# decision-making Published articles for decision-making. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Modernizing the Trade Lifecycle With Governed Data and AI DevFeed: [Modernizing the Trade Lifecycle With Governed Data and AI]() Original publisher: [Read original article]() Author: Kim Hatton; Andrea DeSosa Published: 2026-09-17T14:00:00Z Content type: article Language: en Sources: [Databricks]() Topics: [data](), [Artificial Intelligence](), [databricks](), [decision-making](), [execution](), [Risk](), [systems]() Tags: [ai](), [data](), [databricks](), [decision-making](), [execution](), [financial-services](), [fragmentation](), [industries](), [modernization](), [real-time](), [risk](), [systems]() ### AI overview Capital-markets firms are modernizing trade lifecycle workflows as data volumes grow, AI initiatives move toward production, settlement cycles shorten, and regulatory scrutiny increases. The article argues that governed, discoverable, reliable data across research, trading, risk, operations, and compliance is more durable than isolated models. ### Source excerpt Capital-markets firms are modernizing the trade lifecycle under pressure from every direction: growing data volumes... ## How Engineering Principles Can Help You Scale DevFeed: [How Engineering Principles Can Help You Scale]() Original publisher: [Read original article]() Author: Khan Academy Published: 2019-08-21T22:00:00Z Content type: opinion Language: en Sources: [Khan Academy]() Topics: [Development](), [Code](), [Processes]() Tags: [architecture](), [communication](), [decision-making](), [engineering](), [growth](), [news](), [process](), [professional-development](), [scale](), [velocity]() ### AI overview Marta Kosarchyn describes how Khan Academy's growing engineering team outgrew its initial principles. The article argues that evolving engineering principles can support scaling while preserving culture and engagement, and explains how the original approach eventually contributed to slower delivery, code clutter, and implicit architectural decisions. ### Source excerpt By Marta Kosarchyn Our engineering team has grown a lot over the past couple of years, and we're ... Read more ## What do software architects at Khan Academy do? DevFeed: [What do software architects at Khan Academy do?]() Original publisher: [Read original article]() Author: Khan Academy Published: 2018-05-14T22:00:00Z Content type: opinion Language: en Sources: [Khan Academy]() Topics: [Software](), [Development](), [coding]() Tags: [architecture](), [article](), [collaboration](), [decision-making](), [engineering](), [infrastructure](), [news](), [platforms](), [process](), [software](), [standards](), [tools]() ### AI overview Kevin Dangoor explains how software architects at Khan Academy view their role: as product managers for the system in which software is built. The article emphasizes improving coding standards, tools, platforms, and processes by collaborating with engineers and engineering management, with DACI used to structure architecture-change decisions. ### Source excerpt By Kevin Dangoor "Architect" is a new role in Khan Academy's engineering team this year, and my colleague, ... Read more ## Beyond the data: what a Business Analyst does at Nubank DevFeed: [Beyond the data: what a Business Analyst does at Nubank]() Original publisher: [Read original article]() Author: Nubank Editorial Published: 2026-09-15T12:14:41Z Content type: opinion Language: en Sources: [Nubank]() Topics: [Data analysis](), [decision-making](), [dashboards](), [Tech Lead](), [User Experience](), [Finance]() Tags: [analysts](), [business-analyst](), [dashboards](), [data](), [data-analysis](), [data-analytics](), [data-science-machine-learning](), [decision-making](), [experimentation](), [meetings](), [tech-lead](), [user-experience]() ### AI overview This article explains how Business Analysts at Nubank connect data analysis, business context, and experimentation to product decisions. It describes their work in multidisciplinary squads, including investigating metrics, evaluating trade-offs, and making decisions under uncertainty. ### Source excerpt Understand how Business Analysts at Nubank use data, context, and experimentation to guide product decisions. The post Beyond the data: what a Business Analyst does at Nubank appeared first on Building Nubank. ## Beyond the data: what a Business Analyst does at Nubank DevFeed: [Beyond the data: what a Business Analyst does at Nubank]() Original publisher: [Read original article]() Author: Nubank Editorial Published: 2026-09-15T12:14:41Z Content type: opinion Language: en Sources: [Nubank]() Topics: [Data analysis](), [decision-making](), [context](), [Tech Lead]() Tags: [adoption](), [business-analyst](), [data](), [data-analysis](), [data-analytics](), [data-science-machine-learning](), [decision-making](), [manager](), [tech-lead](), [user-experience]() ### AI overview A first-person account of the Business Analyst role at Nubank, describing how BAs connect data analysis, context, and experimentation to product and business decisions. The article emphasizes clarifying trade-offs, investigating metrics, and collaborating with product and technical roles. ### Source excerpt Understand how Business Analysts at Nubank use data, context, and experimentation to guide product decisions. The post Beyond the data: what a Business Analyst does at Nubank appeared first on Building Nubank. ## Why Strong Candidates Are Rejected in Big Tech Interviews DevFeed: [Why Strong Candidates Are Rejected in Big Tech Interviews]() Original publisher: [Read original article]() Author: Prasad Rao Published: 2026-09-03T16:22:55Z Content type: opinion Language: en Sources: [Big Tech Careers]() Topics: [Tech Careers](), [decision-making]() Tags: [careers](), [decision-making](), [interview](), [storytelling]() ### AI overview The article argues that strong technical candidates are often rejected from big tech interviews because they underprepare for behavioral interviews and struggle to communicate their past work, impact, and decision-making clearly. ### Source excerpt The #1 reason people get rejected in big tech interviews, and the 4 week fix. ## The Art of Simplifying Decisions. DevFeed: [The Art of Simplifying Decisions.]() Original publisher: [Read original article]() Author: David Pereira Published: 2026-08-20T12:27:37Z Content type: opinion Language: en Sources: [Untrapping Product Teams]() Topics: [decision-making](), [data analytics](), [Artificial Intelligence]() Tags: [ai-agents](), [analysis](), [data-analytics](), [decision-making]() ### AI overview The article argues that decision-making has become a bottleneck as coding becomes more scalable. It compares AI-supported options, additional meetings, and decision-making within limited scope, while emphasizing the need for context, evidence, and empowered product decisions. ### Source excerpt Decision-making is the bottleneck today. ## Unlocking your data: the value is in collaboration DevFeed: [Unlocking your data: the value is in collaboration]() Original publisher: [Read original article]() Author: Sam Perridge Published: 2026-08-12T14:59:00Z Content type: opinion Language: en Sources: [Scott Logic]() Topics: [data](), [datasets](), [Artificial Intelligence](), [decision-making](), [systems]() Tags: [ai](), [business-intelligence](), [claude](), [collaboration](), [data](), [data-democratisation](), [data-engineering](), [data-maturity](), [data-platform](), [datasets](), [decision-making](), [governance](), [guardrails](), [reporting](), [self-service]() ### AI overview This opinion article argues that organisations unlock more value from data when datasets are connected and insights are accessible across teams. It describes a progression from paper records and siloed systems to connected and democratised data, including self-service analytics and AI, while emphasising governance and practical adoption. ### Source excerpt Organisations often focus on collecting data and connecting systems, but the greatest value comes from helping datasets work together and making insights accessible to the people who need them. In this post, I explore the journey from siloed data to democratised access, showing how self-service analytics and AI can unlock hidden value, while strong governance provides the guardrails for confident decision-making. ## The benefits of medical AI assistance vary based on user expertise DevFeed: [The benefits of medical AI assistance vary based on user expertise]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-08-04T09:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [Human-AI evaluation](), [AI Development](), [AI Chat]() Tags: [ai](), [artificial-intelligence](), [bias](), [computer-science-and-technology](), [decision-making](), [dermatological-diagnosis](), [diagnosing-skin-disease](), [diagnostics](), [electrical-engineering-and-computer-science-eecs](), [explainability](), [explainable-ai](), [health-care](), [human-computer-interaction](), [institute-for-medical-engineering-and-science-imes](), [jameel-clinic](), [laboratory-for-information-and-decision-systems-lids](), [large-language-model](), [machine-learning](), [marzyeh-ghassemi](), [medicine](), [research](), [technology-and-society](), [users]() ### AI overview A study found that AI assistance improved skin-disease diagnosis for non-experts and clinicians, but explainability affected users differently. Non-experts often deferred to LLM-based explanations even when the AI was wrong, while clinicians performed best with the model's prediction alone. ### Source excerpt Study finds non-experts deferred to LLM-based diagnostic assistance, even when it was wrong, while clinicians caught AI errors. ## A framework for sequential decisions in daily life and beyond DevFeed: [A framework for sequential decisions in daily life and beyond]() Original publisher: [Read original article]() Author: Nisarg Suthar Published: 2026-08-04T07:16:01Z Content type: article Language: en Sources: [Data Science at Microsoft]() Topics: [decision-making]() Tags: [data-modeling](), [data-science](), [decision-making](), [model](), [operations-research](), [sequential-decision](), [structured](), [supply-chain]() ### AI overview The article introduces a Universal Modeling Framework proposed by Warren Powell for reasoning about sequential decisions under uncertainty and incomplete information. It explains how mathematical modeling can clarify decision problems and support better choices across areas such as finance, healthcare, data-center infrastructure, and supply chains. ### Source excerpt Photo by Sophia Kunkel on Unsplash Decision-making occupies a significant portion of our mental uptime. Whether we work in finance, energy, transportation, healthcare, e-commerce, foreign policy, or global supply chains, we are constantly required to make choices in the presence of uncertainty and incomplete information. As new information arrives, decisions must be revised, refined, and sometimes completely reconsidered. Every decision carries consequences -- some rewarding, others costly. The ability to consistently make better choices is often a defining factor behind successful outcomes. Yet effective decision-making remains notoriously difficult. Describing a problem as "mind-bogglingly complex" is really just a by-product of a failure to think about the problem in a structured way.-- Dr. Warren Powell This article introduces a Universal Modeling Framework for reasoning about sequential decision problems proposed by Dr. Warren Powell, an operations researcher at Princeton University. The same framework can be applied across a remarkably diverse set of problems: buying or selling financial assets, evaluating a new user experience, selecting candidate drugs for clinical trials, investing in data-center infrastructure, or managing large-scale supply chains. Here we shall undertake an approach that focusses on identifying the core elements of a decision-making process. Central to our approach is the creation of a simple mathematical model that eliminates the ambiguity of describing problems in language. Modeling is an art, guided by a mathematical framework, and results in a well-defined problem that we can put on a computer to solve. Even when the ultimate goal is not to automate the decision, the act of modeling itself often leads to deeper understanding and better choices. A dynamic model for sequential ecisions A sequential decision process can be represented as follows: Where: Sₜ is the state variable capturing our state of knowledge at time t. For example, inve ## Devavrat Shah's research and Ikigai Labs use tabular data for real-time forecasting and decision-making DevFeed: [Devavrat Shah's research and Ikigai Labs use tabular data for real-time forecasting and decision-making]() Original publisher: [Read original article]() Author: David Chandler | Laboratory for Information and Decision Systems Published: 2026-07-14T20:25:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [decision-making](), [Structured-data](), [AI Models](), [data](), [Time Series](), [Computer science](), [Electrical engineering and computer science (EECS)](), [Temporal data](), [systems]() Tags: [ai](), [ai-and-decision-making](), [ai-in-business-planning](), [ai-models](), [algorithms](), [artificial-intelligence](), [business-and-management](), [business-modeling](), [celonis-context-model](), [computer-science-and-technology](), [data](), [data-systems](), [decision-making](), [devavrat-shah](), [electrical-engineering-and-computer-science-eecs](), [faculty](), [forecasting](), [idss](), [ikigailabs](), [laboratory-for-information-and-decision-systems-lids](), [mit-eecs](), [mit-idss](), [mit-intellectual-property](), [mit-lids](), [mit-schwarzman-college-of-computing](), [predictions](), [profile](), [real-time](), [research](), [school-of-engineering](), [startups](), [structured](), [tabular-data](), [time-series-data]() ### AI overview MIT Professor Devavrat Shah's research led to a foundation model for tabular and time-series enterprise data. Developed with Ikigai Labs, the system continuously tests predictions against real outcomes to support large-scale forecasting and decision-making. ### Source excerpt Through research and entrepreneurship, Professor Devavrat Shah is helping to design methods that can handle constant decision-making using limited computational resources. ## AI Can Amplify Misalignment in Product Development DevFeed: [AI Can Amplify Misalignment in Product Development]() Original publisher: [Read original article]() Author: David Pereira Published: 2026-06-24T12:27:25Z Content type: opinion Language: en Sources: [Untrapping Product Teams]() Topics: [Development](), [decision-making](), [Artificial Intelligence]() Tags: [ai](), [critical-thinking](), [decision-making](), [development]() ### AI overview The article argues that AI-generated strategies, roadmaps, and user stories can produce polished artefacts quickly without creating the alignment and shared understanding required for product development. It warns that relying on generated outputs can weaken decision-making and critical thinking, while presenting AI as an amplifier of existing strengths and weaknesses. ### Source excerpt "AI amplifies misalignment." Andrey Khusid, Miro CEO. ## One year with Codeberg DevFeed: [One year with Codeberg]() Original publisher: [Read original article]() Author: Ludovic Courtès Published: 2026-06-22T14:00:00Z Content type: article Language: en Sources: [GNU Guix -- Blog]() Topics: [pull-requests](), [hosting](), [issue tracking](), [decision-making](), [Git](), [forgejo]() Tags: [article](), [change](), [collaboration](), [community](), [decision-making](), [forgejo](), [issue-tracking](), [pull-requests](), [source]() ### AI overview Guix describes its migration to Codeberg for source code hosting, issue tracking, and pull requests after more than a decade using Savannah, email-based bug reports, patches, and Debbugs. The article discusses the reasons for the change, the project's collective decision-making process, and early takeaways after one year. ### Source excerpt A year ago, Guix migrated to Codeberg for source code hosting, issue tracking, and pull requests. This is a significant change for a project with more than 400 people contributing code each year, after more than decade hosting code at Savannah and dealing with bug reports and patches by email, tracked by a Debbugs instance . This article discusses the process that led to this change and lists some takeaways, a year later. The non-obvious choice For years before, the question of our choice of source code hosting and collaboration tools would regularly come up. However, with... ## Uncovering the Shape of Fraud with Cosmos Explorer: Visual Metaphors Behind Millions of Transactions DevFeed: [Uncovering the Shape of Fraud with Cosmos Explorer: Visual Metaphors Behind Millions of Transactions]() Original publisher: [Read original article]() Author: João Bernardo Narciso Published: 2026-04-07T17:24:51Z Content type: article Language: en Sources: [Feedzai]() Topics: [Data visualization](), [data]() Tags: [data](), [data-visualization](), [dataviz](), [decision-making](), [design](), [feedzai](), [fraud](), [fraud-detection](), [patterns](), [research]() ### AI overview Feedzai's Data Visualization Research team is developing Cosmos Explorer, an interface that uses universe-inspired visual metaphors to help analysts examine patterns, trends, outliers, and possible fraud across hundreds of millions or billions of transactions. The project explores how to preserve meaningful details at very large scale while supporting data analysts and data scientists. ### Source excerpt Uncovering the Shape of Fraud with Cosmos Explorer: Visual Metaphors Behind Millions of Transactions The Data Visualization Research team is developing Cosmos Explorer, an interface that leverages universe-related visual metaphors to convey information about the billions of transactions processed by Feedzai. Pedro Cruz, professor at Northeastern University, partnered with Feedzai to bring this idea to life by contributing with his creativity and expertise to solve this challenging visualization problem. https://medium.com/media/1b2ecfadd91d204640462f89fa6ff67f/href When we look out into the universe, we don't just see emptiness. We see an unimaginable scale: billions of galaxies, each containing billions of stars, each a point of light carrying its own story. No single observer can take it all in at once. Yet with the right instruments, patterns emerge: the structure of the cosmos itself becomes visible. In the digital realm, there is another universe just as vast and intricate. Every day, hundreds of millions of events flow through Feedzai's system which assesses them to protect consumers all over the world. Each one is a unique data point (e.g., a purchase, a login, a transfer). Individually, they don't tell us much. Together they form a living universe of behavior that represents the diversity in people's lives. But fraud lurks in everyday transactions, with criminals trying to hide their activities within the sheer volume of transactions. The question is: how can we represent those patterns meaningfully, the normal behaviors and the fraudulent behaviors, the trends and the outliers, to empower data analysts and data scientists in their decision-making processes? The biggest challenge is scale. No one can look at billions of events one by one. Aggregation helps, but it smooths over the details, which often encode the most interesting signals like the faint outlines of fraud or unusual clusters of activity. But what if we could see it all at once? Not just a summa ## postmarketOS in 2026-03: conference announcement DevFeed: [postmarketOS in 2026-03: conference announcement]() Original publisher: [Read original article]() Published: 2026-04-05T00:00:00Z Content type: article Language: en Sources: [postmarketOS Blog]() Topics: [Linux](), [Python 3.14](), [decision-making](), [meetings](), [Hackathon]() Tags: [2026](), [alpine](), [announcement](), [conference](), [decision-making](), [gnome](), [hackathon](), [meetings](), [postmarketos](), [teams](), [technical]() ### AI overview A postmarketOS monthly update covering Python 3.14 entering Alpine edge, GNOME 50, community event participation, an upcoming postmarketOS conference in Aachen, governance changes, and package-maintainer workflow updates. ### Source excerpt A lot has happened since the last monthly blog post. Python 3.14 is now in Alpine edge (which as usually involved rebuilding and fixing up a lot of packages, thanks to everybody who took part in this!). The amazing GNOME developers brought us GNOME 50 "Tokyo" and speaking of that city, OSC Tokyo 2026 Spring took place and we were represented by Rob at the event. He had a stand, handed out Japanese and English leaflets about postmarketOS as well as stickers and showed postmarketOS running on several devices. From his event report: "As always there were many interested members of the community coming to the table, many [had not] heard about postmarketOS, but also several (10-20%) mentioned, they had an older device, which they want to test on but have not done so." Conference Announcement While we have talked about lots of events that we attended before, we are very excited to announce that we are hosting our own little conference later this year! When: 25th to 27th of September Where: RWTH Aachen University, Aachen, Germany (two rooms, not all buildings 😉) The goal is to get people from postmarketOS and related communities like Alpine Linux together and to have talks, workshops, technical discussions, figure out how to advance the project and to simply see each other IRL. A lot still needs to be figured out, but we are announcing the date early so the people who want to attend can already mark it in their calendar. We will follow up with a call for participation, registration and a conference schedule and are extremely excited to bring this together. Thanks to RWTH Aachen for the opportunity and to Aelin, Achill and Pablo for organizing it so far! Organizational Following up on the power delegation and teams topic from the hackathon, the decision making power has been moved from the Core Team to the whole team (Core Contributors + Trusted Contributors), and decisions are now being made asynchronously between team meetings (so more people have the chance to vote, not ## Scaling Experimentation Quality at Booking.com DevFeed: [Scaling Experimentation Quality at Booking.com]() Original publisher: [Read original article]() Author: Edgar Cano Published: 2026-03-24T11:44:21Z Content type: article Language: en Sources: [Booking.com Data Science]() Topics: [experiments](), [Development](), [decision-making](), [human review]() Tags: [best-practices](), [data-science](), [decision-making](), [experimentation](), [featured](), [human-review](), [metrics](), [product-development](), [quality]() ### AI overview Booking.com describes how it addressed declining experimentation quality as experiment volume grew. The article discusses arbitrary test durations, significance-seeking, and the trade-offs between enforcing standards and educating product teams. ### Source excerpt Authors: Edgar Cano, Daisy Duursma, Nils Skotara, Melanie Mueller Figure 1. Three-pillar components of Booking.com's Experimentation Quality Experimentation is at the core of product development in Booking.com, powered by our in-house platform, "ET" (Experiment Tool). At any given moment, we run approximately 1,000 parallel experiments to evaluate product changes. These experiments or A/B tests allow teams to directly compare a new version of the website against the existing one, validating hypotheses about how specific changes impact important metrics. In our organization, these pitfalls became more evident as our experiment volume grew. We observed that experimenters might set an arbitrary "two-week" duration without thinking about sufficient power, or extend a test until results "became significant" or "trended positive." Knowing that this lack of consistency leads to flawed decision-making we dedicated significant effort to increasing the quality of our experimentation process, making Experimentation Quality a key KPI for our program. However, identifying the problem was only the start; the greater challenge is how to implement these standards across a large organization. Enforcement vs. Education When deciding how to scale quality, we faced a fundamental choice: Do we enforce strict controls or we rely on education. Ultimately, we left it to product teams to decide how to conduct their experiments. This choice entailed several trade-offs: Enforcement: Ensures comparability, consistency, and reliability. However, it comes at the cost of flexibility. There is a risk that people follow "rules" blindly without understanding the rationale. Education: Aims for a culture where experimenters understand the why behind best practices. This leads to better buy-in, allows teams to challenge methods, and highlights individual responsibility. It also prevents bottlenecking. If enforcement requires human review, it slows down development. However, education requires a massive ## Reframing Healthcare Technology in the AI Era DevFeed: [Reframing Healthcare Technology in the AI Era]() Original publisher: [Read original article]() Author: Jon Wettersten Published: 2026-03-06T22:14:00Z Content type: opinion Language: en Sources: [8th Light](), [8th Light Insights]() Topics: [Artificial Intelligence](), [Generative AI](), [Automation](), [decision-making](), [trust](), [digital]() Tags: [ai](), [automation](), [decision-making](), [generative-ai](), [governance](), [healthcare](), [healthtech](), [human-centered-design](), [safety](), [technology](), [transparency](), [trust]() ### AI overview This article argues that healthcare organizations should approach AI adoption through human-centered design, stronger governance, transparency, and alignment with clinical standards. It emphasizes that clinicians need technology to reduce administrative burden and support patient care without compromising safety, trust, or practical and ethical readiness. ### Source excerpt At 8th Light, we help healthcare organizations design and build AI-enabled systems that put people first, combining product strategy, human-centered design, and technology leadership to create innovative solutions that work for clinicians as well as patients. Why This Matters Although AI continues to dominate healthtech headlines, many frontline clinicians are still battling clunky systems, fragmented workflows, and administrative overload. The reality? Healthcare practitioners are not asking for more technology, they're asking for more time with patients. The tension between innovation and adoption continues to shape healthcare's digital transformation. As AI tools quickly evolve and adapt to revolutionize diagnosis, documentation, and decision-making, many healthcare providers remain cautious. Their questions are not about the potential of technology, but about its practical and ethical readiness for real-world care. For instance: How do we vet these tools? Can we trust the outcomes? Will this actually improve care or just add more work? Will this solution deliver measurable ROI without compromising patient safety? These are not questions of resistance, they're questions of responsibility. As healthcare systems race to deploy generative AI, predictive analytics, and automation, clinicians and administrators are demanding stronger governance, transparency, and alignment with clinical standards. Their goal isn't to slow innovation, but to ensure technology protects both patients and the licensed physicians who care for them. Behind these questions lies a workforce already under significant strain, and technology that fails to ease that burden risks making it worse. The Urgent Need for Human-Centered Healthtech The U.S. is already experiencing workforce strain across many health professions, driven by geographic maldistribution, burnout, and demographic pressures. Federal workforce projections show ongoing gaps across multiple professions, with non-metro communities ## Normalized Entropy or Apply Rate? Evaluation Metrics for Online Modeling Experiments DevFeed: [Normalized Entropy or Apply Rate? Evaluation Metrics for Online Modeling Experiments]() Original publisher: [Read original article]() Author: Megan Chen Published: 2025-11-11T06:16:53Z Content type: opinion Language: en Sources: [Indeed]() Topics: [recommendation systems](), [Model Development](), [decision-making](), [benchmarking]() Tags: [a-b-testing](), [data-science](), [decision-making](), [evaluation](), [experiments](), [machine-learning](), [metric](), [models](), [ranking](), [unsorted]() ### AI overview Indeed examines whether model performance metrics or product metrics should guide online modeling experiments. It discusses how optimizing individual ranking models may not align with broader business goals and considers evaluation metrics for model rollouts. ### Source excerpt Introduction At Indeed, our mission is to help people get jobs. We connect job seekers with their next career opportunities and assist employers in finding the ideal candidates. This makes matching a fundamental problem in the products we develop. The Ranking Models team is responsible for building Machine Learning models that drive matching between job [...] ## Management and Productivity Laws for Decision-Making and Organizational Behavior DevFeed: [Management and Productivity Laws for Decision-Making and Organizational Behavior]() Original publisher: [Read original article]() Author: Tom Published: 2025-08-26T00:00:00Z Content type: opinion Language: en Sources: [Tomasz Gągor]() Topics: [decision-making](), [Agile](), [meetings]() Tags: [business-efficiency](), [communication](), [conway-s-law](), [corporate-best-practices](), [decision-making](), [effectiveness](), [leadership](), [leadership-tips](), [management](), [management-laws](), [murphy-s-law](), [organizational-behavior](), [processes](), [productivity-principles](), [team-management](), [workplace-decision-making]() ### AI overview This opinion article introduces several management and productivity "laws," including Kidlin's Law, Gilbert's Law, Wilson's Law, and Falkland's Law. It explains how clear problem statements, explicit guidance, timely action, and avoiding unnecessary decisions can support workplace decision-making and organizational effectiveness. ### Source excerpt Discover the most influential management and productivity "laws"-from Murphy's Law to Conway's Law-that shape decision-making, leadership, and organizational behavior. Learn practical applications and scientific backgrounds to boost your effectiveness at work. ## How Managers Can Develop Team Leadership Through Meeting Delegation DevFeed: [How Managers Can Develop Team Leadership Through Meeting Delegation]() Original publisher: [Read original article]() Author: Ryan Murphy Published: 2025-07-14T12:00:13Z Content type: opinion Language: en Sources: [The Software Engineering Times]() Topics: [meetings](), [decision-making](), [trust](), [real-time]() Tags: [coaching](), [decision-making](), [leadership](), [manager](), [meetings](), [real-time](), [retro](), [support](), [sync](), [trust]() ### AI overview This opinion article argues that managers should avoid leading every meeting and instead give team members opportunities to run standups, retrospectives, and stakeholder syncs. It recommends providing preparation support, staying available, and debriefing afterward to develop independent leaders and reduce managerial burnout. ### Source excerpt Let's just say it straight. If you're the one leading every single meeting, you're either inexperienced, insecure, or haven't been shown how to be a real manager yet. If that stings a little, that's okay. Maybe it should. There are a few exceptions. Sure, incidents. Maybe a gnarly stakeholder review or a performance conversation. But even in those high-stakes moments, there's usually room to coach. Let someone else run the show. As long as someone is clear on decision-making, you don't need to be the one running the room every time. That's not what good leadership looks like. Good leadership is about building people who can do the job without you. Why You Keep Grabbing the Mic Most managers hog the meetings for one of a few reasons: It's faster if I do it They're not ready yet What if they mess it up? I'll be blamed if it goes wrong I'm the one accountable If that sounds like you, you're not alone. I've heard them all before. I've said a few of them too, early in my career. But you need to realise what those excuses are actually saying to your team: You're not ready. I don't trust you. I don't care enough to help you grow. Not great. What It Looks Like to Coach Instead This isn't about disappearing. It's not about sitting out. It's not delegating and walking away. It's about showing up differently. Coaching in real time. Supporting from the side, not from the front. Let them lead the standup. Let them chair the retro. Let them run the stakeholder sync. Then debrief. What worked? What would you do differently next time? What was hard? That's coaching. That's how they get better. That's how you build a team that can do hard things without you steering every move. The Real Reason You're Still Leading Everything Sometimes, it's not about fear. It's about ego. If everything runs through you If you always need to be in the room If you secretly like being the person with the answers That's ego. It's not sustainable. It's not leadership. It's how you end up with a team that ## Making Better Decisions - How we can decide better together. DevFeed: [Making Better Decisions - How we can decide better together.]() Original publisher: [Read original article]() Author: tobi@techunicorn.builders (Tobias Mende) Published: 2025-06-19T00:00:00Z Content type: article Language: en Sources: [Tobias Mende]() Topics: [decision-making]() Tags: [berlin](), [collaboration](), [conference](), [decision-making](), [processes](), [quality](), [teams]() ### AI overview This talk presents collaborative decision-making methods, real-world examples, and practical strategies for improving decision quality, inclusiveness, clarity, and team commitment. It also discusses simplifying decision-making processes and using tools to improve efficiency. ### Source excerpt With the number of daily decisions increasing significantly over the past ten years, effective decision-making is crucial. Decisions need to be made as fast and as good as possible. This talk focuses on the value of collaborative decision-making, highlighting benefits such as improved problem-solving through diverse perspectives and enhanced commitment from team members. We will explore various group decision-making methods supported by real-world examples. ## Integrating Xandr's Real-Time Data Provider On-Premises: Infrastructure Requirements and Engineering Challenges DevFeed: [Integrating Xandr's Real-Time Data Provider On-Premises: Infrastructure Requirements and Engineering Challenges]() Original publisher: [Read original article]() Author: Guy Watson Published: 2025-04-22T22:57:37Z Content type: article Language: en Sources: [GumGum]() Topics: [real-time](), [Requirements](), [on-prem](), [Latency](), [Low Latency](), [Hardware](), [data](), [digital]() Tags: [ads](), [advertising](), [challenges](), [clients](), [data-processing](), [decision-making](), [hardware](), [infrastructure](), [latency](), [low-latency](), [metric](), [on-prem](), [performance](), [platform](), [real-time]() ### AI overview The article describes GumGum's experience integrating Xandr's Real-Time Data Provider on-premises, focusing on infrastructure requirements, scaling challenges, latency, traffic volume, and the decision to own hardware. ### Source excerpt How We Slashed Our Infrastructure Costs by 80% While Successfully Integrating Xandr's Real-Time Ad Platform!Introduction In the fast-paced world of digital advertising, maximizing return on investment (ROI) for ad spend is paramount. Buyers need to make data-driven decisions in real time to ensure their ads reach the right audience at the right moment. For us, integrating Xandr's Real-Time Data Provider (RTDP) was essential to providing the real-time optimization signals that would help our clients maximize their ROI by boosting Attention Time -- the key metric driving the effectiveness of programmatic ad campaigns. Our story is one of navigating through technical hurdles, experimenting with new infrastructure, and finding creative solutions to challenging engineering problems. As we embarked on integrating Xandr Real Time Data Provider, the path was far from straightforward. From vendor selection to scaling issues, and even the decision to own our hardware, every step had its complications. Yet, each obstacle presented an opportunity for innovation and refinement. In this article, we'll take you through the key challenges we faced, how we overcame them, and the lessons we learned from integrating Xandr on-prem. Whether you're considering a similar project or are simply curious about the behind-the-scenes of large-scale advertising tech deployments, our experience might just offer the insights you need. Xandr Integration and Its Requirements Xandr, a leading programmatic advertising platform, provides powerful tools to streamline and optimize ad delivery, targeting, and performance tracking. For us, integrating with Xandr RTDP meant integrating with the real time optimisation Xandr platform. Xandr's system, built to handle vast amounts of data in real time, comes with certain infrastructure demands. These include: Volume: The platform must be capable of handling massive amounts of traffic at any given time. For example, billions of ad impressions are processed daily, ## Deciding Better in Organizations DevFeed: [Deciding Better in Organizations]() Original publisher: [Read original article]() Author: tobi@techunicorn.builders (Tobias Mende) Published: 2025-03-29T05:00:00Z Content type: opinion Language: en Sources: [Tobias Mende]() Topics: [decision-making](), [context](), [meetings]() Tags: [collaboration-decision-making-leadership-culture](), [context](), [decision-making](), [distributed](), [improvements](), [meetings]() ### AI overview The article examines decision-making in software organizations, arguing that decision quality should be evaluated by the decision-making context and process rather than by outcomes alone. It discusses organizational problems such as centralized decisions, prolonged peer discussions, blocked work, and limited commitment, and points to collaborative and distributed decision-making methods as ways to improve. ### Source excerpt Deciding Better: How to Make Faster, Smarter, and More Effective Decisions in Your Organization The quality of your decisions influences the quality of your life. The quality of the decisions in your organization determines your company's future. ## The Leadership Dilemma - When Times Change and Generations Collide DevFeed: [The Leadership Dilemma - When Times Change and Generations Collide]() Original publisher: [Read original article]() Author: tobi@techunicorn.builders (Tobias Mende) Published: 2025-03-01T05:00:00Z Content type: opinion Language: en Sources: [Tobias Mende]() Topics: [decision-making](), [FIRST]() Tags: [decision-making](), [founders](), [leadership](), [leadership-culture-decision-making-generational-differences-coaching](), [organization](), [startups]() ### AI overview The article argues that founders may apply leadership assumptions shaped by older workplace models even as organizations and younger employees increasingly value autonomy, impact, purpose, and inspiration. It focuses on decision-making problems, including evaluating decisions by outcomes, fear of poor results, and top-down interventions that can increase organizational dependency. ### Source excerpt The Leadership Dilemma - When Times Change and Generations Collide Your leadership style is heavily influenced by the leadership you have experienced yourself. The way how you run and build a company is the result of the companies you have worked in, your experiences, and your considerations. [Next page]()