a finance background, go for SAP FICO. If you’re into analytics, maybe start with SAP Analytics Cloud or BW. Don’t try to learn everything — you’ll just confuse yourself. 3. Learn the business process first, system process second. I’d first learn: What happens when a company sells a product? What is a quotation, order, delivery, and invoice in business terms? Then I’d map that to SAP: How does SAP capture those steps? What is a sales document? What is a pricing condition? That’s how I make the system make sense. 4. Practice in a demo system. Watching videos is helpful — but nothing beats hands-on. I’d subscribe to a demo SAP server and start playing with transactions. Make mistakes. Break things. Learn how it works. 5. Prepare for interviews from Day 1. I wouldn’t wait till “I complete the course.” I’d start practicing answers like: “Can you explain the end-to-end sales process in SAP?” “What is the role of a pricing procedure?” “How does output determination work?” And I’d build confidence by explaining answers in simple terms — even if just to myself in front of a mirror. 6. Build a digital presence. This is 2025. I’d post what I’m learning on LinkedIn. I’d share summaries, my takeaways, or even small lessons from SAP. Not because I’m an expert — but to learn better and attract opportunities. 🎯 Bottom line: Start with business understanding. Go deep in one module. Practice hands-on. Prepare for interviews early. And share your journey online. That’s what I would do if I had to start learning SAP all over again in 2025. And if you’re just starting — remember, you’re not late. You’re right on time. If this post helped you or someone you know is starting their SAP journey, tag them or share this. And if you're looking for 1:1 mentorship, feel free to DM me — always happy to guide. #SAP #CareerTips #SAPLearning #SAPSD #ERP #Mentorship #SAPInterview #SAPBeginners #DigitalTransformation #LinkedInLearning

The numbers don't lie: 2025 is becoming the year of the pink slip. But this isn't just about cost-cutting anymore. Just 6 months into 2025, 𝗨𝗦 𝗲𝗺𝗽𝗹𝗼𝘆𝗲𝗿𝘀 𝗮𝗹𝗼𝗻𝗲 have already announced almost 𝟴𝟬𝟬,𝟬𝟬𝟬 𝗷𝗼𝗯 𝗰𝘂𝘁𝘀 - an 80% increase from the same period last year. 𝗠𝗮𝗷𝗼𝗿 𝗽𝗹𝗮𝘆𝗲𝗿𝘀 𝗺𝗮𝗸𝗶𝗻𝗴 𝗰𝘂𝘁𝘀: • 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁: 15,000 workers (4% of global staff) - despite reporting $70.1B revenue • 𝗜𝗻𝘁𝗲𝗹: 4,000 jobs slashed in major restructuring • 𝗜𝗕𝗠: 8,000 employees, mainly HR roles replaced by AI chatbot "AskHR" • 𝗠𝗲𝘁𝗮: 3,600 layoffs to make space for AI talent    𝗪𝗵𝗮𝘁'𝘀 𝗥𝗲𝗮𝗹𝗹𝘆 𝗕𝗲𝗵𝗶𝗻𝗱 𝗧𝗵𝗶𝘀 𝗪𝗮𝘃𝗲? It's not just economic pressure. Companies are posting strong earnings while cutting thousands of jobs. The real drivers: 1️⃣ 𝗔𝗜 𝗗𝗶𝘀𝗽𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁: Microsoft's CEO revealed AI now writes 30% of their code, while 40% of recent layoffs targeted software engineers 2️⃣ 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗥𝗲𝗮𝗹𝗹𝗼𝗰𝗮𝘁𝗶𝗼𝗻: Companies are "shedding roles whose economic value has fallen below the AI line" 3️⃣ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗶𝗼𝗻: 41% of companies worldwide plan to reduce workforces by 2030 due to AI 4️⃣ 𝗘𝗰𝗼𝗻𝗼𝗺𝗶𝗰 𝗛𝗲𝗮𝗱𝘄𝗶𝗻𝗱𝘀: Tariffs, funding cuts, and economic pessimism are putting intense pressure on workforces 𝗧𝗵𝗲 𝗛𝗮𝗿𝗱 𝗧𝗿𝘂𝘁𝗵: This isn't temporary. Companies aren't planning to hire these people back. They're using the money they save to buy more AI systems. 𝗛𝗼𝘄 𝘁𝗼 𝗦𝘁𝗮𝘆 𝗥𝗲𝗹𝗲𝘃𝗮𝗻𝘁: 𝗧𝗵𝗲 𝗟𝗶𝗳𝗲𝗹𝗼𝗻𝗴 𝗘𝗺𝗽𝗹𝗼𝘆𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗣𝗹𝗮𝘆𝗯𝗼𝗼𝗸 ✅ 𝗠𝗮𝘀𝘁𝗲𝗿 𝗔𝗜 𝗧𝗼𝗼𝗹𝘀 𝗜𝗺𝗺𝗲𝗱𝗶𝗮𝘁𝗲𝗹𝘆 • Learn ChatGPT, Claude, GitHub Copilot • 120 million workers need retraining within three years - don't be last in line    ✅ 𝗗𝗲𝘃𝗲𝗹𝗼𝗽 𝗔𝗜-𝗥𝗲𝘀𝗶𝘀𝘁𝗮𝗻𝘁 𝗦𝗸𝗶𝗹𝗹𝘀 • Strategic thinking and complex decision-making • Interpersonal communication and empathy • Leadership of human-AI hybrid teams    ✅ 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗟𝗲𝗮𝗿𝗻𝗲𝗿 • 70% of skills used in most jobs will change by 2030 • Focus on skills-based learning over traditional credentials • AI skills are evolving 66% faster than non-AI skills    ✅ 𝗣𝗼𝘀𝗶𝘁𝗶𝗼𝗻 𝗬𝗼𝘂𝗿𝘀𝗲𝗹𝗳 𝗮𝘀 𝗔𝗜-𝗡𝗮𝘁𝗶𝘃𝗲 • Learn to work WITH AI, not against it • Find roles that combine human judgment with AI capabilities • Focus on human skills AI can't replicate: creativity, empathy, strategic thinking    ✅ 𝗕𝘂𝗶𝗹𝗱 𝗔𝗻𝘁𝗶-𝗙𝗿𝗮𝗴𝗶𝗹𝗲 𝗖𝗮𝗿𝗲𝗲𝗿 𝗥𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝗰𝗲 • Diversify your skill portfolio across multiple domains • Stay agile and adaptable to rapid change    The question isn't whether AI will change your job - it's whether you'll be ready when it does. What's your strategy for staying relevant in an AI-first world? https://lnkd.in/gaekg3gf

Most L&D is just "content consumption" in disguise. We build massive libraries, launch complex LMS platforms, and track "completion rates" like they are a business metric. But at the end of the day, if the behaviour hasn't shifted, you haven't delivered training. You have delivered a distraction. Performance-led L&D doesn't care about what people know. It cares about what people can DO. To get there, you have to stop being an "Order Taker" and become an Impact Architect. Here is my 5-day blueprint to transition your L&D from a cost center to a performance engine: -Day 1: Diagnose the Performance Gap. Stop asking what they want to learn. Ask what business metric fails if we do nothing. If there’s no KPI attached, it’s not a priority. -Day 2: Design the 5-Day Sprint. Ditch the 40-slide deck. Architect a 5-day journey focused on one narrow, observable skill. Solve the learner's most immediate frustration first. -Day 3: Build the "Job Aid" Ecosystem. If they have to leave their workflow to learn, they won't. Build checklists, decision trees, and prompt templates that live where the work happens. -Day 4: Activate Accountability Loops. Learning without evidence is just a hobby. Create a system where learners must post "Proof of Work" - a win, a draft, or a screenshot - within 48 hours. -Day 5: Measure the Behavioural Shift. Proof is better than praise. Use a before-and-after rubric to show stakeholders the actual delta in performance. L&D isn't about filling heads; it's about moving needles. Stop building libraries. Start building engines of behaviour change. I am Zubin Rashid. I help organisations architect performance-led learning that actually sticks. ♻️ Repost if you believe L&D should be measured by impact, not attendance. ➕ Follow me for more insights on Performance Orchestration.

Stop learning "Python basics" when you need "Zapier automations that pull evidence from your actual tech stack into Notion." I built an open-source GRC lab generator that creates learning paths for YOUR exact situation, tools, and goals. Here's the full journey: Junior GRC Analyst → Promotion-Ready in 2 weeks. Using the open-source GRC Lab Generator, I walked through a complete real-world example that shows exactly how this works end-to-end. The scenario: → Junior Compliance Analyst at healthcare SaaS → Solo GRC team drowning in manual evidence collection → Non-technical ("I've watched a documentary on snakes once") → Tools: Excel, JIRA, Notion → Goal: Get promoted to intermediate analyst The challenge: "I'm losing ages pulling evidence from multiple systems. I want a continuously updated dashboard I can use with control owners. I want to be promoted." What the lab generated: A 2-week intensive sprint that built: ✅ Notion system of record (50+ controls, 100+ evidence items) ✅ 4 working Zapier automations (no coding required) ✅ Executive dashboard ready to present ✅ Control owner dashboards (3-5 owners) ✅ Time savings tracker with real data The promotion pitch it included: "Over the past 2 weeks, I built a compliance evidence management system that has transformed how we collect, track, and report on compliance evidence." Complete with metrics, documentation, and a 6-slide presentation deck. Why this example matters: This is the difference between generic training ("let's hit some APIs") and custom labs ("here's how to build YOUR promotion case with a flagship project using YOUR tools in YOUR 2-week window"). The lab matched their: → Non-technical skill level (visual diagrams, no code) → Actual tools (Excel + JIRA + Notion, not some fancy GRC platform) → Real timeline (2-week intensive sprint) → Career goal (promotion to intermediate analyst) Impact metrics from the lab: 10+ hours saved per week 80%+ control coverage with current evidence 3+ automations running successfully Real-time compliance visibility The best part? This works for ANY GRC role, technical level, or challenge. Fill out the form once, generate unlimited custom labs. Try it yourself! #GRCEngineering #CareerGrowth

Your app can sound sure. Can it show where the answer came from? Can it stay inside the user’s permissions? Can it recover when a tool fails? Can it make the work lighter? That is the work. Andrew Ng named four skills. I turned them into a path I would actually follow. Build the app. Learn tokens, context, tools. Ship something you can test. Add retrieval when the model does not know enough. Check that the evidence supports the answer. Measure quality, latency, and cost. Do not give an agent power until you have written what it may do, when it must stop, and what happens when it breaks. Remember it is still software. APIs. Databases. Auth. Tests. Deploy. A slow call can exhaust a pool. A retry can write twice. A cache can show the wrong user’s data. If you miss that, people will not trust the product. Use the coding agent. Give it the task, the context, and the definition of done. Then read the diff. Run your own checks. Know what it imported and what it assumed. If you cannot explain the code, you do not own it. Shape the build from week one. Talk to users. Pick one problem worth solving. Ship a small version. Watch where it fails. Decide the next cut. A clever feature that creates more work is not progress. If you are learning, keep one project alive. Start with a tested API. Add one model-powered feature. Bring in retrieval or tools only when you need them. Deploy it. Measure it. Ask someone who uses it. Twenty-four weeks is a guide. Move when you can show the skill. Andrew Ng drew the four parts. The sequence and the projects are my expansion. What would you add from something you had to build?

“Dashboards are dead”? Only the context-free ones. Most teams start with definitions. They write a KPI dictionary, argue about formulas, then stack charts. Start with relationships. Map what drives what. Use metric maps and driver trees to sketch causality. ↳ Then define formulas. ↳ Then design screens. ↳ Then pick visuals. Here’s the 4-layer model we use: 1) Maps & Drivers: – metrics maps – driver trees 2) Definitions: – cohorts – formulas – granularity – attribution model – validation checks 3) Information Architecture – filters – page flow – drill paths – segments – comparisons 4) Visuals & UX – chart patterns – color semantics – legends & labels – responsive layout – conditional formatting Why this order? Because “what moved?” is useless without “why.” Common traps this avoids: ✕ Glossary-first thinking. Clean formulas ≠ causal logic. ✕ Chart sprawl. More graphs ≠ more clarity. ✕ Mixed levels. Result, diagnostic, actionable in one pot. If your dashboard doesn’t explain change, it’s reporting, not analytics. Build the logic first. Then display it. #dashboards

As a recent graduate and someone early in my career, I’m learning that education doesn’t stop after school—or even at work. Simply completing the tasks assigned to me isn’t enough to grow or stay competitive in my field. The industry evolves fast, and to stay relevant, continuous learning and upskilling are non-negotiable. That’s why I’ve developed a few strategies to keep learning every day, and in this video, I’m sharing them with you: 1️⃣ Follow Industry-Specific Pages and Thought Leaders: Staying connected with trends and innovations inspires new ideas and helps me see the bigger picture. 2️⃣ Learn on the Go: Whether it’s reading an article during gym breaks or listening to a podcast while driving or cooking, finding small windows for learning can make a big difference. 3️⃣ Dive into Online Courses: Platforms like Coursera and Udacity offer targeted courses in many topics. These have been game changers for expanding my technical skills. 4️⃣ Make Learning a Habit: Consistency is key. Even dedicating 15-30 minutes a day to learning keeps you growing and adapting. These habits are helping me stay ahead, and I hope they inspire you to create your own learning routine! Watch the video for more tips, and don’t forget to share your own learning strategies in the comments below.👇🏾

"Test what I am saying, experiment with it and think how you are going to apply it- don't just accept it." - I say this every now and then. When did you last participate in a powerful workshop, felt inspired, took pages of notes.. and then a month later, barely remembered a thing? In 9 years of working with professionals- I’ve seen a pattern. People don’t struggle with learning. They struggle with retaining and applying what they learn. It’s not you- it’s just how the brain works. Your brain doesn’t store knowledge like a library, neatly filed away for later use. It’s more like a jungle, constantly growing, pruning, and reshaping itself- there is a mix up of information. Every new piece of knowledge creates a fragile pathway in your brain. If you don’t reinforce it, your brain will erase it. This is called synaptic pruning—your brain’s way of decluttering. It only keeps what you use. This is why one-time learning events rarely lead to lasting improvements. It’s why information doesn’t change people- application does. I meet 300- 450 people as participants every month, I’ve found that the best learners—the ones who truly transform—do three things differently: 1. Space It Out – Revisit ideas over time. 2. Engage, Don’t Just Consume – Teach, discuss, apply. 3. Use Multi-Sensory Learning – Read, write, speak, experience. Reading about a concept isn’t enough. Writing it, speaking it, and experiencing it in action makes it real. When I design my training programs, I focus on wiring the brain for lasting impact. Because learning isn’t about what you hear in a room. It’s about what stays with you when you walk out. Great read: Make It Stick by Peter Brown #learningdesign #retention #application #impactfullearning #softskills #training #experientiallearning