Technical Problem Solving

How software engineers solve problems vs how GRC teams solve problems 🔧 Software engineers: → Define the pain point clearly (Trello: "network partitioning causes message loss") → List specific requirements (failover capabilities, throughput needs, latency targets) → Evaluate multiple alternatives systematically (Kafka, SNS+SQS, Kinesis, Redis Streams) → Choose based on technical fit, not features (Kafka met requirements; Redis Streams was unstable) → Implement incrementally (shadow traffic, gradual rollout, measure results) → Build for future scale (anticipate growth, design for reliability) How traditional GRC solves problems: 📋 → Think in project cycles (audit deadlines drive everything) → Optimise for framework coverage over driving down risk → Ignore stakeholder toil ("just fill out this 50-question Google Forms") → Choose tools by feature count (does it have 200+ connectors?) → PoC based on demos not depth of what you actually need → Complain about your tool online → Reset after each audit instead of building iteratively The GRC Engineering difference? We apply software engineering problem-solving methodology to compliance challenges. ✅ Clear problem definition: "Automated evidence collection doesn't scale outside of public cloud specific controls" ✅ Requirements-driven evaluation: What do we actually need vs. what vendors sell vs. what we can build? ✅ Systematic comparison: Technical fit over feature lists ✅ Incremental implementation: Test with low-risk controls first but in production settings ✅ Future-proof architecture: Build systems that can be maintained by the team and can scale to production-grade environments Just like Trello didn't choose Kafka because it had the most features, they chose it because it solved their specific technical requirements. Your GRC program deserves the same engineering rigour. 🚀 #GRCEngineering #SystemsThinking #EngineeringMethodology

Over the years working in chemical processing, one of the recurring challenges I’ve faced is with heat exchangers. They are essential for energy efficiency, but even minor issues can create significant downtime and cost. Not long ago, we encountered a serious fouling issue in one of our exchangers. The deposits were reducing heat transfer efficiency, causing higher energy consumption and forcing frequent shutdowns for cleaning. 🔍Instead of treating it as just another maintenance task, we carried out a detailed root cause analysis: • Reviewed process conditions and flow patterns. • Checked velocity and temperature profiles. • Involved both the operations and maintenance teams in the discussion. The findings showed that low fluid velocity was the main driver for fouling. By redesigning the piping layout and adjusting the operating parameters, we were able to: ✅ Increase turbulence and reduce fouling. ✅ Extend cleaning cycles from every 3 months to once a year. ✅ Achieve over 15% improvement in efficiency. For me, the key takeaway is that every technical problem is also an opportunity to innovate and improve reliability. Collaboration and data-driven decisions can transform a recurring issue into a long-term success.

🛠️ One question I get asked often is: How do experienced technicians diagnose faults so quickly, sometimes in minutes, without running every test in the book? The answer lies in a mix of intuition, logic, and years of pattern recognition. Here’s what’s actually happening behind the scenes: 1️⃣ Sensory awareness → They listen to strange noises, feel vibrations, smell burning insulation, their senses are tuned like instruments. 2️⃣ Pattern memory → They’ve seen it before, not once, but dozens of times. And their brain stores those symptoms like mental flashcards. 3️⃣ Isolation technique → They rule out what’s working before chasing what’s not. This narrows the field, fast. 4️⃣ Start simple → They don’t jump to complex solutions. They check the basics first power, connections, settings, alignments. 5️⃣ Ask the right questions → Often, the operator holds the key. A simple, “When did this start?” or “What changed recently?” reveals more than a sensor scan. 6️⃣ Calm under pressure → They don’t panic. They pause, observe, and act methodically, even when the clock is ticking. Why does this matter beyond engineering? Because this troubleshooting mindset applies everywhere: → When leading teams → Solving business problems → Or making personal decisions under pressure The best problem-solvers don’t just rely on tools, they develop awareness, stay calm, and trust their process. So next time you face a complex challenge, don’t rush. Slow down. Ask the right questions. Start simple. And trust that every problem has a pattern, you just have to learn to see it. What’s your go-to method when troubleshooting something under pressure? #Troubleshooting #EngineeringMindset #TechnicalExcellence #STEMCareers #ProblemSolving #SkilledTrades

It was a 2 hour drive to the next service appointment. I was riding with a senior tech who didn’t seem too thrilled to have “the tech guy from California” in the truck. I shared why the owner chose him to have me ride along. “He said you’re the expert and if you can’t break it, he’ll buy it.” He laughed. He said, “I don’t know about all that. I just don’t stop asking why until I get to the root cause.” You sound like my 6 year old. He chuckled and explained most guys look for the quick fix. But in machines everything happens for a reason. The puzzle is figuring out why. You have to just keep asking why until there is no other reason like they did at Toyota. Sakichi Toyoda is credited with the 5 whys methodology. This is a problem-solving technique that aims to identify the root cause of an issue by repeatedly asking "Why?" typically five times. It encourages deeper analysis beyond surface-level symptoms, helping to uncover underlying causes that may not be immediately apparent. By addressing these root causes, the method promotes more effective and lasting solutions to problems, rather than quick fixes that only treat symptoms. The legend goes, an automatic loom kept shutting down. Rather than simply fixing the malfunctioning part, the team kept asking why: Why did the loom stop? The fuse blew due to an overload. Why was there an overload? The bearing wasn’t lubricated enough. Why wasn’t it lubricated enough? The lubrication pump wasn’t working properly. Why wasn’t the pump working? The shaft of the pump was worn out. Why was the shaft worn out? There was no filter to prevent debris from entering the pump. Until they realized the shaft was worn out, they’d have to continually come back and fix the same issue. Getting to the last why requires curiosity, persistence, patience. All hallmarks of an expert.

Problems aren't roadblocks. They're invitations. An invitation to innovate. To rethink. To leap. The difference between stuck and unstoppable? It's not the challenge. It's you. Your lens. Your toolkit. Your willingness to dance with the difficulty. As a tech leader, your ability to solve complex issues can make or break your career. I've led teams across continents, industries, and crises. Here's what I've learned: 𝟭. 𝗥𝗼𝗼𝘁 𝗖𝗮𝘂𝘀𝗲 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 Peel back the layers. Ask "Why?" repeatedly. You're not fixing a leak; you're redesigning the plumbing. 𝟮. 𝗦𝗪𝗢𝗧 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 Map your battlefield. Know your strengths, weaknesses, opportunities, and threats. Sun Tzu would approve. 𝟯. 𝗠𝗶𝗻𝗱 𝗠𝗮𝗽𝗽𝗶𝗻𝗴 Visualize the chaos. Connect the dots. Your brain on paper, minus the mess. 𝟰. 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 Prepare for multiple futures. Be the chess player who sees ten moves ahead. 𝟱. 𝗦𝗶𝘅 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗛𝗮𝘁𝘀 Wear different perspectives. Be the critic, the optimist, the data analyst, the artist, the operator. Your mind is pliable; use it. 𝙒𝙝𝙮 𝙩𝙝𝙞𝙨 𝙢𝙖𝙩𝙩𝙚𝙧𝙨: - 76% of IT leaders rank problem-solving as the top soft skill (Global Knowledge) - Strong problem-solvers are 3.5x more likely to hit strategic goals (Harvard Business Review) - 70% of problem-solving pros drive more innovation (PwC) These aren't just methods. They're mindsets. Tools to reshape your thinking. I've used these to navigate multi-million-dollar projects and multinational teams. They work. Period. But the real differentiator: consistency. Use these daily. Make them habits. Your problem-solving muscle grows with every rep. Start now. Pick one method. Apply it to a current challenge. Share your results. The best tech leaders aren't born. They're forged in the fires of solving complex problems. What will you solve today?

To become a top data analyst you need to be a strong problem solver! Follow this structure to find the real reasons behind business problems: 1. 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Start by clearly stating the issue. For example, “We’ve observed a significant decrease in sales in the UK over the last few days.”   2. 𝗚𝗮𝘁𝗵𝗲𝗿 𝗗𝗮𝘁𝗮: Collect relevant information such as order processing times, customer service interactions, inventory levels, and active marketing campaigns.   3. 𝗔𝗻𝗮𝗹𝘆𝘇𝗲 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮: Use tools like SQL, Python, or Excel to analyze the data. Look for patterns, trends, and anomalies that could point to the root cause.   4. 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗖𝗮𝘂𝘀𝗲𝘀: Brainstorm all possible reasons for the issue. Use methods like the 5 Whys technique to investigate each potential cause more deeply.   5. 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗛𝘆𝗽𝗼𝘁𝗵𝗲𝘀𝗲𝘀: Test your hypotheses against the data to see if they are supported. If not, refine your hypotheses and test again.   6. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀: Once you’ve identified the root cause, support the business by showing possible solutions to address it. Monitor the results to ensure the issue is resolved. 𝗔 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗲𝘅𝗮𝗺𝗽𝗹𝗲 𝗳𝗿𝗼𝗺 𝗺𝘆 𝗽𝗮𝘀𝘁: We notice an increase in customer lead time and here’s how we tackle it. 1. 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: “Customer lead time has increased by 20% in the last three months.”     2. 𝗚𝗮𝘁𝗵𝗲𝗿 𝗗𝗮𝘁𝗮: We collected data on order processing, sales forecast deviation, and shipping times.     3. 𝗔𝗻𝗮𝗹𝘆𝘇𝗲 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮: We found that the actual sales were in line with the forecast, and shipping times had remained constant. However, order processing times had increased significantly.     4. 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗖𝗮𝘂𝘀𝗲𝘀: We checked factors such as outages in warehouses, staffing issues due to high sickness rates, and process inefficiencies resulting from operating close to maximum capacity.     5. 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗛𝘆𝗽𝗼𝘁𝗵𝗲𝘀𝗲𝘀: Data revealed that a spike in the sickness rate had reduced the available workforce.     6. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀: We proposed to increase capacity buffers by 5% to 10% during the winter and hiring additional temporary workers to address the situation in the short term.   Following this approach for your root-cause analysis, you will become a valued problem-solving partner for your stakeholders. How do you ensure you’re addressing the root cause of an issue and not just the symptoms? ---------------- ♻️ 𝗦𝗵𝗮𝗿𝗲 if you find this post useful. ➕ 𝗙𝗼𝗹𝗹𝗼𝘄 for more daily insights on how to grow your career in the data field. #dataanalytics #datascience #rootcauseanalysis #problemsolving #careergrowth

Are you struggling to troubleshoot your tech projects effectively? Try this 5-step process. A lot of us want to fake it 'til we make it. But that won't cut it in tech. Troubleshooting is the real deal. You can't fake that. When I got my certs and started interviewing, I realized I lacked troubleshooting skills the most. Here's a 5-step process to troubleshoot any tech project you have: → Identify the Problem Break it down. Understand what’s going wrong. Get specific. → Isolate the Issue Narrow it down. Is it hardware? Software? Network? Find the root cause. → Research Solutions Look up similar issues. Use forums, documentation, and guides. Gather possible fixes. → Test and Implement Try your solutions one by one. See what works. Document your steps. → Evaluate and Learn Did it fix the problem? If not, go back to step one. Learn from each attempt. Hands-on skills, projects, and certs are important. But troubleshooting? That’s the game-changer. Start learning that today.

Most people chase quick fixes. Here's how experts actually solve problems. The blueprint for solving problems effectively: 1. IDEAL Framework ↳ Identify the problem ↳ Define the context ↳ Explore possible strategies ↳ Act on the best strategy ↳ Look back and learn 2. 5 Whys Technique ↳ Ask "Why?" repeatedly ↳ Dig deeper beyond surface symptoms ↳ Find root causes of problems 3. Design Thinking ↳ Empathise with user needs ↳ Define the problem clearly ↳ Ideate creative solutions ↳ Prototype low-fidelity versions ↳ Test and refine with feedback Expert frameworks for structured problem-solving: PDCA Cycle ↳ Plan: Identify and analyse ↳ Do: Implement solutions ↳ Check: Evaluate results ↳ Act: Standardize or restart OODA Loop ↳ Observe: Collect information ↳ Orient: Analyse and synthesise ↳ Decide: Choose action ↳ Act: Follow through Kepner-Tregoe Method ↳ Situation Appraisal ↳ Problem Analysis ↳ Decision Analysis ↳ Potential Problem Analysis The biggest mistake isn't trying to solve problems. It's not using a systematic approach when needed. ♻️ Reshare to help others solve problems better. 🔔 Follow Luke Tobin for more problem-solving insights.

$50M was at risk. We had 5 days to save it. An incredible resource from my friend Stephanie Hills, Ph.D. (give her a follow) I’ve always been known as a problem solver. Not because I was the smartest person in the room. Not because I had all the answers. Not because I had the best ideas. But because I knew how to think clearly when the pressure was high. I’ll never forget one situation early in my executive career. A $50M customer had already decided to fire us. The relationship was broken. Quality issues were stacking up. Delivery was delayed. Trust was gone. There was no time to debate. No room for politics. No margin for error. Wednesday, I was pulled in. Thursday, I met with our executive team. That same day, I met with the global engineering team. We whiteboarded everything that wasn’t working. Friday, I met with engineering leadership. Saturday and Sunday, I built the plan. Sunday night, I presented it to our executives. Monday, I flew to Canada and met with the customer. We solved the quality issues. We fixed the delays. We kept the account. Not because I was better than anyone else. But because I relied on proven ways of thinking when everything was on the line. Here are the exact frameworks I’ve relied on throughout my career 👇 🧠 7 PROBLEM-SOLVING FRAMEWORKS EXECUTIVES USE 1️⃣ OODA Loop → Speeds up decisions and action in fast-changing situations → When to use: Competitive crises, market shifts, or urgency 2️⃣ DMAIC Framework → Data-driven method to pinpoint issues, measure performance, and test fixes → When to use: Operational and process efficiency, continuous improvement 3️⃣ Root Cause Analysis (5 Whys) → Drills down past symptoms to uncover the true cause → When to use: Recurring failures that keep resurfacing 4️⃣ Pre-Mortem Analysis → Assumes failure in advance to identify risks before they happen → When to use: New initiatives and strategic launches 5️⃣ First Principles Thinking → Breaks problems into fundamental truths and rebuilds from the ground up → When to use: When conventional approaches fail 6️⃣ Six Thinking Hats → Uses parallel thinking to balance facts, emotion, risk, and creativity → When to use: Team alignment and collaboration 7️⃣ Decision Tree Analysis → Maps choices, probabilities, and outcomes visually → When to use: High-stakes decisions with uncertainty This is the difference between reacting and leading. Pressure used to test your leadership. Now it tests how well you use AI. Leaders must guide it. High achievers must master it. Because AI is becoming part of every decision, every workflow, and every team. If you want to confidently lead with AI in your work, products, and organization, join Stephanie’s AI Readiness Masterclass: 🔗 stephanieshills.com/ai Move faster. Make better decisions. Avoid costly mistakes. ♻️ Repost to help another leader make decisions under pressure