Project Management Methodologies

If I had to setup a PMO today, Here's what I'd do: Step 1: See how things really are ↳ Interview execs, sponsors, PMs, and business leads ↳ Map all current projects - active, planned, and stalled ↳ Benchmark maturity across processes, tools, and culture ↳ Identify pain points (missed deadlines, ROI leakage, siloed teams) Step 2: Figure out how they should actually be ↳ Align with executives on “why the PMO exists” ↳ Lock in sponsorship to protect the PMO’s mandate ↳ Clarify which business units and geographies the PMO supports ↳ Define KPIs: cycle time, benefits realization, stakeholder trust, etc ↳ Decide scope: standards, governance, delivery, or strategy partner Step 3: Lay the groundwork ↳ Draft a RACI for PMO vs. execs vs. PMs ↳ Stand up intake and prioritization workflows ↳ Pinpoint quick wins the PMO can solve immediately ↳ Pick a starter toolset - Excel, Smartsheet, or light PPM ↳ Define governance checkpoints that enable - not delay - delivery ↳ Set lightweight standards (scope, schedule, risk, status reporting) Step 4: Pilot with purpose ↳ Select 1–2 projects with high visibility and executive sponsorship ↳ Apply the PMO framework in real time - don’t over-engineer ↳ Track value delivered vs. “old way” of running projects ↳ Package results into a case study to showcase impact ↳ Capture lessons learned in a living playbook Step 5: Roll out & roadshow ↳ Position PMO as an enabler - solving pain points, not adding burden ↳ Conduct PMO “roadshows” to share wins and benefits org-wide ↳ Create cheat sheets, quick guides, and templates for adoption ↳ Scale pilot practices across 3–5 additional projects ↳ Train PMs and sponsors on new processes Step 6: Measure & share ↳ Compare portfolio spend vs. strategic value delivered ↳ Share updates regularly with executives to build trust ↳ Use metrics to secure more resources and influence ↳ Report on benefits realized, not just activities done ↳ Create dashboards with one version of the truth Step 7: Take the next stride ↳ Update frameworks based on adoption, not theory ↳ Run quarterly PMO retrospectives with stakeholders ↳ Gather qualitative feedback (ease of use, clarity, impact) ↳ Push toward the next level of maturity without losing agility ↳ Expand into advanced areas (portfolio mgmt, benefits tracking, AI tools) ⚠️ What I’d avoid at all costs: ↳ Measuring success by reports produced instead of value delivered ↳ Trying to impose control instead of building credibility first ↳ Rolling out a PPM tool before fixing processes ↳ Starting with 50 templates nobody asked for 💡 If you had to build a PMO from scratch tomorrow, which step would you double down on first? -- ♻️ Repost to help PMOs succeed! 🔔 Follow me (Hussain Bandukwala) for more content like this.

Anthropic 𝗷𝘂𝘀𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝗮 𝗱𝗲𝗻𝘀𝗲 𝗮𝗻𝗱 𝗵𝗶𝗴𝗵𝗹𝘆 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗿𝗲𝗽𝗼𝗿𝘁 𝗼𝗻 𝗵𝗼𝘄 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 — 𝗽𝗮𝗰𝗸𝗲𝗱 𝘄𝗶𝘁𝗵 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀: ⬇️ Not just marketing, BUT a real, practical blueprint for developers and teams building AI agents that actually work. It explains how Claude Code (tool for agentic coding) can function as a software developer: writing, reviewing, testing, and even managing Git workflows autonomously. BUT in my view: The principles and patterns described in this document are not Claude-specific. You can apply them to any coding agent — from OpenAI’s Codex to Goose, Aider, or even tools like Cursor and GitHub Copilot Workspace. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 7 𝗸𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗼𝗿 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗯𝗲𝘁𝘁𝗲𝗿 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 — 𝘁𝗵𝗮𝘁 𝘄𝗼𝗿𝗸 𝗶𝗻 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝘄𝗼𝗿𝗹𝗱: ⬇️ 1. 𝗔𝗴𝗲𝗻𝘁 𝗱𝗲𝘀𝗶𝗴𝗻 ≠ 𝗷𝘂𝘀𝘁 𝗽𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 ➜ It’s not about clever prompts. It’s about building structured workflows — where the agent can reason, act, reflect, retry, and escalate. Think of agents like software components: stateless functions won’t cut it. 2. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗶𝘀 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 ➜ The way you manage and pass context determines how useful your agent becomes. Using summaries, structured files, project overviews, and scoped retrieval beats dumping full files into the prompt window. 3. 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 𝗶𝘀𝗻’𝘁 𝗼𝗽𝘁𝗶𝗼𝗻𝗮𝗹 ➜ You can’t expect an agent to solve multi-step problems without an explicit process. Patterns like plan > execute > review, tool use when stuck, or structured reflection are necessary. And they apply to all models, not just Claude. 4. 𝗥𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗮𝗴𝗲𝗻𝘁𝘀 𝗻𝗲𝗲𝗱 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘁𝗼𝗼𝗹𝘀 ➜ Shell access. Git. APIs. Tool plugins. The agents that actually get things done use tools — not just language. Design your agents to execute, not just explain. 5. 𝗥𝗲𝗔𝗰𝘁 𝗮𝗻𝗱 𝗖𝗼𝗧 𝗮𝗿𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀, 𝗻𝗼𝘁 𝗺𝗮𝗴𝗶𝗰 𝘁𝗿𝗶𝗰𝗸𝘀 ➜ Don’t just ask the model to “think step by step.” Build systems that enforce that structure: reasoning before action, planning before code, feedback before commits. 6. 𝗗𝗼𝗻’𝘁 𝗰𝗼𝗻𝗳𝘂𝘀𝗲 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝘆 𝘄𝗶𝘁𝗵 𝗰𝗵𝗮𝗼𝘀 ➜ Autonomous agents can cause damage — fast. Define scopes, boundaries, fallback behaviors. Controlled autonomy > random retries. 7. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝘃𝗮𝗹𝘂𝗲 𝗶𝘀 𝗶𝗻 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 ➜ A good agent isn’t just a wrapper around an LLM. It’s an orchestrator: of logic, memory, tools, and feedback. And if you’re scaling to multi-agent setups — orchestration is everything. Check the comments for the original material! Enjoy! Save 💾 ➞ React 👍 ➞ Share ♻️ & follow for everything related to AI Agents!

So I just had an interview and I was asked about a project playbook Creating a Project Playbook is one of the most strategic things a PMO leader or senior project manager like you can do. It’s a living document (or toolkit) that standardizes how projects are run across the organization—so you’re not starting from scratch each time. It helps ensure consistency, clarity, and quality in execution, and is a great way to demonstrate leadership, scalability, and process maturity. Section Description 1. Introduction & Purpose Why the playbook exists and who it’s for (e.g., PMs, stakeholders, business leads). 2. Project Lifecycle Define stages: Initiation, Planning, Execution, Monitoring/Control, Closure. 3. Governance & Roles Clarify who does what—PM, sponsor, stakeholders, steering committee. 4. Methodologies Outline which methodologies apply (Agile, Waterfall, Hybrid) and when to use them. 5. Tools & Systems Standard PM tools (e.g., Smartsheet, Jira, Asana) and how they’re used. 6. Templates & Checklists Links to essential templates: project charter, RACI, risk log, status report, change request, etc. 7. Communication & Reporting Stakeholder communication plan, meeting cadences, and reporting expectations. 8. Risk, Issue & Change Management Processes for identifying, escalating, and resolving issues or changes. 9. KPIs & Success Metrics What success looks like (on time, budget, scope, outcomes). 10. Lessons Learned & Continuous Improvement Process for retrospectives and embedding lessons into future work.

🚢 How To Launch Big Complex Projects. How to reduce costs and schedule overruns, manage risks and be prepared for an unlucky turn of events ↓ 🤔 99.5% of big projects overrun budgets and schedules. 🤔 These are big relaunches, legacy re-dos, big initiatives. 🚫 Adding 15–20% buffer time/costs rarely saves them. ✅ Complex projects often follow “fat-tailed” distribution. ✅ There, overruns of 60–500% turn into big disasters. 🚫 Beware of unchecked optimism → unrealistic forecasts. 🚫 Beware of “cutting-edge” → untested technology spirals risk. 🚫 Beware of “bespoke/unique” → high chance of exploding costs. 🚫 Beware of “brand new team” → rely on tested and reliable teams. 🚫 Beware of “most advanced” → build small things, then compose. 🤔 The only way to prevent big disasters is to plan more. ✅ Best strategy: Think Slow (designing) + Act Fast (delivery). ✅ Good planning includes experiments, tests, simulations. ✅ Reference-class forecast → study mean actual cost, time. ✅ Track and review past projects in your company (cost, time). Things almost never go according to the plan — and on complex projects, they don’t even come close. We often assume that if we just thoroughly collect all the costs needed and estimate complexity or efforts, we should get a decent estimate of where we will eventually land. Nothing could be further from the truth. Complex projects have plenty of unknown unknowns. No matter how many risks and dependencies and upstream challenges we identify, there are many more we can’t even imagine. The best way to be more accurate is to define a realistic anchor — for time, costs and benefits — from similar projects done in the past. That’s what Prof. Bent Flyvbjerg calls reference-class forecasting (RCF) — experience-based, real-world outcomes that shape our estimates. No project is a snowflake; it always shares similarities with other projects. And however meticulous our calculations are, they usually approximate best-case-scenarios. Complex projects start with a deep deficit of experience. To increase the chances of success, we need to minimize the chance of mistakes even happening. That means trying to make the process as repetitive as possible — with smaller “work modules”, repeated by teams over and over again. It also means relying on reliable: from well-tested technology to stable teams that have worked well together in the past. And: always spend a bit more time planning, experimenting, testing and refining the plan before drawing a single pixel on the screen. It will pay off big time — every single time. I can only wholeheartedly recommend a book on “How Big Things Get Done” by Prof. Bent Flyvbjerg and Dan Gardner which goes in all the fine detail of how big project fails and when they succeed. It's not a book about design, but a fantastic book for designers who want to plan and estimate better. #ux #design

💡Triple Diamond Design Process The "Triple Diamond" process is a process that builds upon the widely known Double Diamond design process. While the Double Diamond focuses on two main phases—problem definition and solution design—the Triple Diamond adds a third phase to add depth and breadth to the design methodology.  This variant of a triple diamond process, crafted by Ted Goas (https://lnkd.in/eJFCR8rF), adds a third diamond for iterative development. It emphasizes iterative cycles, prioritization of user needs, and continuous refinement of the solution throughout the product lifecycle. Quick overview of the 5 key phases of this process: 1️⃣ Discovery (What’s our problem?) This phase focuses on identifying the problem to solve. Goal: Understanding customer pain points & narrowing down insights into actionable focus areas. Activities: ✔ Customer empathy budding: Researching user needs. ✔ Market research: Analyzing market trends. ✔ Competitive analysis: Assessing competition. ✔ Insights prioritization: Organizing findings for strategic focus. ✔ Building product strategy: Setting goals for the product. 2️⃣ Definition (What’s our solution?) This phase focuses on solution ideation & validation. Goal: Generate multiple ideas, structure them and validate the most promising ideas Activities: ✔ Ideation: Brainstorming and generating ideas. ✔ Drafting experience workflow: Mapping out how users will interact with the solution. ✔ Wireframeing: Visualizing the solution. ✔ Initial prototyping: Creating early product models for testing. 3️⃣ Development (Let’s build our solution) This phase is about building, iterating, and refining the product. Goal: Breaking down features and iterating to reduce risks. Activities: ✔ Feature breakdown: Breaking the solution into smaller deliverable tasks. ✔ Iterative build cycle: Continuously building and improving the product. ✔ Collecting research insights: Using feedback to refine features. 4️⃣ Distribution (Initial customer feedback) Focuses on testing the product with users and preparing for the final release. Phases: ✔ Internal release: Early internal testing (alpha and beta testing) ✔ Early access program: Collecting feedback from early adopters. ✔ General (Public) release: Launching the product publicly. 5️⃣ Retro (What did we learn?) Post-release reflection phase to gather insights for future iterations. Using insights collected from feedback, metrics, and retrospective discussions to refine the product. 📕 A Comprehensive guide to product design process https://lnkd.in/eyh4YGy6 #design #designprocess #ux #uxdesign #productdesign #uidesign #ui

Project Management Methods Explained Agile (Purple) Imagine you're building with Lego blocks, but instead of following all the instructions at once, you build a small part, show it to your friends, get their ideas, and then make it better. Then you move on to the next part. With Agile, teams work in short "sprints" where they build a little bit at a time, check if it's working, and make changes quickly if needed. It's like taking small steps instead of giant leaps! Kanban (Blue) Think of Kanban like your classroom's job chart or a chore board at home. Everyone can see what needs to be done, what's being worked on right now, and what's finished. It's like sticky notes on a board that move from "To Do" to "Doing" to "Done." This helps everyone know exactly what's happening without having to ask! Lean (Orange) Lean is like cleaning up your room by getting rid of things you don't need. If you're making a sandwich, Lean would ask: "Do we really need to use three knives when one would work?" It helps teams remove extra steps or wasted time. If something doesn't make your project better, Lean says to remove it! Waterfall (Pink) Waterfall is like following a recipe when baking a cake. You have to mix the ingredients before you can put it in the oven, and you have to bake it before you can frost it. Each step must be completed before moving to the next one, and it's hard to go back once you've moved forward. It works well when the steps are clear and won't change. Six Sigma (Green) Six Sigma is like being a detective who solves the mystery of why things go wrong. If your lemonade stand isn't selling enough drinks, Six Sigma helps you figure out exactly why and fix it. Maybe the cups are too small, or your sign isn't visible enough. It uses math and careful observation to make processes better and more consistent. Each of these methods helps people work together to create things or solve problems, just in different ways—like having different strategies for winning different games!

Software Engineering Methodologies: A Comprehensive Guide Let's dive into 12 key methodologies that shape modern software development: 1. Waterfall: The classic linear approach. Best for projects with well-defined, stable requirements. It's structured but inflexible, moving through phases like requirements, design, implementation, testing, and maintenance sequentially. 2. Agile: Embraces change and iterative development. Ideal for projects with evolving requirements or needing constant customer feedback. It focuses on delivering small, workable increments rapidly. 3. Incremental Development: Breaks projects into manageable chunks. Each increment adds functionality, allowing for gradual development and frequent testing. It's a middle ground between Waterfall and Agile. 4. Scrum: An Agile framework emphasizing teamwork and adaptability. It uses sprints (usually 2-4 weeks) to deliver incremental value, with roles like Scrum Master and Product Owner guiding the process. 5. V-Model (Verification and Validation): Extends Waterfall by incorporating testing at every stage. It's rigorous and suits projects where quality assurance is paramount. 6. Kanban: Visualizes workflow to optimize efficiency. It's excellent for projects requiring continuous delivery and flexibility in changing priorities. Work-in-progress limits are key. 7. Extreme Programming (XP): Customer-centric and quality-focused. It emphasizes practices like pair programming, continuous integration, and test-driven development. Great for projects with frequently changing requirements. 8. Spiral Model: Risk-driven approach combining iterative development with Waterfall's systematic aspects. Suitable for large, complex projects with high risk levels. 9. Rapid Application Development (RAD): Prioritizes rapid prototyping and quick feedback cycles. It's ideal for projects needing fast delivery with evolving requirements. 10. Feature-Driven Development (FDD): Focuses on developing features one at a time. It suits large-scale projects where feature-based development can be effectively managed. 11. Lean: Emphasizes eliminating waste and maximizing value to the customer. It streamlines processes and is great for projects needing to optimize resources and speed. 12. Prototype Model: Develops a working prototype early to clarify requirements. It's beneficial for projects with unclear or evolving requirements where early user feedback is crucial. P.S. It's crucial to remember that no single methodology is universally superior. The most effective approach often involves tailoring methods to your specific project requirements, team dynamics, and organizational culture. Be prepared to adapt and potentially blend different methodologies as needed. Regularly assess your processes and be willing to adjust your approach to optimize efficiency and outcomes. The goal is to select and implement methodologies that best serve your project's unique needs and constraints.

When a Quality Manager join a new company, how he must start his working in professionally and effectively for improvement , step by step.. *Phase 1: Familiarization and Foundation Building 1. Review Company Policies and Procedures 2. Meet with Key Personnel's of all departments 3. Conduct a thorough tour of the facility to understand operations, identify potential quality risks, and get a sense of the company culture. 4. Examine quality records, including audit reports, customer complaints, and corrective actions to understand the company's quality performance. *Phase 2: Assessment and Gap Analysis 1. Evaluate quality processes, such as inspection, testing, and calibration to identify gaps and inefficiencies. 2. Identify potential quality risks, including supply chain risks, equipment risks, and process risks. 3. Analyze quality data, including defect rates, customer satisfaction, and supplier performance to identify trends and areas for improvement. 4. Develop a comprehensive report outlining the gaps and inefficiencies in the quality management system. *Phase 3: Setting Key Performance Indicators (KPIs) and Targets 1. Establish quality objectives, including defect reduction, customer satisfaction improvement, and supplier performance enhancement. 2. Develop KPIs to measure quality performance, including defect rates, customer satisfaction, and supplier performance. 3. Set targets and benchmarks for each KPI based on industry standards, customer requirements, and company goals. 4. Communicate KPIs and targets to relevant stakeholders, including department heads, supervisors, and quality team members. *Phase 4: Quality improvements plan 1. Prioritize areas for improvement based on the gap analysis report and quality data analysis. 2. Develop corrective actions to address gaps and inefficiencies in the quality management system. 3. Establish timelines and responsibilities for implementing corrective actions. 4. Develop a comprehensive quality improvement plan outlining the corrective actions, timelines, and responsibilities. *Phase 5: Implementation and Monitoring 1. Implement corrective actions outlined in the quality improvement plan. 2. Regularly monitor progress against KPIs and targets. 3. Continuously evaluate and improve the quality management system to ensure it remains effective and efficient. 4. Communicate results to relevant stakeholders, including department heads, supervisors, and quality team members. Countermeasures for inefficiencies- 1. Streamline processes to reduce waste and increase efficiency. 2. Implement lean principles to minimize waste and maximize value. 3. Provide training and development opportunities to enhance employee skills and knowledge. 4. Foster open communication across departments and levels to ensure quality issues are identified and addressed promptly. 5. Conduct regular audits to ensure compliance with quality standards and identify areas for improvement.

The biggest lie I was sold in corporate: “Get the strategy right first. Execution can wait” Sounds smart, right? I did that once at a corporate. Six months of building a strategy deck...meetings, alignment calls, and PPTs that could win design awards. And finally the day we got approval? The market’s already moved on. Our “perfect” plan solved yesterday’s problem. Fast forward to AdOnMo, Year 1. Big client. Urgent need. Monday morning call. Corporate me almost said, “Let’s come back with a perfect plan next week.” Startup me said, “We’ll have a pilot ready by Thursday.” 72 hours. No slides. No strategy doc. Just execution. It wasn’t perfect. But it worked. We won the client, not because we had the best plan, but because we showed up first. Corporate taught me: Plan for perfection. Startups taught me: Perfect kills momentum. In India, your customer won’t wait for your deck. Your competitor isn’t waiting for your roadmap. Speed isn’t reckless. Sometimes, speed is the strategy! So ask yourself every day: “What are you still planning…that you should already be doing?” #Startups #Execution #Strategy #Entrepreneurship #AdOnMo

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