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The Gartner Hype Cycle is a valuable framework for understanding where an emerging technology stands on its journey into the mainstream. It helps chart public perception, from the “Peak of Inflated Expectations” through the “Trough of Disillusionment,” and eventually up the “Slope of Enlightenment” toward the “Plateau of Productivity.” In 2015, Gartner removed big data from the Hype Cycle. Analyst Betsy Burton explained that it was no longer considered an “emerging technology” and has become prevalent in our lives. Shes right. In hindsight, it’s remarkable how quickly enterprises recognized the value of their data and learned to use it for their business advantage. Big data moved from novelty to necessity at an impressive pace. Yet in some ways, I disagree with Gartner. Adoption has been widespread, but effectiveness is another matter. Do most enterprises truly have the tools and infrastructure to make the most of the data they hold? I dont believe they do. Which is why I also dont believe the true big data revolution has happened yet. But it’s coming. Dissecting the Stack A key reason big data is seen as mature, even mundane, is that people often confuse software progress with overall readiness. The reality is more nuanced. Yes, the software is strong. We have robust platforms for managing, querying, and analyzing massive datasets. Many enterprises have assembled entire software stacks that work well. But that software still needs hardware to run on. And here lies the bottleneck. Most data-intensive workloads still rely on traditional central processing units (CPUs)the same processors used for general IT tasks. This creates challenges. CPUs are expensive, energy hungry, and not particularly well suited to parallel processing. When a query needs to run across terabytes or even petabytes of data, engineers often divide the work into smaller tasks and process them sequentially. This method is inefficient and time-consuming. It also ends up requiring more total computation than a single large job would. Even though CPUs can run at high clock speeds, they simply don’t have enough cores to efficiently handle complex queries at scale. As a result, hardware has limited the potential of big data. But now, thats starting to change with the rise of accelerated computing. Breaking the Bottleneck Accelerated computing refers to running workloads on specialized hardware designed to outperform CPUs. This could mean field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs) built for a specific task. More relevant to big data, though, are graphics processing units (GPUs). GPUs contain thousands of cores and are ideal for tasks that benefit from parallel processing. They can dramatically speed up large-scale data operations. Interestingly, GPU computing and big data emerged around the same time. Nvidia launched CUDA (compute unified device architecture) in 2006, enabling general-purpose computing on graphics hardware. Just two years earlier, Googles MapReduce paper laid the foundation for modern big data processing. Despite this parallel emergence, GPUs havent become a standard part of enterprise data infrastructure. Thats due to a mix of factors. For one, cloud-based access to GPUs was limited until relatively recently. When I started building GPU-accelerated software, SoftLayernow absorbed into IBM Cloudwas the only real option. There was also a perception problem. Many believed GPU development was too complex and costly to justify, especially for general business needs. And for a long time, few ready-made tools existed to make it easier. Those barriers have largely fallen. Today, a rich ecosystem of software exists to support GPU-accelerated computing. CUDA tools have matured, benefiting from nearly two decades of continuous development. And renting a top-tier GPU, like Nvidias A100, now costs as little as $1 per hour. With affordable access and a better software stack, were finally seeing the pieces fall into place. The Real Big Data Revolution Whats coming next will be transformative. Until now, most enterprises have been constrained by hardware limits. With GPU acceleration more accessible and a mature ecosystem of supporting tools, those constraints are finally lifting. The impact will vary by organization. But broadly, companies will gain the ability to run complex data operations across massive datasets, without needing to worry about processing time or cost. With faster, cheaper insights, businesses can make better decisions and act more quickly. The value of data will shift from how much is collected to how quickly it can be used. Accelerated computing will also enable experimentation. Freed from concerns about query latency or resource drain, enterprises can explore how their data might power generative AI, smarter applications, or entirely new user experiences. Gartner took big data off the Hype Cycle because it no longer seemed revolutionary. Accelerated computing is about to make it revolutionary again.
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In an age that prizes specialization, we’re often encouraged to distill our identities into a singular narrativeyour specialization, your personal brand, a streamlined profile that fits neatly into a predefined box. Yet while the world rewards narrow expertise, it simultaneously demands multidimensional thinking. Innovation and resilience don’t emerge from narrowing down; they arise from exploring intersections and embracing contradictions. The leaders we need today are not one-dimensional experts but multidimensional individuals who can hold tension, connect disparate disciplines, and lead from a place of full-spectrum presence. However, many unconventional thinkersespecially those with deep technical or creative giftsfeel pressured to conform to traditional leadership molds. The entrepreneur-turned-creative director may feel their place is to produce big ideas, not weigh in with their operational expertise; the lead programmer with a fine arts background may think their domain is confined to coding, even if they could lend fresh ideas about product design. This tension often leads to disconnection, burnout, and a reservoir of untapped potential. So what does it mean to embrace your full dimensionality as a leadership strategy? The Case for Wholeness Institutions like MIT Sloan have championed the concept of the “T-shaped” leader, a person with deep expertise in one domain and broad collaborative fluency across disciplines. These leaders are better equipped to navigate complexity, break down organizational silos, and foster innovation. But today’s challenges require more than just cross-functional skills; they demand wholeness. Leaders must access intellect and intuition, logic and emotion, embodiment and systems thinking. This holistic approach isn’t new. During the Italian Renaissance, figures like Leonardo da Vinci seamlessly integrated art and science, embodying the essence of multidimensional leadership. This isnt a theoretical shift; Ive seen it in action. In a recent engagement with a life sciences executive team, we paused our strategic agenda to explore personal leadership stories. One leader, a deeply analytical CFO, opened up about a passion for playing jazz guitar that had shaped how he collaborates and leads others. That single story shifted the room. His vulnerability allowed others to bring themselves into the conversation, and the teams cohesion transformed. The business outcomes didnt come from better models, but from deeper connections. This kind of Renaissance energy isnt a luxury; its a catalyst. During the Renaissance, thinkers like da Vinci seamlessly integrated disciplines to show up as fully expressed humans. That spirit lives in us still. We dont need more narrow specialists. We need leaders willing to be curious, courageous, and creatively integrated. A Framework for Full-Dimensional Leadership Leading from your full dimensionality doesnt happen by accident. It requires thoughtful exploration and practice. A way of thinking I call the 3D frameworkdiscover, distill, designoffers a regenerative pathway from internal awareness to external impact. Its not a linear ladder, but a looping process of personal and professional evolution. 1. Discover. Begin by understanding yourself more deeply. Reconnect with your experiences, surface buried brilliance, and reclaim sidelined aspects of your identity. Reflect on questions like: What moments have significantly shaped my journey? Where have I conformed at the expense of authenticity? What parts of me are yearning to lead? Where does your true brilliance live? Leaders who demonstrate high self-awareness are more effective in their roles, fostering stronger team dynamics and grounded decision-making. 2. Distill. After discovery comes discernment. Strip away distractions to focus on what’s essential. Integrate your roles, values, and responsibilities by considering: What narratives about myself need rewriting? What is the essence of my leadership at this moment? What tensions should I hold rather than resolve? Effective leaders today are defined not by certainty, but by their ability to navigate complexity with clarity. 3. Design. From clarity comes creation. Lead with conscious intention, designing systems and cultures that reflect your unique brilliance. This is where multidimensional leadership becomes visible, where presence meets action. Ask yourself: What future is emerging through me? What systems, habits, and environments need to change? How can I embody leadership that energizes rather than exhausts? At Ideo, leaders are encouraged to prototype their leadership models based not on job titles, but on what energizes them. This intentional experimentation has led to measurable improvements in psychological safety, team cohesion, and innovation velocity. Design isnt just about what you build but how you build it. The End of One-Dimensional Leadership Challenges like climate instability, AI disruption, and cultural fragmentation can’t be addressed with logic alone. They require leaders who can hold polarities, think and feel, zoom in and out, and integrate data with empathy. This multidimensional leadership isn’t a luxury; it’s a necessity. It enables leaders to move from reactivity to intentionality, from exhaustion to resonance. If you’ve ever felt the need to fragment yourself to be effective, consider this an invitation to return to wholeness. Discover who you are, distill what matters, and design what’s next. The future will be led by those who are most fully alive and courageous enough to lead from that place.
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Grasses growing in the shade of a solar array were only a little less productive than those growing nearby in open grassland during years of average and above-average rainfallbut in a dry year, the shaded plants grew much better than those growing in full sun. Thats the result of a four-year study we conducted in a semi-arid grassland of northern Colorado. When choosing a location for generating solar power, consistent sunlight and interconnection to the electric grid are key criteria. In Colorado, the combination of new electrical transmission infrastructure, abundant sunlight, and short vegetation that is easy to maintain have made grasslands a prime target for solar development. Grasslands, like those that dominate the eastern plains of Colorado, provide important habitat for wildlife and serve as a critical food source for livestock. Although these grasslands have long been productive despite their normally arid environment, a warmer climate has increased the potential for more frequent and severe drought. For instance, a recent global study found that previous research likely underestimated the threat of extreme drought in grasslands. Semi-arid grassland near Cheyenne, Wyo., with close-ups of flowers of some of the plants that grow there. [Photo: Matthew Sturchio, CC BY-ND] At Colorado State University, biology professor Alan Knapp and I started the ecovoltaics research group to study the effects of solar development in grasslands. Our primary goal is to ensure an ecologically informed solar energy future. Solar panels create microclimates Strings of solar panels redirect rain to the edge of panels. Because of this, small rain events can provide biologically relevant amounts of water instead of evaporating quickly. Simultaneously, solar panels shade plants growing beneath them. Some arrays, including the ones used in our study, move the panels to follow the path of the sun across the sky. This results in a combination of sun and shade that is very different from the uninterrupted sunlight beating down on plants in a grassland without solar panels. In turn, patterns of plant stress and water loss also differ in grasses under solar arrays. How grasses respond to a solar panel canopy To get a handle on how these different conditions affect grasses, we measured plant physiological response during the early stages of our study. More specifically, we tracked leaf carbon and water exchange throughout daylight hours, 9 a.m. to 5 p.m., over 16 weeks in summer 2022 at Jacks Solar Garden, a solar array over grassland in Longmont, Colorado. In general, plants that are adapted to full sun conditions, including most grasses, might not be expected to grow as well in partial shade. But we suspected that growth benefits from reduced water stress could outweigh potential reductions in growth from shading. We call this the aridity mitigation potential hypothesis. Sure enough, we found evidence of aridity mitigation across multiple years, with the most pronounced effect during the driest year. When water is scarce, increases in grassland productivity are more valuable because there isnt as much around. Therefore, increasing grassland production in dry years could provide more available food for grazing animals and help offset some of the economic harm of drought in rangelands. Informing sustainable solar development in grasslands So far, our research has been limited to a grassland dominated by a cool-season grass: smooth brome. Although it is a perennial commonly planted for hay, fields dominated by smooth bromegrass lack the diversity of life found in native grasslands. Future work in native shortgrass prairies would provide new information about how solar panels affect plant water use, soils, and grazing management in an ecosystem with 30% less precipitation than Jacks Solar Garden. Were beginning that work now at the shortgrass ecovoltaic research facility near Nunn, Colorado. This facility, which will be fully operational later in 2025, was constructed with support from the U.S. Department of Agriculture, through the wider SCAPES project. Testing the effects of solar panels over grasslands in a native ecosystem with even greater aridity will help us develop a clearer picture of ways solar energy can be developed in concert with grassland health. Matthew Sturchio is a postdoctoral research associate in natural resources and the environment at Cornell University and a faculty affiliate in ecology at Colorado State University. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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