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Book Data Analytics in Project Management

Download or read book Data Analytics in Project Management written by Seweryn Spalek and published by Taylor & Francis Group/CRC Press. This book was released on 2019-01-01 with total page 11 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Book Project Management Analytics

Download or read book Project Management Analytics written by Harjit Singh and published by FT Press. This book was released on 2015-11-12 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Book The Data Driven Project Manager

Download or read book The Data Driven Project Manager written by Mario Vanhoucke and published by Apress. This book was released on 2018-03-27 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Book Data Analytics for Engineering and Construction Project Risk Management

Download or read book Data Analytics for Engineering and Construction Project Risk Management written by Ivan Damnjanovic and published by Springer. This book was released on 2019-05-23 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Book Data Analytics Initiatives

Download or read book Data Analytics Initiatives written by Ondřej Bothe and published by CRC Press. This book was released on 2022-04-20 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Book Aligning Business Strategies and Analytics

Download or read book Aligning Business Strategies and Analytics written by Murugan Anandarajan and published by Springer. This book was released on 2018-09-27 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Book Big Data Analytics

    Book Details:
  • Author : Kim H. Pries
  • Publisher : CRC Press
  • Release : 2015-02-05
  • ISBN : 1482234521
  • Pages : 576 pages

Download or read book Big Data Analytics written by Kim H. Pries and published by CRC Press. This book was released on 2015-02-05 with total page 576 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Book Managing Your Data Science Projects

Download or read book Managing Your Data Science Projects written by Robert de Graaf and published by Apress. This book was released on 2019-06-07 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: At first glance, the skills required to work in the data science field appear to be self-explanatory. Do not be fooled. Impactful data science demands an interdisciplinary knowledge of business philosophy, project management, salesmanship, presentation, and more. In Managing Your Data Science Projects, author Robert de Graaf explores important concepts that are frequently overlooked in much of the instructional literature that is available to data scientists new to the field. If your completed models are to be used and maintained most effectively, you must be able to present and sell them within your organization in a compelling way. The value of data science within an organization cannot be overstated. Thus, it is vital that strategies and communication between teams are dexterously managed. Three main ways that data science strategy is used in a company is to research its customers, assess risk analytics, and log operational measurements. These all require different managerial instincts, backgrounds, and experiences, and de Graaf cogently breaks down the unique reasons behind each. They must align seamlessly to eventually be adopted as dynamic models. Data science is a relatively new discipline, and as such, internal processes for it are not as well-developed within an operational business as others. With Managing Your Data Science Projects, you will learn how to create products that solve important problems for your customers and ensure that the initial success is sustained throughout the product’s intended life. Your users will trust you and your models, and most importantly, you will be a more well-rounded and effectual data scientist throughout your career. Who This Book Is For Early-career data scientists, managers of data scientists, and those interested in entering the field of data science

Book Project Management  Planning and Control

Download or read book Project Management Planning and Control written by Albert Lester and published by Butterworth-Heinemann. This book was released on 2007 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: This fifth edition provides a comprehensive resource for project managers. It describes the latest project management systems that use critical path methods.

Book Agile Analytics

Download or read book Agile Analytics written by Ken Collier and published by Addison-Wesley. This book was released on 2012 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Book Analytics

    Book Details:
  • Author : Phil Simon
  • Publisher : John Wiley & Sons
  • Release : 2017-07-03
  • ISBN : 1119424208
  • Pages : 304 pages

Download or read book Analytics written by Phil Simon and published by John Wiley & Sons. This book was released on 2017-07-03 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Book Data Analysis for Business  Economics  and Policy

Download or read book Data Analysis for Business Economics and Policy written by Gábor Békés and published by Cambridge University Press. This book was released on 2021-05-06 with total page 741 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data.

Book Fundamentals of Project Management

Download or read book Fundamentals of Project Management written by James P. Lewis and published by Amacom. This book was released on 2002 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: Updated concepts and tools to set up project plans, schedule work, monitor progress-and consistently achieve desired project results.In today's time-based and cost-conscious global business environment, tight project deadlines and stringent expectations are the norm. This classic book provides businesspeople with an excellent introduction to project management, supplying sound, basic information (along with updated tools and techniques) to understand and master the complexities and nuances of project management. Clear and down-to-earth, this step-by-step guide explains how to effectively spearhead every stage of a project-from developing the goals and objectives to managing the project team-and make project management work in any company. This updated second edition includes: * New material on the Project Management Body of Knowledge (PMBOK) * Do's and don'ts of implementing scheduling software* Coverage of the PMP certification offered by the Project Management Institute* Updated information on developing problem statements and mission statements* Techniques for implementing today's project management technologies in any organization-in any industry.

Book Financial Data Analytics

Download or read book Financial Data Analytics written by Sinem Derindere Köseoğlu and published by Springer Nature. This book was released on 2022-04-25 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: ​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.

Book The Perspective of Women Project Management Professionals

Download or read book The Perspective of Women Project Management Professionals written by Ipek Sahra Ozguler and published by . This book was released on 2020-03-19 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The Perspective of Women Project Management Professionals" is a book consisted of interviews with distinguished women project management professionals.

Book Data Analytics and AI

Download or read book Data Analytics and AI written by Jay Liebowitz and published by CRC Press. This book was released on 2020-08-06 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: Analytics and artificial intelligence (AI), what are they good for? The bandwagon keeps answering, absolutely everything! Analytics and artificial intelligence have captured the attention of everyone from top executives to the person in the street. While these disciplines have a relatively long history, within the last ten or so years they have exploded into corporate business and public consciousness. Organizations have rushed to embrace data-driven decision making. Companies everywhere are turning out products boasting that "artificial intelligence is included." We are indeed living in exciting times. The question we need to ask is, do we really know how to get business value from these exciting tools? Unfortunately, both the analytics and AI communities have not done a great job in collaborating and communicating with each other to build the necessary synergies. This book bridges the gap between these two critical fields. The book begins by explaining the commonalities and differences in the fields of data science, artificial intelligence, and autonomy by giving a historical perspective for each of these fields, followed by exploration of common technologies and current trends in each field. The book also readers introduces to applications of deep learning in industry with an overview of deep learning and its key architectures, as well as a survey and discussion of the main applications of deep learning. The book also presents case studies to illustrate applications of AI and analytics. These include a case study from the healthcare industry and an investigation of a digital transformation enabled by AI and analytics transforming a product-oriented company into one delivering solutions and services. The book concludes with a proposed AI-informed data analytics life cycle to be applied to unstructured data.

Book Foundations for Architecting Data Solutions

Download or read book Foundations for Architecting Data Solutions written by Ted Malaska and published by "O'Reilly Media, Inc.". This book was released on 2018-08-29 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: While many companies ponder implementation details such as distributed processing engines and algorithms for data analysis, this practical book takes a much wider view of big data development, starting with initial planning and moving diligently toward execution. Authors Ted Malaska and Jonathan Seidman guide you through the major components necessary to start, architect, and develop successful big data projects. Everyone from CIOs and COOs to lead architects and developers will explore a variety of big data architectures and applications, from massive data pipelines to web-scale applications. Each chapter addresses a piece of the software development life cycle and identifies patterns to maximize long-term success throughout the life of your project. Start the planning process by considering the key data project types Use guidelines to evaluate and select data management solutions Reduce risk related to technology, your team, and vague requirements Explore system interface design using APIs, REST, and pub/sub systems Choose the right distributed storage system for your big data system Plan and implement metadata collections for your data architecture Use data pipelines to ensure data integrity from source to final storage Evaluate the attributes of various engines for processing the data you collect