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| data analytics vs project management: Data Analytics in Project Management Seweryn Spalek, J. Davidson Frame, Yanping Chen, Carl Pritchard, Alfonso Bucero, Werner Meyer, Ryan Legard, Michael Bragen, Klas Skogmar, Deanne Larson, Bert Brijs, 2019-01-01 Data Analytics in Project Management. Data analytics plays a crucial role in business analytics. Without a rigid approach to analyzing data, there is no way to glean insights from it. Business analytics ensures the expected value of change while that change is implemented by projects in the business environment. Due to the significant increase in the number of projects and the amount of data associated with them, it is crucial to understand the areas in which data analytics can be applied in project management. This book addresses data analytics in relation to key areas, approaches, and methods in project management. It examines: • Risk management • The role of the project management office (PMO) • Planning and resource management • Project portfolio management • Earned value method (EVM) • Big Data • Software support • Data mining • Decision-making • Agile project management Data analytics in project management is of increasing importance and extremely challenging. There is rapid multiplication of data volumes, and, at the same time, the structure of the data is more complex. Digging through exabytes and zettabytes of data is a technological challenge in and of itself. How project management creates value through data analytics is crucial. Data Analytics in Project Management addresses the most common issues of applying data analytics in project management. The book supports theory with numerous examples and case studies and is a resource for academics and practitioners alike. It is a thought-provoking examination of data analytics applications that is valuable for projects today and those in the future. |
| data analytics vs project management: Project Management Analytics Harjit Singh, 2015-11-12 To manage projects, you must not only control schedules and costs: you must also manage growing operational uncertainty. Today’s powerful analytics tools and methods can help you do all of this far more successfully. In Project Management Analytics, Harjit Singh shows how to bring greater evidence-based clarity and rationality to all your key decisions throughout the full project lifecycle. Singh identifies the components and characteristics of a good project decision and shows how to improve decisions by using predictive, prescriptive, statistical, and other methods. You’ll learn how to mitigate risks by identifying meaningful historical patterns and trends; optimize allocation and use of scarce resources within project constraints; automate data-driven decision-making processes based on huge data sets; and effectively handle multiple interrelated decision criteria. Singh also helps you integrate analytics into the project management methods you already use, combining today’s best analytical techniques with proven approaches such as PMI PMBOK® and Lean Six Sigma. Project managers can no longer rely on vague impressions or seat-of-the-pants intuition. Fortunately, you don’t have to. With Project Management Analytics, you can use facts, evidence, and knowledge—and get far better results. Achieve efficient, reliable, consistent, and fact-based project decision-making Systematically bring data and objective analysis to key project decisions Avoid “garbage in, garbage out” Properly collect, store, analyze, and interpret your project-related data Optimize multi-criteria decisions in large group environments Use the Analytic Hierarchy Process (AHP) to improve complex real-world decisions Streamline projects the way you streamline other business processes Leverage data-driven Lean Six Sigma to manage projects more effectively |
| data analytics vs project management: The Data-Driven Project Manager Mario Vanhoucke, 2018-03-27 Discover solutions to common obstacles faced by project managers. Written as a business novel, the book is highly interactive, allowing readers to participate and consider options at each stage of a project. The book is based on years of experience, both through the author's research projects as well as his teaching lectures at business schools. The book tells the story of Emily Reed and her colleagues who are in charge of the management of a new tennis stadium project. The CEO of the company, Jacob Mitchell, is planning to install a new data-driven project management methodology as a decision support tool for all upcoming projects. He challenges Emily and her team to start a journey in exploring project data to fight against unexpected project obstacles. Data-driven project management is known in the academic literature as “dynamic scheduling” or “integrated project management and control.” It is a project management methodology to plan, monitor, and control projects in progress in order to deliver them on time and within budget to the client. Its main focus is on the integration of three crucial aspects, as follows: Baseline Scheduling: Plan the project activities to create a project timetable with time and budget restrictions. Determine start and finish times of each project activity within the activity network and resource constraints. Know the expected timing of the work to be done as well as an expected impact on the project’s time and budget objectives. Schedule Risk Analysis: Analyze the risk of the baseline schedule and its impact on the project’s time and budget. Use Monte Carlo simulations to assess the risk of the baseline schedule and to forecast the impact of time and budget deviations on the project objectives. Project Control: Measure and analyze the project’s performance data and take actions to bring the project on track. Monitor deviations from the expected project progress and control performance in order to facilitate the decision-making process in case corrective actions are needed to bring projects back on track. Both traditional Earned Value Management (EVM) and the novel Earned Schedule (ES) methods are used. What You'll Learn Implement a data-driven project management methodology (also known as dynamic scheduling) which allows project managers to plan, monitor, and control projects while delivering them on time and within budget Study different project management tools and techniques, such as PERT/CPM, schedule risk analysis (SRA), resource buffering, and earned value management (EVM) Understand the three aspects of dynamic scheduling: baseline scheduling, schedule risk analysis, and project control Who This Book Is For Project managers looking to learn data-driven project management (or dynamic scheduling) via a novel, demonstrating real-time simulations of how project managers can solve common project obstacles |
| data analytics vs project management: Data Analytics for Engineering and Construction Project Risk Management Ivan Damnjanovic, Kenneth Reinschmidt, 2019-05-23 This book provides a step-by-step guidance on how to implement analytical methods in project risk management. The text focuses on engineering design and construction projects and as such is suitable for graduate students in engineering, construction, or project management, as well as practitioners aiming to develop, improve, and/or simplify corporate project management processes. The book places emphasis on building data-driven models for additive-incremental risks, where data can be collected on project sites, assembled from queries of corporate databases, and/or generated using procedures for eliciting experts’ judgments. While the presented models are mathematically inspired, they are nothing beyond what an engineering graduate is expected to know: some algebra, a little calculus, a little statistics, and, especially, undergraduate-level understanding of the probability theory. The book is organized in three parts and fourteen chapters. In Part I the authors provide the general introduction to risk and uncertainty analysis applied to engineering construction projects. The basic formulations and the methods for risk assessment used during project planning phase are discussed in Part II, while in Part III the authors present the methods for monitoring and (re)assessment of risks during project execution. |
| data analytics vs project management: Data Analytics Initiatives Ondřej Bothe, Ondřej Kubera, David Bednář, Martin Potančok, Ota Novotný, 2022-04-20 The categorisation of analytical projects could help to simplify complexity reasonably and, at the same time, clarify the critical aspects of analytical initiatives. But how can this complex work be categorized? What makes it so complex? Data Analytics Initiatives: Managing Analytics for Success emphasizes that each analytics project is different. At the same time, analytics projects have many common aspects, and these features make them unique compared to other projects. Describing these commonalities helps to develop a conceptual understanding of analytical work. However, features specific to each initiative affects the entire analytics project lifecycle. Neglecting them by trying to use general approaches without tailoring them to each project can lead to failure. In addition to examining typical characteristics of the analytics project and how to categorise them, the book looks at specific types of projects, provides a high-level assessment of their characteristics from a risk perspective, and comments on the most common problems or challenges. The book also presents examples of questions that could be asked of relevant people to analyse an analytics project. These questions help to position properly the project and to find commonalities and general project challenges. |
| data analytics vs project management: Agile Analytics Ken Collier, 2012 Using Agile methods, you can bring far greater innovation, value, and quality to any data warehousing (DW), business intelligence (BI), or analytics project. However, conventional Agile methods must be carefully adapted to address the unique characteristics of DW/BI projects. In Agile Analytics, Agile pioneer Ken Collier shows how to do just that. Collier introduces platform-agnostic Agile solutions for integrating infrastructures consisting of diverse operational, legacy, and specialty systems that mix commercial and custom code. Using working examples, he shows how to manage analytics development teams with widely diverse skill sets and how to support enormous and fast-growing data volumes. Collier's techniques offer optimal value whether your projects involve back-end data management, front-end business analysis, or both. Part I focuses on Agile project management techniques and delivery team coordination, introducing core practices that shape the way your Agile DW/BI project community can collaborate toward success Part II presents technical methods for enabling continuous delivery of business value at production-quality levels, including evolving superior designs; test-driven DW development; version control; and project automation Collier brings together proven solutions you can apply right now--whether you're an IT decision-maker, data warehouse professional, database administrator, business intelligence specialist, or database developer. With his help, you can mitigate project risk, improve business alignment, achieve better results--and have fun along the way. |
| data analytics vs project management: Project Management Waterfall-Agile-It-Data Science Dr. Festus Elleh PhD PMP PMI-ACP, 2023-03-22 This book is intended to introduce learners to waterfall, agile, information technology, and data science project management methodologies. Readers will learn about the concepts, processes, tools, and techniques that are useful for executing projects in waterfall, agile information technology, and data science environments. The objective is for learners to become contributors to the field of project management and deploy a structured approach to managing projects. Learners who read this book will be able to think critically about the concepts and practices of project management and perform exceptionally well in the PMP and PMI-ACP examinations. |
| data analytics vs project management: Analytics Phil Simon, 2017-07-03 For years, organizations have struggled to make sense out of their data. IT projects designed to provide employees with dashboards, KPIs, and business-intelligence tools often take a year or more to reach the finish line...if they get there at all. This has always been a problem. Today, though, it's downright unacceptable. The world changes faster than ever. Speed has never been more important. By adhering to antiquated methods, firms lose the ability to see nascent trends—and act upon them until it's too late. But what if the process of turning raw data into meaningful insights didn't have to be so painful, time-consuming, and frustrating? What if there were a better way to do analytics? Fortunately, you're in luck... Analytics: The Agile Way is the eighth book from award-winning author and Arizona State University professor Phil Simon. Analytics: The Agile Way demonstrates how progressive organizations such as Google, Nextdoor, and others approach analytics in a fundamentally different way. They are applying the same Agile techniques that software developers have employed for years. They have replaced large batches in favor of smaller ones...and their results will astonish you. Through a series of case studies and examples, Analytics: The Agile Way demonstrates the benefits of this new analytics mind-set: superior access to information, quicker insights, and the ability to spot trends far ahead of your competitors. |
| data analytics vs project management: Aligning Business Strategies and Analytics Murugan Anandarajan, Teresa D. Harrison, 2018-09-27 This book examines issues related to the alignment of business strategies and analytics. Vast amounts of data are being generated, collected, stored, processed, analyzed, distributed and used at an ever-increasing rate by organizations. Simultaneously, managers must rapidly and thoroughly understand the factors driving their business. Business Analytics is an interactive process of analyzing and exploring enterprise data to find valuable insights that can be exploited for competitive advantage. However, to gain this advantage, organizations need to create a sophisticated analytical climate within which strategic decisions are made. As a result, there is a growing awareness that alignment among business strategies, business structures, and analytics are critical to effectively develop and deploy techniques to enhance an organization’s decision-making capability. In the past, the relevance and usefulness of academic research in the area of alignment is often questioned by practitioners, but this book seeks to bridge this gap. Aligning Business Strategies and Analytics: Bridging Between Theory and Practice is comprised of twelve chapters, divided into three sections. The book begins by introducing business analytics and the current gap between academic training and the needs within the business community. Chapters 2 - 5 examines how the use of cognitive computing improves financial advice, how technology is accelerating the growth of the financial advising industry, explores the application of advanced analytics to various facets of the industry and provides the context for analytics in practice. Chapters 6 - 9 offers real-world examples of how project management professionals tackle big-data challenges, explores the application of agile methodologies, discusses the operational benefits that can be gained by implementing real-time, and a case study on human capital analytics. Chapters 10 - 11 reviews the opportunities and potential shortfall and highlights how new media marketing and analytics fostered new insights. Finally the book concludes with a look at how data and analytics are playing a revolutionary role in strategy development in the chemical industry. |
| data analytics vs project management: Event Project Management Mohamed Salama, 2021-01-27 This text provides a unique lens for studying event project management in the era of sustainability, digital transformation, smart cities and rapid development in technology. It discusses and explains how to manage events utilising the sustainable project management model adapted to the specific context of event management. |
| data analytics vs project management: Financial Data Analytics Sinem Derindere Köseoğlu, 2022-04-25 This book presents both theory of financial data analytics, as well as comprehensive insights into the application of financial data analytics techniques in real financial world situations. It offers solutions on how to logically analyze the enormous amount of structured and unstructured data generated every moment in the finance sector. This data can be used by companies, organizations, and investors to create strategies, as the finance sector rapidly moves towards data-driven optimization. This book provides an efficient resource, addressing all applications of data analytics in the finance sector. International experts from around the globe cover the most important subjects in finance, including data processing, knowledge management, machine learning models, data modeling, visualization, optimization for financial problems, financial econometrics, financial time series analysis, project management, and decision making. The authors provide empirical evidence as examples of specific topics. By combining both applications and theory, the book offers a holistic approach. Therefore, it is a must-read for researchers and scholars of financial economics and finance, as well as practitioners interested in a better understanding of financial data analytics. |
| data analytics vs project management: Project Management Essentials: Delivering Results on Time and Budget Dr. Bhaveshkumar J Parmar, 2023-10-04 Master the essentials of project management with this guide to delivering results on time and within budget. Covering key methodologies and best practices, this book is an invaluable resource for project managers aiming for successful project outcomes. |
| data analytics vs project management: Feature Engineering for Machine Learning and Data Analytics Guozhu Dong, Huan Liu, 2018-03-14 Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific features. The first chapter defines the concepts of features and feature engineering, offers an overview of the book, and provides pointers to topics not covered in this book. The next six chapters are devoted to feature engineering, including feature generation for specific data types. The subsequent four chapters cover generic approaches for feature engineering, namely feature selection, feature transformation based feature engineering, deep learning based feature engineering, and pattern based feature generation and engineering. The last three chapters discuss feature engineering for social bot detection, software management, and Twitter-based applications respectively. This book can be used as a reference for data analysts, big data scientists, data preprocessing workers, project managers, project developers, prediction modelers, professors, researchers, graduate students, and upper level undergraduate students. It can also be used as the primary text for courses on feature engineering, or as a supplement for courses on machine learning, data mining, and big data analytics. |
| data analytics vs project management: Data Analysis for Business, Economics, and Policy Gábor Békés, Gábor Kézdi, 2021-05-06 A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data. |
| data analytics vs project management: Project Management in the Digital Transformation Era Sergey Bushuyev, Ronggui Ding, Mladen Radujkovic, 2023-06-16 This book presents the proceedings of the 32nd World Congress of the International Project Management Association (IPMA). Digitalization is changing many fields of development and accelerating the global economic world. This challenge concerns project management as the driver of change. More than 1000 participants of the 32nd IPMA World Congress are an international community of the best experts and practitioners of project management. The Program Committee of the Congress includes more than 30 experts from Europe, Asia, America, and Australia, heads of large companies, and leading scientists and practitioners representing various areas of management, economics, and digital technology. The project management community discussed the challenges and prospects of the digital age, to find solutions to the problems that it poses to project management. The discussion took place in different formats – presentations, master classes, panel discussions, business games, and seminars that will be conducted by the world’s leading experts in the project management field. |
| data analytics vs project management: The Handbook of Project Management Martina Huemann, Rodney Turner, 2024-02-28 This practice-oriented handbook presents practitioners and students with a comprehensive overview of the essential knowledge and current best practices in project management. It includes the most up-to-date thinking in the discipline, describing recent developments in a way that practitioners can immediately use in their work. The Handbook of Project Management was the first “APM Body of Knowledge Approved” title for the Association for Project Management. Over the course of six editions, The Handbook of Project Management has become the definitive desk reference for project management practitioners. The team of expert contributors, selected to introduce the reader to the knowledge and skills required to manage projects, includes many of the most experienced and highly regarded international writers and practitioners. The book is divided into six parts: Projects; Performance; Process; People; Portfolio; and Perspectives. Including over 25 completely new chapters, this sixth edition provides a fully up-to-date encyclopaedia for the discipline and profession of project management. The book will be of use to all project management practitioners, from those starting out in the profession to people with advanced experience. It is also highly relevant to students, with earlier editions being used as a set or recommended text on Masters’ courses in project management. |
| data analytics vs project management: PROJECT MANAGEMENT NARAYAN CHANGDER, 2024-03-04 THE PROJECT MANAGEMENT MCQ (MULTIPLE CHOICE QUESTIONS) SERVES AS A VALUABLE RESOURCE FOR INDIVIDUALS AIMING TO DEEPEN THEIR UNDERSTANDING OF VARIOUS COMPETITIVE EXAMS, CLASS TESTS, QUIZ COMPETITIONS, AND SIMILAR ASSESSMENTS. WITH ITS EXTENSIVE COLLECTION OF MCQS, THIS BOOK EMPOWERS YOU TO ASSESS YOUR GRASP OF THE SUBJECT MATTER AND YOUR PROFICIENCY LEVEL. BY ENGAGING WITH THESE MULTIPLE-CHOICE QUESTIONS, YOU CAN IMPROVE YOUR KNOWLEDGE OF THE SUBJECT, IDENTIFY AREAS FOR IMPROVEMENT, AND LAY A SOLID FOUNDATION. DIVE INTO THE PROJECT MANAGEMENT MCQ TO EXPAND YOUR PROJECT MANAGEMENT KNOWLEDGE AND EXCEL IN QUIZ COMPETITIONS, ACADEMIC STUDIES, OR PROFESSIONAL ENDEAVORS. THE ANSWERS TO THE QUESTIONS ARE PROVIDED AT THE END OF EACH PAGE, MAKING IT EASY FOR PARTICIPANTS TO VERIFY THEIR ANSWERS AND PREPARE EFFECTIVELY. |
| data analytics vs project management: Big Data Analytics Kim H. Pries, Robert Dunnigan, 2015-02-05 With this book, managers and decision makers are given the tools to make more informed decisions about big data purchasing initiatives. Big Data Analytics: A Practical Guide for Managers not only supplies descriptions of common tools, but also surveys the various products and vendors that supply the big data market.Comparing and contrasting the dif |
| data analytics vs project management: Proceedings of the International Conference on Technology and Innovation Management (ICTIM 2022) Arnifa Asmawi, 2023-02-10 This is an open access book.The Centre for Knowledge and Innovation Management (CEKIM), Faculty of Management, Multimedia University is set to hold its second conference titled `International Conference on Technology and Innovation Management 2022 (ICTIM 2022)’ which carries the theme `Humanizing Innovation for Sustainability’. This conference will bring together academic researchers, industry players, policymakers and civil society leaders to engage and share the latest trends and development in technology and innovation management. |
| data analytics vs project management: Data Analytics Adedeji B. Badiru, 2020-12-22 Good data analytics is the basis for effective decisions. Whoever has the data, has the ability to extract information promptly and effectively to make pertinent decisions. The premise of this handbook is to empower users and tool developers with the appropriate collection of formulas and techniques for data analytics and to serve as a quick reference to keep pertinent formulas within fingertip reach of readers. This handbook includes formulas that will appeal to mathematically inclined readers. It discusses how to use data analytics to improve decision-making and is ideal for those new to using data analytics to show how to expand their usage horizon. It provides quantitative techniques for modeling pandemics, such as COVID-19. It also adds to the suite of mathematical tools for emerging technical areas. This handbook is a handy reference for researchers, practitioners, educators, and students in areas such as industrial engineering, production engineering, project management, civil engineering, mechanical engineering, technology management, and business management worldwide. |
| data analytics vs project management: An Introduction to Project Modeling and Planning Gündüz Ulusoy, Öncü Hazır, 2021-04-05 This textbook teaches the basic concepts and methods of project management but also explains how to convert them to useful results in practice. Project management offers a promising working area for theoretical and practical applications, and developing software and decision support systems (DSS). This book specifically focuses on project planning and control, with an emphasis on mathematical modeling. Models and algorithms establish a good starting point for students to study the relevant literature and support pursuing academic work in related fields. The book provides an introduction to theoretical concepts, and it also provides detailed explanations, application examples, and case studies that deal with real-life problems. The chapter topics include questions that underlie critical thinking, interpretation, analytics, and making comparisons. Learning outcomes are defined and the content of the book is structured following these goals. Chapter 1 begins by introducing the basic concepts, methods, and processes of project management. This Chapter constitutes the base for defining and modeling project management problems. Chapter 2 explores the fundamentals of organizing and managing projects from an organization’s perspective. Issues related to project team formation, the role of project managers, and organization types are discussed. Chapter 3 is devoted to project planning and network modeling of projects, covering fundamental concepts such as project scope, Work Breakdown Structure (WBS), Organizational Breakdown Structure (OBS), Cost Breakdown Structure (CBS), project network modeling, activity duration, and cost estimating, activity-based costing (ABC), data and knowledge management. Chapter 4 introduces deterministic scheduling models, which can be used in constructing the time schedules. Models employing time-based and finance-based objectives are introduced. The CPM is covered. The unconstrained version of maximizing Net Present Value (NPV) is also treated here together with the case of time-dependent cash flows. Chapter 5 focuses on the time/cost trade-off problem, explaining how to reduce the duration of some of the activities and therefore reduce the project duration at the expense of additional costs. This topic is addressed for both continuous and discrete cases. Chapter 6 discusses models and methods of scheduling under uncertain activity durations. PERT is introduced for minimizing the expected project duration and extended to the PERT-Costing method for minimizing the expected project cost. Simulation is presented as another approach for dealing with the uncertainty in activity durations and costs. To demonstrate the use of the PERT, a case study on constructing an earthquake-resistant residential house is presented. Classifications of resource and schedule types are given in Chapter 7, and exact and heuristic solution procedures for the single- and multi-mode resource constrained project scheduling problem (RCPSP) are presented. The objective of maximizing NPV under resource constraints is addressed, and the capital-constrained project scheduling model is introduced. In Chapter 8, resource leveling, and further resource management problems are introduced. Total adjustment cost and resource availability cost problems are introduced. Various exact models are investigated. A heuristic solution procedure for the resource leveling problem is presented in detail. Also, resource portfolio management policies and the resource portfolio management problem are discussed. A case study on resource leveling dealing with the annual audit project of a major corporation is presented. Project contract types and payment schedules constitute the topics of Chapter 9. Contracts are legal documents reflecting the results of some form of client-contractor negotiations and sometimes of a bidding process, which deserve closer attention. Identification and allocation of risk in contracts, project control issues, disputes, and resolution management are further topics covered in this Chapter. A bidding model is presented to investigate client-contractor negotiations and the bidding process from different aspects. Chapter 10 focuses on processes and methods for project monitoring and control. Earned Value Management is studied to measure the project performance throughout the life of a project and to estimate the expected project time and cost based on the current status of the project. How to incorporate inflation into the analysis is presented. In Chapter 11, qualitative and quantitative techniques including decision trees, simulation, and software applications are introduced. Risk phases are defined and building a risk register is addressed. An example risk breakdown structure is presented. The design of risk management processes is introduced, and risk response planning strategies are discussed. At the end of the Chapter, the quantitative risk analysis is demonstrated at the hand of a team discussion case study. Chapter 12 covers several models and approaches dealing with various stochastic aspects of the decision environment. Stochastic models, generation of robust schedules, use of reactive and fuzzy approaches are presented. Sensitivity and scenario analysis are introduced. Also, simulation analysis, which is widely used to analyze the impacts of uncertainty on project goals, is presented. Chapter 13 addresses repetitive projects that involve the production or construction of similar units in batches such as railway cars or residential houses. Particularly in the construction industry repetitive projects represent a large portion of the work accomplished in this sector of the economy. A case study on the 50 km section of a motorway project is used for demonstrating the handling of repetitive project management. How best to select one or more of a set of candidate projects to maintain a project portfolio is an important problem for project-based organizations with limited resources. The project selection problem is inherently a multi-objective problem and is treated as such in Chapter 14. Several models and solution techniques are introduced. A multi-objective, multi-period project selection and scheduling model is presented. A case study that addresses a project portfolio selection and scheduling problem for the construction of a set of dams in a region is presented. Finally, Chapter 15 discusses three promising research areas in project management in detail: (i) Sustainability and Project Management, (ii) Project Management in the Era of Big Data, and (iii) the Fourth Industrial Revolution and the New Age Project Management. We elaborate on the importance of sustainability in project management practices, discuss how developments in data analytics might impact project life cycle management, and speculate how the infinite possibilities of the Fourth Industrial Revolution and the new technologies will transform project management practices. |
| data analytics vs project management: Data Science Thinking Longbing Cao, 2018-08-17 This book explores answers to the fundamental questions driving the research, innovation and practices of the latest revolution in scientific, technological and economic development: how does data science transform existing science, technology, industry, economy, profession and education? How does one remain competitive in the data science field? What is responsible for shaping the mindset and skillset of data scientists? Data Science Thinking paints a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective, as data science thinking from the scientific-thinking perspective, as a trans-disciplinary science from the disciplinary perspective, and as a new profession and economy from the business perspective. |
| data analytics vs project management: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| data analytics vs project management: Computational Intelligence in Engineering and Project Management Pedro Yobanis Piñero Pérez, |
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| data analytics vs project management: How to Start a Cloud based Project Management Platform AS, How to Start a Business About the Book: Unlock the essential steps to launching and managing a successful business with How to Start a Business books. Part of the acclaimed How to Start a Business series, this volume provides tailored insights and expert advice specific to the industry, helping you navigate the unique challenges and seize the opportunities within this field. What You'll Learn Industry Insights: Understand the market, including key trends, consumer demands, and competitive dynamics. Learn how to conduct market research, analyze data, and identify emerging opportunities for growth that can set your business apart from the competition. Startup Essentials: Develop a comprehensive business plan that outlines your vision, mission, and strategic goals. Learn how to secure the necessary financing through loans, investors, or crowdfunding, and discover best practices for effectively setting up your operation, including choosing the right location, procuring equipment, and hiring a skilled team. Operational Strategies: Master the day-to-day management of your business by implementing efficient processes and systems. Learn techniques for inventory management, staff training, and customer service excellence. Discover effective marketing strategies to attract and retain customers, including digital marketing, social media engagement, and local advertising. Gain insights into financial management, including budgeting, cost control, and pricing strategies to optimize profitability and ensure long-term sustainability. Legal and Compliance: Navigate regulatory requirements and ensure compliance with industry laws through the ideas presented. Why Choose How to Start a Business books? Whether you're wondering how to start a business in the industry or looking to enhance your current operations, How to Start a Business books is your ultimate resource. This book equips you with the knowledge and tools to overcome challenges and achieve long-term success, making it an invaluable part of the How to Start a Business collection. Who Should Read This Book? Aspiring Entrepreneurs: Individuals looking to start their own business. This book offers step-by-step guidance from idea conception to the grand opening, providing the confidence and know-how to get started. Current Business Owners: Entrepreneurs seeking to refine their strategies and expand their presence in the sector. Gain new insights and innovative approaches to enhance your current operations and drive growth. Industry Professionals: Professionals wanting to deepen their understanding of trends and best practices in the business field. Stay ahead in your career by mastering the latest industry developments and operational techniques. Side Income Seekers: Individuals looking for the knowledge to make extra income through a business venture. Learn how to efficiently manage a part-time business that complements your primary source of income and leverages your skills and interests. Start Your Journey Today! Empower yourself with the insights and strategies needed to build and sustain a thriving business. Whether driven by passion or opportunity, How to Start a Business offers the roadmap to turning your entrepreneurial dreams into reality. Download your copy now and take the first step towards becoming a successful entrepreneur! Discover more titles in the How to Start a Business series: Explore our other volumes, each focusing on different fields, to gain comprehensive knowledge and succeed in your chosen industry. |
| data analytics vs project management: Contemporary Challenges for Agile Project Management Naidoo, Vannie, Verma, Rahul, 2021-11-05 Given the pace at which projects must be completed in an era of global hypercompetition and turbulence, examining the project management profession within the contexts of international trade and globalization is essential to encourage the highest level of efficiency and agility. Agile project management provides a flexible approach to managing projects as it allows a team to break large projects down into more manageable tasks that can be tackled in short iterations or sprints, thus enabling a team to adapt to change quickly and deliver work fast. Contemporary Challenges for Agile Project Management highlights the modern struggles that face businesses and leaders as they work to implement agile project management within their processes and try to gain a competitive edge through cross-functional team collaboration. Covering many underrepresented topics related to areas such as critical success factors, data science, and project leadership, this book is an essential resource for project leaders, managers, supervisors, business leaders, consultants, researchers, academicians, and students and educators of higher education. |
| data analytics vs project management: AI-Driven Project Management Kristian Bainey, 2024-04-02 Accelerate your next project with artificial intelligence and ChatGPT In AI-Driven Project Management: Harnessing the Power of Artificial Intelligence and ChatGPT to Achieve Peak Productivity and Success, veteran IT and project management advisor Kristian Bainey delivers an insightful collection of strategies for automating the administration and management of projects. In the book, the author focuses on four key areas where project leaders can achieve improved results with AI's data-centric capabilities: minimizing surprises, minimizing bias, increasing standards, and accelerating decision making. You'll also find: Primers on the role of AI and ChatGPT in Agile, Hybrid, and Predictive approaches to project management How to accurately forecast a project with ChatGPT Techniques for crafting impactful AI strategy using AI project management principles Perfect for managers, executives, and business leaders everywhere, AI-Driven Project Management is also a must-read for project management professionals, tech professionals and enthusiasts, and anyone else interested in the intersection of artificial intelligence, machine learning, and project management. |
| data analytics vs project management: Information Technology for Management: Emerging Research and Applications Ewa Ziemba, 2019-02-27 This book constitutes extended selected papers from the 16th Conference on Advanced Information Technologies for Management, AITM 2018, and the 13th Conference on Information Systems Management, ISM 2018, held as part of the Federated Conference on Computer Science and Information Systems, FedCSIS, which took place in Poznan, Poland, in September 2018. The total of 9 full and 3 short papers presented in this volume were carefully reviewed and selected from a total of 43 submissions. The papers selected to be included in this book contribute to the understanding of relevant trends of current research on information technology for management in business and public organizations. They were organized in topical sections named: information technology and systems for knowledge management, and information technology and systems for business transformation. |
| data analytics vs project management: Using Strategy Analytics for Business Value Creation and Competitive Advantage Kautish, Sandeep Kumar, 2024-07-26 In the field of strategic management and business intelligence, a formidable challenge is presentconventional decision-making processes, heavily reliant on internal and external reports, struggle to meet the demands of this data-driven era. As organizations grapple with the increasing influx of data, the imperative for a strategic shift becomes undeniably apparent. Using Strategy Analytics for Business Value Creation and Competitive Advantage helps to guide leaders in extracting value, structuring complex problems, and crafting robust business strategies. Scholars and industry experts alike will find within the pages of this comprehensive guide a roadmap to navigate the intersection of organizational strategy and analytics, ultimately unlocking the key to business brilliance. Using Strategy Analytics for Business Value Creation and Competitive Advantage stands as a testament to the commitment to addressing the prevailing challenges in strategic decision-making. Tailored for researchers, academicians, industry experts, and scholars, the book delves into the intricacies of strategy analytics, offering transformative insights for those seeking a competitive edge in the evolving business landscape. Capturing the essence of this exploration, the transformative potential of strategy analytics is encapsulated in this valuable resource. |
| data analytics vs project management: Project Management in Health and Community Services Zhanming Liang, Valerie Thiessen, Judith Dwyer, 2025-01-20 The new edition of this best-selling text presents the tools and techniques for effectively managing every kind of development and change in health and community services, while also balancing the needs of a range of stakeholders. It offers practical, problem-solving strategies based on real-life scenarios. A core competency for health and community service practitioners internationally, project management is a key challenge for both new and existing staff. This practitioner’s guide uses project stories and examples to illustrate the core challenges that practitioners may face, including managing the project life cycle, project planning, execution and evaluation, risk management, handling change and building effective teams. Alongside new interviews with staff working across a range of sectors, this edition includes new content on career development and pathways as well as the growing integration of project methods into general management, and the impact of broader changes like digital innovation and transformation. Written by highly experienced authors, and underpinned by the latest research, this enlightening and practical guide is an essential resource for anyone studying or working in health and community services. |
| data analytics vs project management: Data Analytics for Business: Leveraging Data for Strategic Insights Michael Roberts, In the modern business landscape, data is more valuable than ever. Data Analytics for Business: Leveraging Data for Strategic Insights is a comprehensive guide designed to help businesses harness the power of data analytics to drive decision-making, improve operations, and gain competitive advantage. This book covers the entire spectrum of data analytics, from foundational concepts to advanced techniques, with practical examples and real-world case studies. Whether you are a business leader, data professional, or aspiring analyst, this handbook equips you with the knowledge and skills to transform raw data into actionable insights that propel your organization forward. Embrace the future of business intelligence and unlock the full potential of data analytics. |
| data analytics vs project management: Evolving Toolbox for Complex Project Management Alex Gorod, Leonie Hallo, Vernon Ireland, Indra Gunawan, 2019-10-30 This book enhances learning about complex project management principles and practices through the introduction and discussion of a portfolio of tools presented as an evolving toolbox. Throughout the book, industry practitioners examine the toolsets that are part of the toolbox to develop a broader understanding of complex project management challenges and the available tools to address them. This approach establishes a dynamic, structured platform for a comprehensive analysis and assessment of the modern, rapidly changing, multifaceted business environment to teach the next generation of project managers to successfully cope with the ever increasing complexity of the 21st century. |
| data analytics vs project management: The AMA Handbook of Project Management Paul C. Dinsmore, Jeannette Cabanis-Brewin, 2018-11-13 This book is an essential resource that presents a state-of-the-art theory and process of project management. Packed with essays and insights from the field's top professionals,?this authoritative guide?is the resource professionals and students rely on for its practical guidance and big picture overview of the entire field: scheduling and budgeting, engaging stakeholders, measuring performance, managing multiple projects, resolving conflicts, using agile practices, and more. Whether you need advice keeping projects on track or help preparing for certification, this new edition explains every principle, process, and development. Revised to reflect the latest changes to A Guide to the Project Management Body of Knowledge?(PMBOK®),?the fifth edition includes new information on how to: Close the strategy-implementation gap Tap the power of digital transformation Navigate M&A environments Revise your methods for nonprofit settings Keep pace with your evolving role Filled with models, case studies, and in-depth solutions, The AMA Handbook of Project Management helps you master the discipline, overcome obstacles, and fast track your projects and career. |
| data analytics vs project management: Managing Data Science Kirill Dubovikov, 2019-11-12 Understand data science concepts and methodologies to manage and deliver top-notch solutions for your organization Key FeaturesLearn the basics of data science and explore its possibilities and limitationsManage data science projects and assemble teams effectively even in the most challenging situationsUnderstand management principles and approaches for data science projects to streamline the innovation processBook Description Data science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. Traditional approaches often fail as they don't entirely meet the conditions and requirements necessary for current data science projects. In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way. After understanding the practical applications of data science and artificial intelligence, you'll see how to incorporate them into your solutions. Next, you will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you'll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps. By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you'll encounter on a daily basis. What you will learnUnderstand the underlying problems of building a strong data science pipelineExplore the different tools for building and deploying data science solutionsHire, grow, and sustain a data science teamManage data science projects through all stages, from prototype to productionLearn how to use ModelOps to improve your data science pipelinesGet up to speed with the model testing techniques used in both development and production stagesWho this book is for This book is for data scientists, analysts, and program managers who want to use data science for business productivity by incorporating data science workflows efficiently. Some understanding of basic data science concepts will be useful to get the most out of this book. |
| data analytics vs project management: Project Management Fundamentals: Planning and Executing Projects Cybellium, 2024-09-01 Welcome to the forefront of knowledge with Cybellium, your trusted partner in mastering the cutting-edge fields of IT, Artificial Intelligence, Cyber Security, Business, Economics and Science. Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com |
| data analytics vs project management: Data Science and Business Intelligence for Corporate Decision-Making Dr. P. S. Aithal, 2024-02-09 About the Book: A comprehensive book plan on Data Science and Business Intelligence for Corporate Decision-Making with 15 chapters, each with several sections: Chapter 1: Introduction to Data Science and Business Intelligence Chapter 2: Foundations of Data Science Chapter 3: Business Intelligence Tools and Technologies Chapter 4: Data Visualization for Decision-Making Chapter 5: Machine Learning for Business Intelligence Chapter 6: Big Data Analytics Chapter 7: Data Ethics and Governance Chapter 8: Data-Driven Decision-Making Process Chapter 9: Business Intelligence in Marketing Chapter 10: Financial Analytics and Business Intelligence Chapter 11: Operational Excellence through Data Analytics Chapter 12: Human Resources and People Analytics Chapter 13: Case Studies in Data-Driven Decision-Making Chapter 14: Future Trends in Data Science and Business Intelligence Chapter 15: Implementing Data Science Strategies in Corporations Each chapter dives deep into the concepts, methods, and applications of data science and business intelligence, providing practical insights, real-world examples, and case studies for corporate decision-making processes. |
| data analytics vs project management: Social Media for Project Management Johan Ninan, 2022-11-17 The number of projects is increasing worldwide as traditional and repetitive tasks are carried out through automation. Projects being temporary and unique while being adopted globally across sectors presents a challenge for the effective management of environmental, economic, and social parameters. Projects are people centric and require the effective management of internal and external stakeholders. In the modern age, social media is seen as a tool that connects people across the world having significant implications on everyone’s daily life. Social media is used for different purposes and encompasses multiple affordances as these are often free and also bring together people from different walks of life who tend to use them differently. However, the role of social media in managing projects is still under explored. In this edited book, multiple authors working on the application of social media in projects come together to craft an agenda for the future. First, the use of social media for internal stakeholders, such as managers and engineers, are discussed. Following this, the use of social media for external stakeholders, such as communities and project affected persons are discussed. Finally, the guidelines for education using social media and research using social media is discussed. Thus, the book brings together multiple authors to discuss how social media can be used in project settings to facilitate interactions and strategic conversations across hierarchical levels and geographic boundaries for diverse goals. The book is a valuable resource for all project management academics, researchers and practitioners who are interested in learning about the application of social media in project settings. |
| data analytics vs project management: Be a Pmp Ace in 30 Days Roji Abraham, 2015-12-05 Do you want to earn the #1 Certification for Project Management globally? Are you in possession of numerous resources yet clueless on how you could organise yourself to be fully prepared to take on the PMP exam? Does the thought of attempting the PMP exam scare you? Roji Abraham, a successful project manager in a $4 Billion firm, a certified PMP, and an MBA graduate from one of Europe's best business schools, writes in his unique style about his 30 day journey to PMP certification and gives step-by-step guidance on how you could effectively utilise your time while preparing for the exam. 'Be a PMP ace in 30 days' isn't a full-fledged guide with a truckload of information on each section but rather, a companion book, that shows you, how in 30 days, you could use your resources effectively, and be ready for the PMP exam and succeed. That too, without having to take even a day off from work! Here's what you get from this book that will oversee your personal journey to PMP certification: 1. Guidance on the necessary tools and resources you need while preparing for the PMP exam and how to use them effectively. 2. Website links to a downloadable weekly calendar with suggested daily and hourly schedules for covering each topic and reviewing them effectively over 30 days 3. Website link to a print-friendly downloadable process chart 4. Key notes for each day that highlights the most important topics for that day. 5. Information on some great free/budget online resources 6. Useful tips for the exam day. 7. Five interviews with successful PMP candidates, from around the globe, with their suggestions on how to conquer the PMP exam. |
| data analytics vs project management: Agile Approaches for Successfully Managing and Executing Projects in the Fourth Industrial Revolution Bolat, Hür Bersam, Temur, Gül Tekin, 2019-03-15 Communication between man and machine is vital to completing projects in the current day and age. Without this constant connectiveness as we enter an era of big data, project completion will result in utter failure. Agile Approaches for Successfully Managing and Executing Projects in the Fourth Industrial Revolution addresses changes wrought by Industry 4.0 and its effects on project management as well as adaptations and adjustments that will need to be made within project life cycles and project risk management. Highlighting such topics as agile planning, cloud projects, and organization structure, it is designed for project managers, executive management, students, and academicians. |
The Role of Data Analytics in Project Management
This paper aims to explore the pivotal role of data analytics in enhancing project management outcomes. Specifically, it will examine the different types of data analytics and their diverse …
Delft University of Technology Data analytics in managing …
Project data are generated, collected and analysed across all stages of project management and project delivery. In this chapter, we will first conceptualise the relation between projects, …
201602 Project Management Analytics - PMI Sacramento
Dec 2, 2015 · WHY IS ANALYTICS IMPORTANT IN PROJECT MANAGEMENT? • Predictive information can help project managers (PMs) make better decisions and keep projects on …
Developing Project Data Analytics Skills - APM
Data and analytics provide opportunities for organisations to gain insights into their projects that were previously inaccessible. Using analytics tools, organisations can identify trends, …
Artificial Intelligence, Analytics and Agile: Transforming Project ...
Analytics holistically improve the project management discipline through continuous improvement and consistent execution. This paper builds upon this discussion and establishes how project …
16. Data management and data analysis* - epidemiolog
Data management: Strategies and issues in collecting, processing, documenting, and summarizing data for an epidemiologic study. 1. Data Management. Data management falls …
Project Analytics to Improve Project and Portfolio Decision …
There is a clear distinction between operationally managed projects and strategically managed projects. Operationally managed projects focus on getting the job done, while strategically …
Article DATA ANALYTICS IN PROJECT MANAGEMENT
Data analytics is one of the main drivers of change for project management, from project selection, scheduling, and resourcing to cost control and risk management. Data analytics …
Data Analysis & Knowledge Management Definitions and …
Data Analysis can drive strategic and tactical decisions, reveal patterns, and provide competitive advantage through the use of analytics. The four levels of data analytics are: Descriptive...
Data Analytics in Project Management - book review
book "Data Analytics in Project Management," edited by Seweryn Spalek, the crucial importance of identifying and leveraging the right data elements in project management is underscored. …
Data Analytics for Project Delivery: Unlocking the Potential of …
In recent years, there has been a growing interest in the potential of data analytics to enhance project delivery. Yet many argue that its application in projects is still lagging behind other …
Project Management Analytics - pearsoncmg.com
Analysis and analytics are similar-sounding terms, but they are not the same thing. They do have some differences. Both are important to project managers. They (project managers) can use …
Data Analytics in Project Management - آکادمی آقای صنایع
in project management include the integration of cost, schedule, and risk variables to enable probabilistic and predictive analytics. Werner Meyer, PhD, is a director at ProjectLink …
Project data analytics: the state of the art and science - APM
Project data analytics, at its simplest, is the use of past and current project data to enable effective decisions on project delivery. This includes: Descriptive analytics presenting data in …
Predictive Project Analytics (PPA) - Deloitte United States
PPA is an analytical project risk management capability that examines a project’s characteristics and assesses whether it has the appropriate level of oversight and governance :
Using Analytics to Predict Project Management Success
We used linear regression and classification models to predict projects’ performance and analyze the underlying factors causing project delay. Our predictive analysis used 131 variables which …
Big Data Analytics in Project Management: A Key to Success
Results indicate that big data analytics fosters improved project performance, more robust risk management, and heightened adaptability. However, challenges related to data quality, …
Getting started in project data - APM
Project data analytics is the use of past and current project data to enable effective decisions on project initiation, delivery and efficient automation of project tasks. This can be done in three …
Enabling Project Success through Analytics - Deloitte United …
• Based on project complexity, PPA’s Analytical Engine determines the level of control necessary to achieve success. • Flexible and scalable solution to supplement existing project …
Data Management, Analysis Tools, and Analysis Mechanics
This chapter discusses how to combine and manage data streams, and how to use data management tools to produce analytical results that are error free and reproducible, once …
The Role of Data Analytics in Project Management
This paper aims to explore the pivotal role of data analytics in enhancing project management outcomes. Specifically, it will examine the different types of data analytics and their diverse …
Delft University of Technology Data analytics in managing …
Project data are generated, collected and analysed across all stages of project management and project delivery. In this chapter, we will first conceptualise the relation between projects, …
201602 Project Management Analytics - PMI Sacramento
Dec 2, 2015 · WHY IS ANALYTICS IMPORTANT IN PROJECT MANAGEMENT? • Predictive information can help project managers (PMs) make better decisions and keep projects on …
Developing Project Data Analytics Skills - APM
Data and analytics provide opportunities for organisations to gain insights into their projects that were previously inaccessible. Using analytics tools, organisations can identify trends, …
Artificial Intelligence, Analytics and Agile: Transforming …
Analytics holistically improve the project management discipline through continuous improvement and consistent execution. This paper builds upon this discussion and establishes how project …
16. Data management and data analysis* - epidemiolog
Data management: Strategies and issues in collecting, processing, documenting, and summarizing data for an epidemiologic study. 1. Data Management. Data management falls …
Project Analytics to Improve Project and Portfolio Decision …
There is a clear distinction between operationally managed projects and strategically managed projects. Operationally managed projects focus on getting the job done, while strategically …
Article DATA ANALYTICS IN PROJECT MANAGEMENT
Data analytics is one of the main drivers of change for project management, from project selection, scheduling, and resourcing to cost control and risk management. Data analytics …
Data Analysis & Knowledge Management Definitions and …
Data Analysis can drive strategic and tactical decisions, reveal patterns, and provide competitive advantage through the use of analytics. The four levels of data analytics are: Descriptive...
Data Analytics in Project Management - book review
book "Data Analytics in Project Management," edited by Seweryn Spalek, the crucial importance of identifying and leveraging the right data elements in project management is underscored. …
Data Analytics for Project Delivery: Unlocking the Potential …
In recent years, there has been a growing interest in the potential of data analytics to enhance project delivery. Yet many argue that its application in projects is still lagging behind other …
Project Management Analytics - pearsoncmg.com
Analysis and analytics are similar-sounding terms, but they are not the same thing. They do have some differences. Both are important to project managers. They (project managers) can use …
Data Analytics in Project Management - آکادمی آقای صنایع
in project management include the integration of cost, schedule, and risk variables to enable probabilistic and predictive analytics. Werner Meyer, PhD, is a director at ProjectLink …
Project data analytics: the state of the art and science - APM
Project data analytics, at its simplest, is the use of past and current project data to enable effective decisions on project delivery. This includes: Descriptive analytics presenting data in …
Predictive Project Analytics (PPA) - Deloitte United States
PPA is an analytical project risk management capability that examines a project’s characteristics and assesses whether it has the appropriate level of oversight and governance :
Using Analytics to Predict Project Management Success
We used linear regression and classification models to predict projects’ performance and analyze the underlying factors causing project delay. Our predictive analysis used 131 variables which …
Big Data Analytics in Project Management: A Key to Success
Results indicate that big data analytics fosters improved project performance, more robust risk management, and heightened adaptability. However, challenges related to data quality, …
Getting started in project data - APM
Project data analytics is the use of past and current project data to enable effective decisions on project initiation, delivery and efficient automation of project tasks. This can be done in three …
Enabling Project Success through Analytics - Deloitte …
• Based on project complexity, PPA’s Analytical Engine determines the level of control necessary to achieve success. • Flexible and scalable solution to supplement existing project …
Data Management, Analysis Tools, and Analysis Mechanics
This chapter discusses how to combine and manage data streams, and how to use data management tools to produce analytical results that are error free and reproducible, once …
