Problem-Solving Skills Development

We make decisions all the time, some small, some big. But how do we know we have made the right decision? The 4Cs of Excellent Decisions help you assess this upfront. Knowing whether a decision is good or bad is easy after the fact, once you know the results. The challenge is to know this at the time of making the decision. This applies to every decision, but becomes more important when the decision is • Complex • High impact • Costly • Irreversible Strategic decisions typically have all these four characteristics. This makes it particularly important to assess the quality of your strategic decisions before you actually make them. The follow criteria will help: CLEAR Is the decision clear, concrete and understood on all its facets?  Is it clear why it is made? Is it clear what the consequences are? Is it clear what it takes to implement it? CORRECT Is the underlying evidence accurate and correct? Are the assumptions leading to this decision correct? Is there sufficient qualitative or quantitative data to support the decision? Has the process for making this decision been correct? COMPLETE Are all important factors taken into account? Is the information needed to make this decision complete? Have all relevant stakeholders been involved? Have all alternatives been taken into account? CONSENSUS Is there a shared understanding of the decision and its context? Do relevant stakeholders agree on the decision made? Have all objections been carefully considered? Is it clear how to deal with those stakeholders that do not agree? These are the 4Cs of Excellent Decisions. Use them to evaluate all your important decisions. Think about your last key decision, did it fulfill all four criteria? === If you like this, you also like my The Strategic Leadership Playbook, which contains 64 tools like this with clear instructions for how to use them. Buy the book here: https://lnkd.in/eGz9AZwP #decisionmaking #leadershipgrowth #businessmanagement

I don’t understand the shame associated with making mistakes. No one is perfect. No one delivered high-quality work on their first attempt. High-quality work IS the outcome of mistakes reflected upon. Use the RISE framework to turn any mistake into progress: R – Recognize Identify the mistake clearly without defensiveness. I – Investigate Ask why it happened. Was it a skill gap, a misunderstanding, or a process flaw? S – Synthesize Turn the insight into a principle or takeaway. E – Execute Differently Apply the learning immediately in your next project or task. Making mistakes is not the mistake. Not learning from them is. Quote from the book: It Always Seems Impossible Until It's Done. PS: If you found this helpful, I share more insights to help you build a growth mindset. Follow along if you’re on the same journey.

I had a call with a YC founder building a billion-dollar company in an AI-native way, and he phrased the new world pretty well. The simplest path to building a $1B+ company? It's not fundraising. It's not team building. It's not even product. It's collecting insights before you need capital. Here's what most founders miss: 1/ The biggest mistake founders make: Raising millions before understanding their market. A YC founder who's building a unicorn told me: "The only thing lacking from building a large company isn't money - it's insights." Here's why this matters: 2/ The landscape has changed dramatically: • AWS made infrastructure cheap • No-code tools reduced dev costs • AI accelerated development • Remote work lowered overhead Result? You need 90% less capital than 10 years ago to start. 3/ But here's what hasn't changed: You still need deep market insights to win. Look at the most successful founders: • Brian Chesky (Airbnb) - Lived the problem • Patrick Collison (Stripe) - Felt the pain firsthand • Tobi Lütke (Shopify) - Built for his own needs 4/ The modern playbook is backwards: ❌ Raise millions ❌ Hire a team ❌ Then figure it out ✅ Gather insights ✅ Test assumptions ✅ Build minimal solution ✅ Let customers pull you forward 5/ How to become an insight-gathering machine: • Talk to 100 potential customers • Join industry Discord servers • Attend niche conferences • Follow practitioners, not influencers • Build side projects in your space 6/ The math of insights: • Every conversation = 1 new insight • Every insight = 10% better product • Every improvement = 2x easier sale • Every sale = 3 more conversations It compounds rapidly. 7/ Signs you have enough insights: • You can predict customer objections • You know the market size firsthand • You understand why others failed • You have customers asking to pay 8/ The secret most founders miss: Money amplifies execution. But insights determine direction. Without insights, more money just helps you go in the wrong direction faster. 9/ Your first job as a founder: Become the most knowledgeable person in your space. Not through: • Reading blogs • Watching videos • Following trends But through: • Direct conversations • Real experiments • Hands-on experience The answer to the "What's your insight" question is worth millions.

In high-stakes decisions, “right” and “wrong” aren’t the point. Your method for making decisions matters more than any single result. Every major choice is a bet on a particular future. Decision quality and outcome quality are two entirely different things. Our brains want tidy stories, so we judge a decision’s quality by its outcome — a bias known as resulting. A brilliant process can still produce a bad outcome because of one unlucky break. Pete Carroll’s infamous Super Bowl call to pass from the 1-yard line was statistically sound, yet it’s reviled because it ended in a game-losing interception. To escape the trap of resulting, you need a better process. The world’s best venture capitalists use repeatable frameworks that protect them from bias and focus their attention where it matters most. Their playbook starts with two disciplines: 𝟭. 𝗦𝗼𝗿𝘁 𝘆𝗼𝘂𝗿 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀: 𝗢𝗻𝗲-𝗪𝗮𝘆 𝘃𝘀. 𝗧𝘄𝗼-𝗪𝗮𝘆 𝗗𝗼𝗼𝗿𝘀 Jeff Bezos uses this mental model to allocate energy: “Two-way doors” are reversible — make those decisions quickly quickly. “One-way doors” are consequential and nearly irreversible, so you should take them slow and deliberate. The first step to better decisions is knowing which door you’re facing. 𝟮. 𝗛𝘂𝗻𝘁 𝗳𝗼𝗿 𝗮𝘀𝘆𝗺𝗺𝗲𝘁𝗿𝗶𝗰 𝗯𝗲𝘁𝘀 Stop worrying about avoiding failure and start making sure your wins are big enough to make failures irrelevant. Don’t just assess the most likely outcome. Map the full range of possibilities. A bet with a 70% chance of a small loss but a 10% chance of a 100x return can be a career-defining win. Top VCs know they’ll be wrong most of the time. In fact, they’re not aiming to be right every time. They’re looking for situations where the upside of a win is exponentially larger than the downside of a loss. I’ll be diving deeper into the methodology behind high-quality decisions in my fall Maven cohort. It’s designed for entrepreneurs, investors, and exec decision makers who have to make dozens of decisions each day. Every decision is a bet on a forecast of the future. You have limited resources to figure out which prediction will have the best return in the long run. In my course, I’ll cover my 6-step process for better, faster decision making: https://bit.ly/4ljImns

A sluggish API isn't just a technical hiccup – it's the difference between retaining and losing users to competitors. Let me share some battle-tested strategies that have helped many  achieve 10x performance improvements: 1. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗖𝗮𝗰𝗵𝗶𝗻𝗴 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 Not just any caching – but strategic implementation. Think Redis or Memcached for frequently accessed data. The key is identifying what to cache and for how long. We've seen response times drop from seconds to milliseconds by implementing smart cache invalidation patterns and cache-aside strategies. 2. 𝗦𝗺𝗮𝗿𝘁 𝗣𝗮𝗴𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 Large datasets need careful handling. Whether you're using cursor-based or offset pagination, the secret lies in optimizing page sizes and implementing infinite scroll efficiently. Pro tip: Always include total count and metadata in your pagination response for better frontend handling. 3. 𝗝𝗦𝗢𝗡 𝗦𝗲𝗿𝗶𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 This is often overlooked, but crucial. Using efficient serializers (like MessagePack or Protocol Buffers as alternatives), removing unnecessary fields, and implementing partial response patterns can significantly reduce payload size. I've seen API response sizes shrink by 60% through careful serialization optimization. 4. 𝗧𝗵𝗲 𝗡+𝟭 𝗤𝘂𝗲𝗿𝘆 𝗞𝗶𝗹𝗹𝗲𝗿 This is the silent performance killer in many APIs. Using eager loading, implementing GraphQL for flexible data fetching, or utilizing batch loading techniques (like DataLoader pattern) can transform your API's database interaction patterns. 5. 𝗖𝗼𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 GZIP or Brotli compression isn't just about smaller payloads – it's about finding the right balance between CPU usage and transfer size. Modern compression algorithms can reduce payload size by up to 70% with minimal CPU overhead. 6. 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻 𝗣𝗼𝗼𝗹 A well-configured connection pool is your API's best friend. Whether it's database connections or HTTP clients, maintaining an optimal pool size based on your infrastructure capabilities can prevent connection bottlenecks and reduce latency spikes. 7. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗟𝗼𝗮𝗱 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 Beyond simple round-robin – implement adaptive load balancing that considers server health, current load, and geographical proximity. Tools like Kubernetes horizontal pod autoscaling can help automatically adjust resources based on real-time demand. In my experience, implementing these techniques reduces average response times from 800ms to under 100ms and helps handle 10x more traffic with the same infrastructure. Which of these techniques made the most significant impact on your API optimization journey?

Why do some qualitative studies generate groundbreaking insights while others barely scratch the surface? The secret is not in the data collected, but in matching your methodology to your research goals. The 5 qualitative research methods nobody talks about: 1. Phenomenology • Perfect for understanding perceptions • Uses deep interview analysis • Captures lived experiences 2. Ethnography • Based on extended fieldwork • Documents cultural patterns • Gives insider perspective 3. Narrative Inquiry • Uses conversations & artifacts • Finds patterns in experiences • Tells people's stories 4. Case Study • Answers specific questions • Uses multiple data sources • Creates rich context 5. Grounded Theory • Perfect for unexplored topics • Analyzes data continuously • Builds new theories Pick your method based on your goal: → Want experiences? Use phenomenology → Need cultural insights? Try ethnography → Looking for stories? Go narrative → Seeking answers? Case study works → Building theory? Grounded theory fits Most researchers fail because they pick the wrong method for their research question. The right method = better research. 🗞️ Join 7,278+ researchers on my weekly newsletter: https://lnkd.in/e4HfhmrH P.S. Do you check method-research-question fit?

In my 2 years of navigating #Data_Analyst #interviews with leading product-based companies, I've decoded a consistent strategy for cracking business case study problems. Sharing here for those prepping for the big leagues: 1. Summarize the Question - First things first, echo the challenge. This not only showcases your grasp of the issue but sets the stage for your analytical journey. 2. Verify the Objective - Zoom in on what success truly means for the scenario at hand. It's like picking the right target before drawing the bow. 3. Ask Clarifying Questions - Dig deeper. Every detail can be a golden nugget in understanding the multifaceted context of the business puzzle. 4. Label the Case and Lay Out Your Structure - Identify the case type and map out your attack plan. A structured framework is your best ally in a sea of data. 5. State Your Hypothesis - Lead with an educated guess. It not only directs your subsequent analysis but also signals your critical thinking prowess. Whether you're a fresh grad or a seasoned professional, mastering these steps is key to presenting your analytical acumen in interviews. Remember, every case is unique, and flexibility is your friend. These steps are just the beginning of the conversation.Would love to hear how others approach case studies in interviews! Share your thoughts below. 👇 Follow Shakra Shamim for more such posts. #BusinessCaseStudy #InterviewPreparation #DataAnalyst

Mistakes don’t define you. How you respond to them does. Every mistake hides a lesson. This framework will help you uncover it. Whether you’re running a business, leading a team, or managing your own life, navigating challenges can feel like uncharted waters. Mistakes happen, but they’re where the magic of growth begins. Let me share a simple framework that changed the way I approach challenges. It's based off of the U.S. Army’s After Action Review method. This framework can help you take any 'failure' - in business, leadership, or life - and turn it into wisdom through 5 reflective questions: 1/ What was planned? ↳ Set clear business goals and success metrics. Example: "We planned to increase website traffic by 50% in Q1 through a targeted social media ad campaign, with a clear focus on driving sign-ups for our newsletter." 2/ What actually unfolded? ↳ Track your progress honestly. ↳ Note both wins and setbacks. Example: "The campaign doubled website traffic, but only 10% of visitors signed up for the newsletter. Additionally, most clicks came from an unexpected age group (18-24)." 3/ What did you learn? ↳ Identify gaps between expectations and reality. ↳ Uncover growth opportunities. Example: "We discovered that our messaging resonated with a younger demographic, which wasn’t our original target audience. The landing page design was also too cluttered, which likely discouraged sign-ups." 4/ What can you do differently next time? ↳ Use your insights to shape a clear strategy. ↳ Create concrete action steps. Example: "We’ll redesign the landing page with a clean, minimal layout and a clearer call-to-action. We’ll also create personalised ads to better target both our intended audience and the younger demographic who engaged." 5/ Where can we try these new ideas next time? ↳ Identify upcoming business decisions. ↳ Apply your insights immediately. Example: "We’ll apply these changes to our next product launch campaign. Specifically, we’ll use the improved landing page design and split-test targeted ads to ensure higher engagement and conversion." Here’s what I want you to remember: ↳ Every challenge you face is shaping you into a stronger, more resilient leader - whether in business, leadership, or life. ↳ You have the power to learn, grow, and improve every time. ⤵️ What’s the most important lesson a mistake has taught you? ♻️ Know someone navigating challenges? Share this post to help them turn mistakes into their next breakthrough. 🔔 Follow me, Jen Blandos, for more practical tips on business growth and leadership.

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