[chatbot]

The Elements of Good Judgment

Leaders with good judgment listen for what’s unsaid. They surround themselves with people who will tell them what they need to know — not what they want to hear. And they think carefully about the risks of implementation — even for small projects.

Share This Post

Facebook
LinkedIn
Twitter

 

Judgment—the ability to combine personal qualities with relevant knowledge and experience to form opinions and make decisions—is “the core of exemplary leadership,” according to Noel Tichy and Warren Bennis (the authors of Judgment: How Winning Leaders Make Great Calls). It is what enables a sound choice in the absence of clear-cut, relevant data or an obvious path. Likierman believes that a more precise understanding of what exactly gives someone good judgment may make it possible for people to learn and improve on it. He approached CEOs at a range of companies, from some of the world’s largest right down to start-ups, along with leaders in the professions: senior partners at law and accountancy firms, generals, doctors, scientists, priests, and diplomats. He asked them to share their observations of their own and other people’s exercise of judgment so that he could identify the skills and behaviors that collectively create the conditions for fresh insights and enable decision makers to discern patterns that others miss. As a result, he has identified six key elements that collectively constitute good judgment: learning, trust, experience, detachment, options, and delivery. He describes these elements and offers suggestions for improvement in each one.

Read more at HBR.org

Subscribe To Our Newsletter

Get updates and learn from the best

More To Explore

Courses

Machine Learning with Python: k-Means Clustering

Clustering—an unsupervised machine learning approach used to group data based on similarity—is used for work in network analysis, market segmentation, search results grouping, medical imaging, and anomaly detection. K-means clustering is one of the most popular and easy to use clustering algorithms. In this course, Fred Nwanganga gives you an introductory look at k-means clustering—how it works, what it’s good for, when you should use it, how to choose the right number of clusters, its strengths and weaknesses, and more. Fred provides hands-on guidance on how to collect, explore, and transform data in preparation for segmenting data using k-means clustering, and gives a step-by-step guide on how to build such a model in Python.

Subscribe to our newsletter

Call Us

+31 6 25396389

©2023 Candidate-1st.com