Ivan Jovanovic

Data leaders significantly impact team success by understanding the business, acting as a liaison between business and data, and ensuring data quality. They focus on functional steps rather than technical details, define terms and functions clearly, and assign roles intentionally. This approach helps avoid decentralized processes and makes the data team irreplaceable in company initiatives

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Learning about root-cause analysis will turn you into a problem-solving expert. Here’s how to uncover the real reasons behind your issues! 1. 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Start by clearly stating the issue. For example, “We’ve observed a significant decrease in sales in France over the last few days.”     2. 𝗚𝗮𝘁𝗵𝗲𝗿 𝗗𝗮𝘁𝗮: Collect the relevant data like 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 dig deeper into each potential cause.     5. 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗛𝘆𝗽𝗼𝘁𝗵𝗲𝘀𝗲𝘀: Test your hypotheses against the data. Does the data support your assumptions? 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. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: You notice an increase in customer lead time. Here’s how you might tackle it: • 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: “Customer lead time has increased by 20% in the last three months.”    • 𝗚𝗮𝘁𝗵𝗲𝗿 𝗗𝗮𝘁𝗮: Collect data on order processing, sales deviations from the forecast, and shipping times.    • 𝗔𝗻𝗮𝗹𝘆𝘇𝗲 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮: You find that the actual sales are in line with the forecast and  shipping times have remained constant, but order processing times have increased significantly.    • 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗖𝗮𝘂𝘀𝗲𝘀: Consider factors like outages in the warehouses,  staffing issues due to high sickness rates, or process inefficiencies due to operating close to the maximum capacity.    • 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗛𝘆𝗽𝗼𝘁𝗵𝗲𝘀𝗲𝘀: Data reveals that a spike in the sickness rate has reduced the available workforce.    • 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀: You propose an increase in capacity planning puffer from 5% to 10% and the hiring of additional temporary workers to fix the situation in the short term. Monitor lead times to ensure improvement. By learning how to perform proper root-cause analysis, you will become an important 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

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🎲 The Evolution of Game Theory & Decision Theory: From Pascal to Nash and Beyond 🚀 By Harry Stendhal Game theory and decision theory have a rich history, evolving from simple concepts of probability to becoming cornerstones of modern strategic decision-making, especially in AI, business, and politics. 🏛 Early Foundations The origins of game theory can be traced back to the 17th century with pioneers like Blaise Pascal and Christiaan Huygens. Their work on probability and expectation laid the groundwork for understanding decision-making in uncertain situations. By 1713, Charles Waldegrave introduced early strategies for two-person games, offering a glimpse into the potential of game theory. 📊 Decision Theory: The Core of Rational Choice Closely related to game theory, decision theory focuses on the principles and models used to make rational choices under uncertainty. It explores how individuals or entities evaluate risks and rewards, laying the foundation for more complex strategic interactions modeled in game theory. Decision theory has profound implications in AI, where algorithms are designed to make optimal choices based on given data and predicted outcomes. 📈 Modern Beginnings The formal birth of game theory occurred in the 20th century with the publication of “Theory of Games and Economic Behavior” by John von Neumann and Oskar Morgenstern in 1944. This seminal work introduced axioms for rational decision-making, focusing on economic scenarios where interests conflict. 💡 Key Contributions The 1950s marked a major breakthrough with John Nash’s introduction of the Nash Equilibrium. Nash expanded game theory beyond zero-sum games, allowing for its application in broader fields, including politics, economics, and beyond. 🏛 Capitalism, Decision Theory, and AI: A Perfect Match? At its core, game theory mirrors the principles of capitalism—competition, optimization, and strategic thinking. Decision theory complements this by providing the framework for making rational choices in uncertain environments. In today’s AI-driven world, these principles are crucial for financial markets, where AI algorithms use game theory and decision theory to predict competitors’ moves, optimize trades, and simulate market conditions. 💹 Game Theory & Decision Theory in Financial Markets AI, empowered by game theory and decision theory, plays a pivotal role in: • 💹 High-Frequency Trading: Predicting competitors’ actions in real-time to optimize trades. • 📊 Market Simulation: Modeling complex interactions among traders, investors, and institutions. • 🔍 Predictive Analytics: Developing more accurate models by recognizing patterns and anticipating market trends. • 📈 Algorithmic Trading: Enhancing trading strategies by accounting for the actions of other market participants and making rational choices based on predicted outcomes. #ai #gametheory #ml #llm #capatalism #business #money #wallst #trade #diplomacy

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It's exciting to see more tools recognizing the importance of ontologies and knowledge graphs, and introducing out-of-the-box integrations. This development is truly unlocking the full potential of Generative AI!

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