Latest news with #MITSloanManagementReview
Yahoo
13-05-2025
- Business
- Yahoo
Measuring the Subjective Value of Your Time: New Research From MIT Sloan Management Review
"The New KPI for Life" — Harvard Professor Launches LIFE Matrix CAMBRIDGE, Mass., May 13, 2025 /PRNewswire/ -- In a world increasingly dominated by performance dashboards, efficiency hacks, and optimized schedules, an old question remains: How can we spend our time in ways that truly enrich us? Research in MIT Sloan Management Review introduces a new way for individuals to measure the subjective value of their time, supported by a framework that identifies the small changes we can make that may significantly increase life satisfaction. Coauthored by Harvard Business School Professor Leslie Perlow and NYU Stern Professor Salvatore Affinito, "Time Well Spent: A New Way to Value Time Could Change Your Life," can give leaders and teams greater insight into how to align their activities with what matters most. "Traditional time management is about optimization and productivity," says Perlow. "But what's been missing is a way to see if your hours actually deliver value — if they bring you joy, a sense of accomplishment, or meaningfulness. We bridge this gap and give people the clarity and agency to make small shifts — and show that even small shifts can make a profound difference in how we live and lead." The LIFE Matrix goes beyond the focus on productivity found in traditional time management tools and, for the first time, makes it possible to assess whether you are crafting your best life with the 168 hours you have each week. It uses your own data to allow you to see whether your life is supporting your values. Based on a personal "JAM Type" — a unique assessment of how individuals prioritize Joy, Achievement, and Meaningfulness in life — and an inventory of weekly activities, the researchers show how to calculate your subjective value of time and capture how well the time you spend on activities aligns with your values. The accompanying free app (now available at maps your results onto a personalized 3x3 matrix that highlights ways you can shift your time toward higher-value activities. Though designed to support individual growth, the LIFE Matrix is also being embraced by teams, alumni groups, and leadership development programs as a way to collectively explore how time use affects well-being. The tool offers anonymized group reporting, allowing teams to see broader patterns in how people spend non-work time and where value can be elevated. "This isn't just a personal tool — it's a cultural one," says Perlow. "It opens the door to deeper conversations around intention, well-being, and what it truly means to perform at a high level. Even reallocating just one to two hours per week toward more valuable activities can significantly enhance overall life satisfaction and well-being, both at work and at home." The Research The researchers conducted multiple studies, with diverse samples of working professionals, to understand how people use their time, what is important to them in life, and what factors lead people to live more fulfilling and satisfying lives. Their samples included over 2,000 Harvard Business School alumni, from recent graduates to those celebrating their 50th reunions, and a representative sample of over 900 working professionals from across the United States. In each study, participants were surveyed using an app-based tool that included multiple questions to (1) determine people's individual needs for joy, achievement, and meaningfulness; (2) identify all their weekly activities, with rough time estimates for each; (3) rate each activity based on the degree of joy, achievement, and meaningfulness that the individual derived from it; and (4) rate their overall well-being and life satisfaction. Read the full article with data and statistics in the MIT Sloan Management Review article "Time Well Spent: A New Way to Value Time Could Change Your Life," which publishes at 1 p.m. ET on May 13, 2025. About Crafting Your Life ProjectThe LIFE Matrix is the signature tool of the Crafting Your Life Project, a research initiative developed out of a popular course at Harvard Business School that helps people live with greater intention. At the core of the project is a single, transformative question: Are you living in alignment with what matters most? Today, the project supports thousands of professionals, alumni, and leaders through tools that promote reflection, clarity, and meaningful change. To learn more, experience the tool firsthand, and contribute to groundbreaking research, visit About Crafting Your Life Project The LIFE Matrix is the signature tool of the Crafting Your Life Project, a research initiative born from one of Harvard Business School's most popular MBA courses. At its core is a powerful question: Are you living in alignment with what matters most? Throughout the semester, students explore this through three modules: (1) Defining a life well-lived, (2) Aligning their weekly schedules with their values, and (3) Preparing for life's inevitable disruptions. Now extending far beyond the classroom, the Crafting Your Life Project supports thousands of former students (known as "LIFERS," Harvard alumni, and leaders worldwide with tools and content that inspire reflection, clarity, and meaningful change. To learn more, try the tool, and contribute to ongoing research, visit About the Authors Leslie Perlow is the Konosuke Matsushita Professor of Leadership at Harvard Business School. Salvatore Affinito is an assistant professor of management and organizations at NYU Stern School of Business. About MIT Sloan Management Review MIT Sloan Management Review is an independent, research-based magazine and digital platform for business leaders published at the MIT Sloan School of Management. MIT SMR explores how leadership and management are transforming in a disruptive world. We help thoughtful leaders capture the exciting opportunities — and face down the challenges — created as technological, societal, and environmental forces reshape how organizations operate, compete, and create value. Connect with MIT Sloan Management Review on: LinkedIn Instagram Threads Blue Sky Facebook X YouTube Tess Woods Tess@ 617-942-0336 View original content to download multimedia: SOURCE MIT Sloan Management Review Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Yahoo
09-05-2025
- Business
- Yahoo
Launching a Product During a Recession Can Pay Off, Researchers Say in MIT Sloan Management Review
CAMBRIDGE, Mass., April 29, 2025 /PRNewswire/ -- It seems counterintuitive to launch a new product when many consumers are likely watching their spending. But according to new research published in MIT Sloan Management Review, economic downturns could be a strategic time to bring new offerings to market. According to the research shared in "When Launching a Product During a Recession Pays Off," "On average, new products launched during a recession have higher sales and market share and remain on the market longer than those launches during boom times (19% longer for automotive and 14% longer for fast-moving consumer goods)." "There are underlying dynamics in recessions that companies can use to their advantage in product launches," states coauthor Steven H. Seggie, an associate professor of marketing at ESSEC Business School in Paris. Because so many businesses are pulling back on spending, particularly advertising, the market is less cluttered and less noisy. That means companies that introduce new products benefit from reduced competition and greater visibility. These companies also have stronger bargaining power with both their upstream and downstream supply chain partners. Companies can secure better deals on materials, access more favorable advertising, and even obtain cheaper shelf space from retailers. "Skillfully navigating these opportunities requires leaders to pay close attention to customers, competitors, and their own company," adds coauthor Koen Pauwels, associate dean and a distinguished professor of marketing at the D'Amore-McKim School of Business at Northeastern University. "Of course, timing is important," adds coauthor Berk Talay, professor of marketing at the Manning School of Business at the University of Massachusetts Lowell. "Launching later in a recession is better than earlier. Use helpful economic indicators, track financial expert forecasts, and create KPIs to watch key variables to assist in timing." Launching products during a recession can send a powerful signal to stakeholders that the company is resilient. That enhances a company's reputation, showcasing it as a strong, well-positioned player in the market, and further boosts confidence and trust in the business's long-term prospects. Read the full article with data and statistics in the MIT Sloan Management Review article "When Launching a Product During a Recession Pays Off," which publishes at 1 p.m. ET on April 29, 2025. About the AuthorsSteven H. Seggie is an associate professor of marketing at ESSEC Business School in Paris and the academic director of its Weekend Executive MBA and Hybrid Executive MBA programs. Berk Talay is a professor of marketing at the Manning School of Business at the University of Massachusetts Lowell. Koen Pauwels is the associate dean of research and a distinguished professor of marketing at the D'Amore-McKim School of Business at Northeastern University. About MIT Sloan Management ReviewMIT Sloan Management Review is an independent, research-based magazine and digital platform for business leaders published at the MIT Sloan School of Management. MIT SMR explores how leadership and management are transforming in a disruptive world. We help thoughtful leaders capture the exciting opportunities — and face down the challenges — created as technological, societal, and environmental forces reshape how organizations operate, compete, and create value. Connect with MIT Sloan Management Review on: LinkedIn Instagram Threads Blue Sky Facebook X YouTube Tess WoodsTess@ View original content to download multimedia: SOURCE MIT Sloan Management Review Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Channel Post MEA
26-03-2025
- Business
- Channel Post MEA
Data Immaturity: A roadblock To Advanced AI
What is data immaturity? Data immaturity, in the context of AI, refers to an organisation's underdeveloped or inadequate data practices, which limit its ability to leverage AI effectively. It encompasses issues with data quality, accessibility, governance, and infrastructure such as: Poor data quality : Inconsistent, incomplete, or outdated data leads to unreliable AI outcomes. : Inconsistent, incomplete, or outdated data leads to unreliable AI outcomes. Limited data availability : Data silos across departments hinder access and comprehensive analysis, limiting insights. : Data silos across departments hinder access and comprehensive analysis, limiting insights. Weak data governance : Lack of policies on data ownership, compliance, and security introduces risks and restricts AI usage. : Lack of policies on data ownership, compliance, and security introduces risks and restricts AI usage. Inadequate data infrastructure : Insufficient tools and infrastructure impede data processing and AI model training at scale. : Insufficient tools and infrastructure impede data processing and AI model training at scale. Unclear data strategy: Lack of a clear strategy results in uncoordinated initiatives and limited focus on valuable data for AI. Data immaturity prevents organisations from harnessing the full potential of AI because high-quality, well-managed, and accessible data is foundational for developing reliable and effective AI systems. Organisations looking to overcome data immaturity often start by building a data strategy, implementing data governance policies, investing in data infrastructure, and enhancing data literacy across teams. The impact on AI adoption In short, data immaturity is a drag on AI adoption. Adoption is already slowing because organisations have, for the most part, already picked the low-hanging generative AI fruit (chatbots, assistants, co-pilots) and are running into data immaturity issues as they try to move toward the more valuable use cases such as workflow automation. Organisations that fail to prioritise data maturity will struggle to unlock these more advanced AI capabilities. Data immaturity leads to a lack of trust in analysis and predictability of execution. That puts a damper on any plans to leverage AI in a more autonomous manner—whether for business or operational process automation. A 2023 study by MIT Sloan Management Review highlights that organisations with mature data management practices are 60 per cent more likely to succeed in workflow automation than those with immature data practices. Data immaturity limits the predictive accuracy and reliability of AI, which are crucial for autonomous functions where decisions are made without human intervention. Organisations must get their data houses in order before they will be able to truly take advantage of AI's potential to optimise workflows and free up valuable time for humans to focus on strategy and design, tasks for which most AI is not yet well suited. Overcoming data immaturity Addressing data immaturity is crucial for enabling advanced AI capabilities. Key steps include: Develop a clear data strategy Align data collection, management, and quality standards with organisational goals to ensure data supports AI projects effectively. Implement robust data governance Establish policies for data ownership, compliance, security, and privacy to improve data quality and build trust in AI insights. Invest in scalable data infrastructure Adopt modern infrastructure, such as cloud storage and data pipelines, to support efficient processing and scalable AI training. Enhance data quality standards Set standards for data accuracy, consistency, and completeness, with regular monitoring and cleaning. Promote data literacy and collaboration Foster a culture of data literacy and teamwork between data and business units to improve data accessibility and impact. By adopting these practices, organisations can establish a solid data foundation for AI, leading to optimised workflows, reduced risks, and more time for strategic tasks. Data maturity is not just a technical necessity; it's a strategic advantage that empowers organisations to unlock the full potential of AI. By overcoming data immaturity, organisations can transition from basic AI applications to more transformative, value-driven use cases, ultimately positioning themselves for long-term success in an AI-driven future.
Tahawul Tech
26-03-2025
- Business
- Tahawul Tech
Data Immaturity: A roadblock to advanced AI
Lori MacVittie, F5 Distinguished Engineer, discusses the nature of data immaturity and its impact on AI adoption in this exclusive op-ed. Every survey on generative AI—including our own—points to one inescapable conclusion: data immaturity is going to get in the way of fully realising the potential of generative AI. When we asked about challenges to AI adoption in our 2024 State of Application Strategy report, the top response with 56% of respondents was 'data immaturity.' A quick look around the industry validates that data immaturity is a serious obstacle on the AI adoption path. What is data immaturity? Data immaturity, in the context of AI, refers to an organisation's underdeveloped or inadequate data practices, which limit its ability to leverage AI effectively. It encompasses issues with data quality, accessibility, governance, and infrastructure such as: Poor data quality: Inconsistent, incomplete, or outdated data leads to unreliable AI outcomes. Limited data availability: Data silos across departments hinder access and comprehensive analysis, limiting insights. Weak data governance: Lack of policies on data ownership, compliance, and security introduces risks and restricts AI usage. Inadequate data infrastructure: Insufficient tools and infrastructure impede data processing and AI model training at scale. Unclear data strategy: Lack of a clear strategy results in uncoordinated initiatives and limited focus on valuable data for AI. Data immaturity prevents organisations from harnessing the full potential of AI because high-quality, well-managed, and accessible data is foundational for developing reliable and effective AI systems. Organisations looking to overcome data immaturity often start by building a data strategy, implementing data governance policies, investing in data infrastructure, and enhancing data literacy across teams. The impact on AI adoption In short, data immaturity is a drag on AI adoption. Adoption is already slowing because organizations have, for the most part, already picked the low-hanging generative AI fruit (chatbots, assistants, co-pilots) and are running into data immaturity issues as they try to move toward the more valuable use cases such as workflow automation. Organisations that fail to prioritize data maturity will struggle to unlock these more advanced AI capabilities. Data immaturity leads to a lack of trust in analysis and predictability of execution. That puts a damper on any plans to leverage AI in a more autonomous manner—whether for business or operational process automation. A 2023 study by MIT Sloan Management Review highlights that organisations with mature data management practices are 60% more likely to succeed in workflow automation than those with immature data practices. Data immaturity limits the predictive accuracy and reliability of AI, which are crucial for autonomous functions where decisions are made without human intervention. Organisations must get their data houses in order before they will be able to truly take advantage of AI's potential to optimise workflows and free up valuable time for humans to focus on strategy and design, tasks for which most AI is not yet well suited. Overcoming data immaturity Addressing data immaturity is crucial for enabling advanced AI capabilities. Key steps include: Develop a clear data strategy Align data collection, management, and quality standards with organisational goals to ensure data supports AI projects effectively. Implement robust data governance Establish policies for data ownership, compliance, security, and privacy to improve data quality and build trust in AI insights. Invest in scalable data infrastructure Adopt modern infrastructure, such as cloud storage and data pipelines, to support efficient processing and scalable AI training. Enhance data quality standards Set standards for data accuracy, consistency, and completeness, with regular monitoring and cleaning. Promote data literacy and collaboration Foster a culture of data literacy and teamwork between data and business units to improve data accessibility and impact. By adopting these practices, organisations can establish a solid data foundation for AI, leading to optimised workflows, reduced risks, and more time for strategic tasks. Data maturity is not just a technical necessity; it's a strategic advantage that empowers organisations to unlock the full potential of AI. By overcoming data immaturity, organisations can transition from basic AI applications to more transformative, value-driven use cases, ultimately positioning themselves for long-term success in an AI-driven future. Image Credit: F5
