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Rebecca S
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ANALYSIS OF SQREAM’S DATA PROCESSING AND ANALYTICS PLATFORM
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Denodo’s Data Governance Capabilities: A Comparative Analysis
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Data Monetization and Return on Data Investment (RODI): Unlocking the True Value of Data
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Data as a Product (DaaP): A New Paradigm in Data Management
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State of Mathematical Optimization 2024
State of Mathematical Optimization 2024  Overview Gurobi is pleased to share the State of Mathematical Optimization 2024 Report, highlighting how today’s operations researchers, data scientists, and other professionals are applying mathematical optimization to tackle complex challenges. Earlier this year, Gurobi surveyed 440 commercial users to understand how they use mathematical optimization at work. This third survey in the series explores trends in optimization adoption, business engagement, and the impact of mathematical optimization on decision-making.  Survey Profile  Education & Experience A. 82% of respondents hold advanced degrees: 46% have a master’s degree, and 36% have a doctorate. B. 57% studied Operations Research, followed by Data Science (34%), Mathematics (32%), and Engineering (31%). C. 41% have more than seven years of commercial optimization experience, while 59% have less than six years of experience.  Job Roles & Industries A. 29% of respondents work in Operations Research, followed by Data Science (19%), Analytics (11%), and Engineering (11%). B. Key industries represented: Consulting (13%), Transportation (10%), Power & Utilities (10%), and Supply Chain (8%).  Methodology The report focuses on commercial users and excludes academic and other non-commercial users to better understand optimization trends in business settings. Key Findings 1. 98% reported that the number of operations researchers at their organization was growing or stable, a 5% increase from last year. 2. 93% indicated that mathematical optimization was gaining traction or maintaining stability with decision-makers. 3. 68% reported having more than one operations research professional at their workplace. 4. 81% use machine learning and mathematical optimization together for at least one project. 5. 84% said their work is mission-critical to their organization.  Mathematical Optimization at Work Growing Demand for Operations Researchers The U.S. Bureau of Labor Statistics predicts a 24,200 increase in operations research jobs and 40,500 additional data scientist roles by 2031. As demand rises, competition for talent skilled in solvers like Gurobi is expected to grow. A. 68% of respondents work with at least one other operations research professional. B. 56% reported a stable number of OR professionals in their company, while only 2% reported a decline. Optimization’s Increasing Role in Business Decision-Making A. 53% of respondents reported that mathematical optimization is gaining traction with decision-makers. B. 40% said its adoption remained steady. C. 84% stated that their work in mathematical optimization is mission-critical. D. 86% believe their organization appreciates the value they provide.  Mathematical Optimization in Practice Top Use Cases  Mathematical optimization is widely applied across multiple domains: 1. 55% use it for planning. 2. 45% apply it in operational settings. 3. 42% leverage it for production planning. 4. 41% employ it for supply chain management. 5. 40% use it in logistics.  Preferred Programming Languages & Problem Types 1. 83% prefer Python for optimization modelling, followed by Java (10%), C++ (10%), and R (9%). 2. 73% solve mixed-integer programming (MIP) problems. 3. 50% work with linear programming. 4. 39% address multi-objective problems.  Integration of Machine Learning and Optimization A Powerful Combination • 81% of organizations integrate machine learning with mathematical optimization—up from 46% in 2020. • Most common machine learning techniques combined with optimization:  Regression (66%)  Clustering (53%)  Classification (52%) • 62% use Scikit-Learn, followed by PyTorch (46%) and TensorFlow (44%). • 55% of respondents collaborate with data scientists weekly.  The Gurobi Advantage Why Organizations Switch to Gurobi Gurobi is the solver of choice for many due to its: 1. Speed – 78% cited faster performance as their reason for switching. 2. Versatility – 41% value Gurobi’s ability to handle diverse optimization problems. 3. Technical Support – 41% appreciate Gurobi’s expert guidance. 4. Python API – 39% prefer Gurobi’s seamless integration with Python. 5. Numerical Robustness – 27% highlighted its accuracy in complex computations.  Business Impact Organizations using Gurobi report: 1. 57% improved operational efficiency. 2. 53% minimized costs. 3. 35% maximized profits. 4. 32% increased revenue. 5. 18% reduced waste. 6. 16% decreased downtime.  The Future of Mathematical Optimization As businesses experience the transformative impact of mathematical optimization, investment in optimization expertise is expected to rise. The U.S. Bureau of Labor Statistics projects that operations research will be among the fastest-growing professions between 2023 and 2033.  Emerging Trends 1. Increased focus on predictive analytics (machine learning), prescriptive analytics (optimization), and generative AI. 2. Growing demand for interdisciplinary teams with expertise in data science, AI, and optimization. 3. Expansion of optimization applications beyond traditional industries into customer service and HR functions.  For more insights and detailed data, visit: Gurobi Academic Resources.
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UNLOCKING CUSTOMER POTENTIAL: A DEEP DIVE INTO CUSTOMER 360 SUCCESS
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API Governance: Ensuring Secure & Scalable APIs
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