Data analytics project management (106 résultats)

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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Master's Thesis from the year 2018 in the subject Computer Science - Commercial Information Technology, grade: 7, Queensland University of Technology (Faculty of Science and Engineering), course: Master in Business Process Management, language: English, abstract: This thesis investigates the project management approach for big data projects for industry partner Red Rocks Company. The aim of this project is to understand best practice project management for big data initiatives and to develop a framework to help such projects to deliver the expected advantages. A brief literature review is undertaken to find out how big data projects are managed. From this, a Big Data Analytics Framework is derived which is based on CRISP-DM. The framework is validated through interviews with stakeholders from the corporate sector. For this case study, the first three phases of the Business Process Management Lifecycle are applied: process discovery, analysis and design.Key findings of the case study are that literature recommends an agile project management approach for big data initiatives. On the contrary, the majority of interviewed industry stakeholders confirmes a waterfall approach is conducted more often to deliver such projects. The developed Big Data Analytics Framework will add significant benefits to Red Rocks Company as it will help to successfully deliver big data initiatives in future. Big data is considered a key enabler for future decision making and process automation. The topic is however very new and not well understood yet. Hence 50% of big data projects are not delivering the expected benefits and are costing more than initially planned.…

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Taschenbuch. Etat : Neu. Neuware - Turn project data into better decisions-and better project outcomes.Modern projects generate enormous amounts of data across schedules, budgets, resources, risks, milestones, changes, and team performance. But having data is not the same as knowing what to do with it.Project Management Analytics for Beginners is a practical, easy-to-follow guide to using analytics to plan projects more effectively, identify problems earlier, control costs, manage risks, optimize resources, and improve delivery performance.Written for project managers, PMO professionals, business analysts, consultants, students, and aspiring project leaders, this book explains analytics without requiring a background in statistics or data science.Inside, you'll learn how to: - Measure project performance using meaningful KPIs and metrics- Analyze schedule delays, milestones, dependencies, and bottlenecks- Use Earned Value Management (EVM) to understand cost and schedule performance- Track SPI, CPI, cost variance, schedule variance, and project forecasts- Analyze budgets, burn rates, resource utilization, and productivity- Use Agile metrics including velocity, cycle time, throughput, and burndown- Quantify and analyze project risks- Identify scope creep, rework, quality problems, and change patterns- Build effective project and PMO dashboards- Apply predictive analytics to anticipate delays and cost overruns- Understand how AI and Generative AI are changing project management- Use AI prompts to support planning, reporting, risk analysis, and decision-making- Turn project reporting into actionable project intelligenceWith practical explanations, worked examples, case studies, formulas, dashboards, and AI applications, Project Management Analytics for Beginners bridges the gap between traditional project management and modern data-driven decision-making.Whether you manage a single project or an entire portfolio, this book will help you move beyond simply asking 'What happened ' toward answering the more valuable questions: Why did it happen What is likely to happen next And what should we do about it.…

Paperback. Etat : New. This book aims to help the reader better understand the importance of data analysis in project management. Moreover, it provides guidance by showing tools, methods, techniques and lessons learned on how to better utilize the data gathered from the projects. First and foremost, insight into the bridge between data analytics and project management aids practitioners looking for ways to maximize the practical value of data procured. The book equips organizations with the know-how necessary to adapt to a changing workplace dynamic through key lessons learned from past ventures. The book's integrated approach to investigating both fields enhances the value of research findings. …

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