Warren Hearnes, PhD’s Post

🔍 Embracing Analytical Behavior in the Workplace 🔍 Analytical behavior is a game-changer in the modern workplace, and here's why: 1️⃣ Critical Thinking: Analytical minds excel at breaking down issues, evaluating evidence, and making informed decisions. This skill is invaluable in any professional setting. 2️⃣ Problem-Solving Power: When faced with challenges, analytical individuals can cut through the noise and uncover the root cause, paving the way for effective problem-solving. 3️⃣ Data-Driven Insights: In today's data-rich environment, the ability to derive meaningful insights from information is a key differentiator. Analytical thinkers excel at turning data into actionable strategies. Whether you're in finance, marketing, tech, or any other field, honing your analytical skills can set you apart and drive success in your career. #AnalyticalThinking #problemsolving #DataDrivenInsights #CareerSuccess 🚀

What makes someone an effective decision maker, problem solver, or analyst? A common saying suggests that common sense is not so common, but where does this lack of common sense originate from? My recent study of books and articles reveal that biases significantly lead to what is perceived as a lack of common sense. Often, we cease searching for additional information too soon. Let's consider three key biases:   Availability Bias: We often assume that the information or data we have accurately represents the entire population. For instance, in a study about consumer habits, researchers might overvalue the opinions of a small, accessible group, mistaking it for the broader market sentiment. Familiarity Bias: We give undue importance to information, data, or concepts that are familiar to us. A business leader might favor traditional strategies they're comfortable with, even when market trends suggest a need for innovation. Confirmation Bias: This involves seeking or believing in information that supports our pre-existing beliefs or hypotheses. A classic example is a scientist favoring data that confirms their original theory, ignoring contradictory evidence.   These biases can arise subconsciously; thus, awareness is crucial. To counteract these biases in problem-solving or decision-making:   Create MECE Lists: Develop mutually exclusive and collectively exhaustive (MECE) list of data and information. Logical Evaluation: Evaluate each piece of information logically, weighing its pros and cons. Use Tools for Objectivity: Instead of mentally evaluating, use paper and pen, or digital tools, to visually map out information, aiding in a more objective analysis. Obtain information from various sources: It's vital to use a variety of up-to-date information sources in the digital age to avoid biases from outdated or limited data. For instance, a businessperson should combine traditional reports with the latest online trends and social media data to make well-rounded decisions. Learn from History: Reviewing past decision-making successes and failures helps avoid repeating old mistakes. For example, learning how businesses in history adapted (or failed to adapt) to market changes can guide modern decision-making strategies.     By incorporating these strategies and being mindful of biases, one can enhance their decision-making and problem-solving capabilities significantly. #decisionmaking #problemsolving #analysis #biases

If I just had enough data, I could make the perfect decision, right? Nope. Why? Because most decisions in business have no right answer. That's right....there is no right answer. Even an infinite amount of data will not give you the 'right answer' when that answer doesn't exist. We scientists are trained to find the right answer. That's what we do. And in the controlled environment of a laboratory, that makes perfect sense. But out in the real world where we mix customers and suppliers and market trends and the economy into the mix...things get a lot less black and white. Things get a lot more grey. When we go to work in a company, our manager needs us to work in the grey. They need us to navigate a world of uncertainty and make a recommendation for how we should move forward...even when we aren't certain. Which should be most of the time. So, learn to make decisions without all of the data you would like. Decision making has been getting a lot of attention lately, and for good reason. Ali Divan, Ph.D. had a great post on decision making just the other day. If you haven't seen it, go read it and watch the attached video. #decisionmaking #phd #scientist

🌟 Struggling with overwhelming data in solving a case study, I initially found myself in a puzzling situation with limited time. While the challenge seemed daunting, I remained optimistic, knowing that I was on the right path. Given more time, I believe I could have cracked the puzzle, by applying the right problem-solving strategies. 💡This experience taught me the importance of patience and perseverance in the face of complex data. It reinforced the need to approach problem-solving with a clear mindset, breaking down the information into manageable chunks. Being able to comprehend and analyze data effectively is crucial to deriving accurate insights and making informed decisions. I've come to realize that success often lies in taking a step back, reassessing the situation, and exploring alternative perspectives. Learning to prioritize relevant information helps in focusing on the key insights that lead to effective solutions. Challenges are inevitable, but they present opportunities for growth and learning. Embracing them with the right strategies and a positive mindset can unlock incredible results. I welcome any insights or suggestions from my LinkedIn community regarding strategies or techniques to handle complex case studies. Let's continue to learn, grow, and support one another in our professional journeys. #ProblemSolving #DataAnalysis #LearningOpportunities #GrowthMindset

While some professors dwell in the ivory towers of academia, spinning theories and contemplating the mysteries of the universe, the real world spins on, driven by industry and practical know-how. It's almost endearing, watching them lecture with authority, all the while having never stepped foot in the bustling, unpredictable land of industry. Ah, the charm of living in a theoretical bubble! Should you desire a successful career in data, away from the theoretical musings of academia, delve into my co-authored new book, “You Got The Data Job.. Now What?” (releasing early 2024) It’s a real-world guide, brimming with insights and practical knowledge, steering you towards industry success. After all, there’s more to mastering data than just dwelling in a realm of theories and hypotheses! ➡️ Follow me for more data and operations research content #Career #Data #DataScience #OperationsResearch #GameTheory #Leadership

How do you make decisions in your work or life? Do you rely on your intuition, data, or the majority view? Or do you use a combination of these factors? Intuition is the ability to understand something instinctively, without the need for conscious reasoning. Data is the collection of facts and figures that can be analyzed and interpreted objectively. The majority view is the opinion or preference of most people in a group or situation. All of these sources of information can be useful in different contexts, but they also have their limitations and biases. Here are some tips on when to trust your intuition, data, or the majority view, and when to be cautious: - Trust your intuition when you have a lot of experience and expertise in a domain, and when you need to make a quick decision in a complex or uncertain situation. Intuition can help you tap into your tacit knowledge and recognize patterns that are not obvious to others. However, be careful not to confuse intuition with emotion, prejudice, or wishful thinking. Intuition can also be influenced by cognitive biases, such as confirmation bias, overconfidence bias, or availability bias. - Trust data when you have a clear and measurable goal, and when you need to test a hypothesis or compare alternatives. Data can help you quantify and evaluate the impact of your actions and decisions. However, be careful not to assume that data is always accurate, complete, or relevant. Data can also be manipulated, misinterpreted, or misrepresented. - Trust the majority view when you have a diverse and representative sample of opinions, and when you need to build consensus or alignment. The majority view can help you leverage the wisdom of crowds and avoid extreme or outlier views. However, be careful not to follow the majority view blindly or uncritically. The majority view can also be influenced by social pressures, groupthink, or herd mentality. The key to making good decisions is to use your intuition, data, and the majority view wisely and appropriately. Sometimes, you may need to use one source more than the others, depending on the situation and the goal. Other times, you may need to use a combination of sources, or even challenge or contradict them. What do you think? How do you balance your intuition, data, and the majority view in your decision making? Share your thoughts and experiences in the comments below. #intuition #data #majorityview #decisionmaking #work

I had an interesting comment about my posts.    “Why do you focus on philosophy and not technical like everyone else?” (ok they didn’t say philosophy but I’m paraphrasing to save space)   Simple, because philosophy is the missing piece most ignore and that ignoring is why most fail at driving value.    Philosophy gets ignored, but, on IQ by major studies, philosophy majors typically land at the number 2 or 3 spot, right behind physics and neck and neck with mathematics. Philosophy is not a sport for light weights.    As a result, philosophy often has a lot of great concepts that have been well tested by some very strong thinkers. When you apply these concepts, correctly, they tend to give you the kind of boost you need in performance for your data, team, and culture. This results in a better customer experience which can lead to better overall revenue and profit performance.   It’s been something I have been using for years. Most in the data and analytics world ignores philosophy and it shows. Consistently for years, data and analytics projects have failed at the rate of 80%+ at delivering value to the business. Our current situation is that businesses are cutting back and these teams are impacted.   Many companies double down and go even more technical and think that will solve the problem. Data is about thinking and moving variables, not so much the technical piece. You are glorifying the least common denominator when you go technical. The real value and win are in the ability to understand the abstract.    By creating systems and structures that allow anyone to navigate the world of data and analytics, philosophy gives you an edge that the technical never will. These are the reasons I bring these topics up. But based on the engagement rate, most don’t seem to understand the value. Hence, why they don’t see the value they dream about. #data #analytics #leadership

Discover the secret to successful analysis projects with this fail-proof framework: 1. Uncover and Understand: Identify the problem that needs solving and formulate the critical questions that will guide your investigation. 2. Strategize with Savvy: Determine the most effective analytics methods to unlock your problem-solving potential. 3. Dive into Data: Embark on a treasure hunt for information from a diverse range of sources. 4. The Cleanup Crew: Dedicate time to meticulously clean, organize, and optimize your data for optimal usability. 5. The Big Reveal: Articulate your findings in an impactful way that incites action. 6. Always Evolving: Embrace the cyclical nature of this process, repeating and refining for continual success. #data #analytics #speaker #consulting #careers #leadership #management ---------------------- 💥 I am Achille 🔺 Supporting you and your businesses leveraging data 🔺 Providing advisory, consultancy and keynotes specializing in Retail, Supply Chain & Construction Like this post? Want to see more? Ring the 🔔 on my profile Follow #achilleai 🔝 Connect with me

The Rise of Connector Roles in Data Science I feel like this is a topic that has been discussed under different names for a number of years now (remember the citizen data scientists??) but it is obviously one that hasn't been solved and continues to be top of mind. The article linked in the comments from the MIT Sloan Management Review provides a few different case studies on how this has been approached while also including thought provoking questions that might help avoid the failures of others (apologies for the pay wall content!). I'm not just recommending it because it says this: " We believe that in the long run, many organizations will want every senior executive to have data science experience in their background. " 😂 One concern I have about the connector roles described is the possible tactical nature of them. They seem to be positioned on a project or product level basis in most cases and that concerns me in it's ability to miss the bigger picture which is one of the biggest problems to solve. The concerns raised around career path can also be combined with concerns about disposability of any augmented support role as I'm sure many have experienced in the last 12 months. I wonder if considering the role of connector as the responsibility of a leader already embedded in the business might be a more sustainable and impactful route, someone with the oversight to see how the role of data fits into the company's most pressing priorities rather than a bottom up gap identification. #strategy #data #datascience #mitsloan #mba #businessstrategy #career #leadership