Analytics In Project Management

  analytics in 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
  analytics in 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.
  analytics in project management: Project Management Essentials Adedeji B. Badiru, 2021-03-01 This Focus book presents the basic principles and practice of project management and simple analytics for project control, using the systems framework of Design, Evaluation, Justification, and Integration (DEJI). The overriding theme of the book is that every pursuit can be organized as a project. This short form book presents the evolution of products in the classical era of introducing new projects needing project management. It discusses the development of project alliances, includes the role of project management in advancing organization goals, illustrates the early applications of project management, and includes humans in the loop. The book will also cover project systems and work design, while showing the integration of quantitative and qualitative analytics. This book can serve as a reference for everyone, since everyone is engaged in project management, whether formal or informal
  analytics in 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
  analytics in project management: Data Analytics in Project Management Seweryn Spalek, 2018-10-25 This book aims to help the reader better understand the importance of data analysis in project management. Moreover, it provides guidance by showing tools, methods, techniques and lessons learned on how to better utilize the data gathered from the projects. First and foremost, insight into the bridge between data analytics and project management aids practitioners looking for ways to maximize the practical value of data procured. The book equips organizations with the know-how necessary to adapt to a changing workplace dynamic through key lessons learned from past ventures. The book’s integrated approach to investigating both fields enhances the value of research findings.
  analytics in 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.
  analytics in project management: Business Analysis, Requirements, and Project Management Karl Cox, 2021-10-26 IT projects emerge from a business need. In practice, software developers must accomplish two big things before an IT project can begin: find out what you need to do (i.e., analyse business requirements) and plan out how to do it (i.e., project management). The biggest problem in IT projects is delivering the wrong product because IT people do not understand what business people require. This practical textbook teaches computer science students how to manage and deliver IT projects by linking business and IT requirements with project management in an incremental and straightforward approach. Business Analysis, Requirements, and Project Management: A Guide for Computing Students presents an approach to analysis management that scales the business perspective. It takes a business process view of a business proposal as a model and explains how to structure a technical problem into a recognisable pattern with problem frames. It shows how to identify core transactions and model them as use cases to create a requirements table useful to designers and coders. Linked to the analysis are three management tools: the product breakdown structure (PBS), the Gantt chart, and the Kanban board. The PBS is derived in part from the problem frame. The Gantt chart emerges from the PBS and ensures the key requirements are addressed by reference to use cases. The Kanban board is especially useful in Task Driven Development, which the text covers. This textbook consists of two interleaving parts and features a single case study. Part one addresses the business and requirements perspective. The second integrates core project management approaches and explains how both requirements and management are connected. The remainder of the book is appendices, the first of which provides solutions to the exercises presented in each chapter. The second appendix puts together much of the documentation for the case study into one place. The case study presents a real-word business scenario to expose students to professional practice.
  analytics in 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.
  analytics in 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.
  analytics in 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.
  analytics in project management: The Business Analyst / Project Manager Robert K. Wysocki, 2010-08-20 A breakthrough game plan illustrating the need for better collaboration between Project Managers and Business Analysts In The Business Analyst/Project Manager, author Robert Wysocki draws on his forty-five years of professional experience as a PM/BA to shed light on the similarities and differences of the roles and responsibilities of these two positions, the need for greater collaboration, and how to staff a project with one or both of these professionals. Examines the boundaries and interactions between the BA and the PM Looks at how to identify the skill sets needed to make the project a success The typical relationship of the BA and PM across the project management life cycle Making the best configuration of leadership assignments based on project characteristics Where the responsibilities of the BA leave off and the PM's begins and where the two have collaborative responsibilities How to use a PM/BA to enhance project performance How to foster a dual career path for PM/BAs development The in-depth discussion of the synergies between the two roles and the advantages of a combined PM/BA makes The Business Analyst/Project Manager a valuable contribution in your ability to be successful on the complex projects of the 21st century.
  analytics in project management: The PMI Guide to Business Analysis , 2017-12-22 The Standard for Business Analysis – First Edition is a new PMI foundational standard, developed as a basis for business analysis for portfolio, program, and project management. This standard illustrates how project management processes and business analysis processes are complementary activities, where the primary focus of project management processes is the project and the primary focus of business analysis processes is the product. This is a process-based standard, aligned with A Guide to the Project Management Body of Knowledge (PMBOK® Guide) – Sixth Edition, and to be used as a standard framework contributing to the business analysis body of knowledge.
  analytics in project management: Lean Project Management Philip Small, 2022-02-20 This Is How The World's Top Tech Companies Manage Their Projects - Use Their Methods And Let Your Startup Thrive! This book includes: Lean Startup, Lean Enterprise, Lean Analytics, Agile Project Management, Lean Six Sigma, Kaizen Do you want to run your small business using the same strategies as the leaders in your field? Do you want to have a clear advantage over your competitors? Do you want your customers to be happy and eager to pay you even more? It's time to learn Lean. With Lean Project Management, you can create highquality products in less time. You can manage projects in a way that actually empowers and motivates your employees. Last but not least, your customers will LOVE working with you if your company uses Lean and Agile methods. This book will show you how to implement Lean methods in your startup and take it to the next level. With this book, you will: Learn the step-by-step process of managing Lean projects Maximize your team's productivity with Scrum Visualize your workflows with Kanban Understand Lean Six Sigma roles and management boards Explore The 5S system - pros and cons Use Lean Analytics to measure the things that matter Adopt the Kaizen mindset to encourage growth and positive change Grow and scale your thriving business! The Lean mindset is your key to maximum productivity and genuine leadership. It's your key to innovation and success (and making more money in the process). You can use it to manage everything from your personal projects to a thriving corporation - Lean is scalable, flexible, and empowering. In fact, Lean Project Management can be used in all fields and industries - so dive in and transform your business now! Get your hands on this book before your competitors do. Get Your Copy Now!
  analytics in project management: Information Systems Project Management David L. Olson, 2014-12-19 Information Systems Project Management addresses project management in the context of information systems. It deals with general project management principles, with focus on the special characteristics of information systems. It is based on an earlier text, but shortened to focus on essential project management elements.This updated version presents various statistics indicating endemic problems in completing information system projects on time, within budget, at designed functionality. While successful completion of an information systems project is a challenge, there are some things that can be done to improve the probability of project success. This book reviews a number of project management tools, including, developing organizational ability to work on projects, better systems analysis and design, project estimation, and project control and termination.
  analytics in project management: Quantitative Methods in Project Management John C. Goodpasture, 2003-12-15 Quantitative Methods for the Project Manager is for professional project managers who need to know how to make everyday use of numerical analysis. It combines theory and practices and is designed to be easily applied.
  analytics in project management: Project Management, Planning and Control Albert Lester, 2007 This fifth edition provides a comprehensive resource for project managers. It describes the latest project management systems that use critical path methods.
  analytics in project management: Organizational Project Management Rosemary Hossenlopp PMP, 2010-06-01 Improve Your Business Results Through Organizational Project Management Organizational project management (OPM) aligns project deliverables with strategy. Understanding this emerging process is essential for all stakeholders, from the corporate sponsor to project team members. OPM is a valuable new tool that can enhance your organization's successful execution of projects in alignment with strategic priorities. Under the editorship of Rosemary Hossenlopp, PMP, ten contributors from around the globe, representing a wide variety of industries, offer valuable insights on how OPM can give any organization the competitive edge. They discuss how to • Improve business outcomes • Better align project work with strategies • Set priorities • Organize project work Whether you direct projects, fund projects, or conduct project work, Organizational Project Management: Linking Strategy and Projects is vital to your understanding of this emerging business discipline.
  analytics in 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.
  analytics in project management: Managing Change in Organizations Project Management Institute, 2013-08-01 Managing Change in Organizations: A Practice Guide is unique in that it integrates two traditionally disparate world views on managing change: organizational development/human resources and portfolio/program/project management. By bringing these together, professionals from both worlds can use project management approaches to effectively create and manage change. This practice guide begins by providing the reader with a framework for creating organizational agility and judging change readiness.
  analytics in project management: Risk and Decision Analysis in Projects John R. Schuyler, 2018-08-21 Decision analysis (DA) guides executives toward logical, consistent decisions under uncertainty. This book instructs readers in applying DA to feasibility analysis, project estimation, and project risk management.This is a wholly rewritten and expanded successor to the best-selling first and second editions.The entire investment lifecycle is covered, from conception, to the project plan, to the post-project review, and to a look-back analysis of the capital investment decision.DA applies to all manner of project management (PM) decisions for individuals, government, and non-profit organizations. The book uses a business investment perspective and assumes that maximizing value for the project owner is the objective.DA is a problem-solving process. There are four key features: 1) probabilities and probability distributions express best judgments about risks and uncertainties. 2) The organization has a decision policy expressed as a single metric (the objective function). 3) Probabilities and outcome values combine in the probability-weighting expected value calculation. 4) The organization as a policy to choose the best expected value alternative.This book aims to make decision making clear, simple, and logical. A clear decision policy can be elusive, and the author offers suggestions for making trade-offs among conflicting objectives. Converting the three pillars of project management (cost, schedule, and performance) into project value equivalents makes the trade-offs clear.This book is intended for serious PM students and practitioners. This is an essential concepts and how-to book. The scope is quantitative analysis, from project inception to post-project review. Project cost and schedule modeling, in modest detail, is essential to feasibility analysis and risk management. A general background in PM and corporate planning will be helpful. The methods are quantitative and straightforward. The reader should be comfortable with basic algebra and Microsoft(r) Excel(r).The book has eight pages of Suggested Reading annotated references (plus footnote additions), over 250 figures, approximately 600 Glossary definitions, and over 2400 Index entries. Online supplements include several whitepapers and other documents, example calculation spreadsheets, detailed color images of several important figures, four videos (including a critical chain simulation), and the Utility Elicitation Program (a web app, free for most users).Key topics include: Decision trees and Monte Carlo simulation for calculating outcome distributions and expected values * Probability concepts, including Bayes' rule for value of information analysis * Popular probability distribution types and when they apply * Eliciting expert judgments, with attention to potential cognitive and motivational biases * Recognizing the three pillars project in terms of project value * A 10-step decision analysis process * Project modeling concepts and techniques, with special attention to risk drivers and other correlations * Deterministic and stochastic sensitivity analysis * Decision policy that distinguishes objectives, time value, and risk attitude * @RISK(r) with Microsoft(r) Project for project simulations under uncertainty * Logical, consistent risk policy expressed as a utility function * Merge bias when task chains converge at a merge point * Tail estimate bias when estimating highly uncertain quantities * Optimizer's curse, a portfolio forecasting bias * Winner's curse, a bias characteristic of auctions * Using the best of critical chain and Monte Carlo simulation * Stochastic variance between a deterministic and a stochastic model * Modeling risk and uncertainty using probabilities, probability distributions, explicit formula relationships, correlation coefficients, risk drivers, conditional branching, and rework cycles.
  analytics in project management: Agile Data Warehousing Project Management Ralph Hughes, 2012-12-28 You have to make sense of enormous amounts of data, and while the notion of agile data warehousing might sound tricky, it can yield as much as a 3-to-1 speed advantage while cutting project costs in half. Bring this highly effective technique to your organization with the wisdom of agile data warehousing expert Ralph Hughes. Agile Data Warehousing Project Management will give you a thorough introduction to the method as you would practice it in the project room to build a serious data mart. Regardless of where you are today, this step-by-step implementation guide will prepare you to join or even lead a team in visualizing, building, and validating a single component to an enterprise data warehouse. - Provides a thorough grounding on the mechanics of Scrum as well as practical advice on keeping your team on track - Includes strategies for getting accurate and actionable requirements from a team's business partner - Revolutionary estimating techniques that make forecasting labor far more understandable and accurate - Demonstrates a blends of Agile methods to simplify team management and synchronize inputs across IT specialties - Enables you and your teams to start simple and progress steadily to world-class performance levels
  analytics in project management: Project Management Analytics Harjit Singh, 2016
  analytics in project management: Mechanics of Project Management Adedeji B. Badiru, S. Abidemi Badiru, I. Adetokunboh Badiru, 2018-10-25 Every organizational endeavor is based on project management. Projects range from simple to complex, with a definite beginning and a definite end. In manufacturing, as an example, the production of each unit of a product is defined as a project. The lifecycle goes from raw material to the product delivery stage, with steps in between managed as a rigorous project. This book covers the mechanics of project management and offers the requirements for executing a project using a systems-engineering framework and the project management body of knowledge, as advocated by the Project Management Institute. It includes the nuts and bolts for untangling the knots that often exist in project execution. Features Offers a unique guide to management projects, both big and small, in all spheres of human endeavor Presents the nuts and bolts of untangling the typical knots in project execution in a step-by-step format Applies to all types of projects, including technical, manufacturing, financial, science, engineering, and personal projects Provides a structured guide to the application of project management techniques Uses the Project Management Body of Knowledge (PMBOK) framework from the Project Management Institute (PMI) as the platform for the topics covered, coupled with a systems view Addresses technical and managerial aspects of projects in every industry
  analytics in project management: Managerial Analytics Michael Watson, Derek Nelson, 2014 Analytics and Big Data Demystified, The up-to-the-minute introduction for every manager, Everything you need to know to get results!, Concepts, applications, tools, techniques, and pitfalls to avoid, How to derive more value from tools and data you already own, Want to start leveraging analytics and Big Data for profit? Managerial Analytics is your ideal first resource. Whatever your industry or management role, this up-to-date guide will help you get started fast, get started right, and quickly start driving value. Book jacket.
  analytics in project management: Fundamentals of Project Management James P. Lewis, 2002 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.
  analytics in project management: Business Analytics with Management Science Models and Methods Arben Asllani, 2015 This book is about prescriptive analytics. It provides business practitioners and students with a selected set of management science and optimization techniques and discusses the fundamental concepts, methods, and models needed to understand and implement these techniques in the era of Big Data. A large number of management science models exist in the body of literature today. These models include optimization techniques or heuristics, static or dynamic programming, and deterministic or stochastic modeling. The topics selected in this book, mathematical programming and simulation modeling, are believed to be among the most popular management science tools, as they can be used to solve a majority of business optimization problems. Over the years, these techniques have become the weapon of choice for decision makers and practitioners when dealing with complex business systems.
  analytics in 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
  analytics in project management: Solving for Project Risk Management: Understanding the Critical Role of Uncertainty in Project Management Christian B. Smart, 2020-11-23 Risk is real—but you can manage it with this hard-hitting guide to reducing risk on any project, in any industry All projects, large and small, are subject to various risks. But the failure to manage inherent risk with diligence and know-how can lead to devastating consequences for an organization. In this comprehensive hands-on guide, a renowned expert in the field provides everything organizations need to conduct project risk management the right way. Why do so many projects come in over schedule and over budget? How do projected expenditures and schedules line up with reality? How can you accurately assess risk to mitigate financial disaster? Through a methodical, statistics-based approach, Christian B. Smart reveals: The enduring problem of cost and schedule growth How rigorous project risk management can reduce the impact of uncertainty The systematic tendency to underestimate risk—and how to avoid it Ways to accurately assess confidence levels in project risk management The need for proper risk management at the portfolio level The author lays out common problems and explains how to effectively solve them. And while he employs a wealth of illustrative charts, graphs, and statistics, he presents the material in an accessible style, and peppers the text with powerful personal anecdotes. Ideal for project managers, business analysts, and senior decision makers in both the public and private sectors, Solving for Project Risk Management offers everything you need to ensure your projects run smoothly, on budget, and deliver the expected outcomes.
  analytics in project management: Business Analytics for Managers Gert Laursen, Jesper Thorlund, 2010-07-13 While business analytics sounds like a complex subject, this book provides a clear and non-intimidating overview of the topic. Following its advice will ensure that your organization knows the analytics it needs to succeed, and uses them in the service of key strategies and business processes. You too can go beyond reporting!—Thomas H. Davenport, President's Distinguished Professor of IT and Management, Babson College; coauthor, Analytics at Work: Smarter Decisions, Better Results Deliver the right decision support to the right people at the right time Filled with examples and forward-thinking guidance from renowned BA leaders Gert Laursen and Jesper Thorlund, Business Analytics for Managers offers powerful techniques for making increasingly advanced use of information in order to survive any market conditions. Take a look inside and find: Proven guidance on developing an information strategy Tips for supporting your company's ability to innovate in the future by using analytics Practical insights for planning and implementing BA How to use information as a strategic asset Why BA is the next stepping-stone for companies in the information age today Discussion on BA's ever-increasing role Improve your business's decision making. Align your business processes with your business's objectives. Drive your company into a prosperous future. Taking BA from buzzword to enormous value-maker, Business Analytics for Managers helps you do it all with workable solutions that will add tremendous value to your business.
  analytics in project management: Handbook of Research on Project Management Strategies and Tools for Organizational Success Moreno-Monsalve, Nelson Antonio, Diez-Silva, H. Mauricio, Diaz-Piraquive, Flor Nancy, Perez-Uribe, Rafael Ignacio, 2020-01-17 Project management tools can be used as an alternative to improve and strengthen a company’s position in the market. However, the management of projects has been in constant transformation. Elements such as time, cost, and scope, on which it is based, have been complemented with other trends, such as the project team, change management, knowledge management, good negotiation practices, management of stakeholders, sustainability, etc. In order to improve the competitiveness of their company and increase earned value, managers must remain up to date on these latest transformations and best practices. The Handbook of Research on Project Management Strategies and Tools for Organizational Success is a pivotal reference source that analyzes and disseminates new trends that will allow managers to improve their skills and strengthen the performance of their companies through obtaining better results in the projects undertaken. While highlighting topics such as market growth, risk management, and value creation, this book is ideally designed for project managers, managers, business professionals, entrepreneurs, academicians, researchers, and students seeking current research on improving the competitiveness of companies as well as increasing their earned value.
  analytics in project management: Project Management with Dynamic Scheduling Mario Vanhoucke, 2013-11-29 The topic of this book is known as dynamic scheduling, and is used to refer to three dimensions of project management and scheduling: the construction of a baseline schedule and the analysis of a project schedule’s risk as preparation of the project control phase during project progress. This dynamic scheduling point of view implicitly assumes that the usability of a project’s baseline schedule is rather limited and only acts as a point of reference in the project life cycle. Consequently, a project schedule should especially be considered as nothing more than a predictive model that can be used for resource efficiency calculations, time and cost risk analyses, project tracking and performance measurement, and so on. In this book, the three dimensions of dynamic scheduling are highlighted in detail and are based on and inspired by a combination of academic research studies at Ghent University (www.ugent.be), in-company trainings at Vlerick Business School (www.vlerick.com) and consultancy projects at OR-AS (www.or-as.be). First, the construction of a project baseline schedule is a central theme throughout the various chapters of the book, and is discussed from a complexity point of view with and without the presence of project resources. Second, the creation of an awareness of the weak parts in a baseline schedule is discussed at the end of the two baseline scheduling parts as schedule risk analysis techniques that can be applied on top of the baseline schedule. Third, the baseline schedule and its risk analyses can be used as guidelines during the project control step where actual deviations can be corrected within the margins of the project’s time and cost reserves. The second edition of this book has seen corrections, additions and amendments in detail throughout the book. Moreover Chapter 15 on Dynamic Scheduling with ProTrack has been completely rewritten and extended with a section on ProTrack as a research tool.
  analytics in project management: The Analytics Lifecycle Toolkit Gregory S. Nelson, 2018-03-07 An evidence-based organizational framework for exceptional analytics team results The Analytics Lifecycle Toolkit provides managers with a practical manual for integrating data management and analytic technologies into their organization. Author Gregory Nelson has encountered hundreds of unique perspectives on analytics optimization from across industries; over the years, successful strategies have proven to share certain practices, skillsets, expertise, and structural traits. In this book, he details the concepts, people and processes that contribute to exemplary results, and shares an organizational framework for analytics team functions and roles. By merging analytic culture with data and technology strategies, this framework creates understanding for analytics leaders and a toolbox for practitioners. Focused on team effectiveness and the design thinking surrounding product creation, the framework is illustrated by real-world case studies to show how effective analytics team leadership works on the ground. Tools and templates include best practices for process improvement, workforce enablement, and leadership support, while guidance includes both conceptual discussion of the analytics life cycle and detailed process descriptions. Readers will be equipped to: Master fundamental concepts and practices of the analytics life cycle Understand the knowledge domains and best practices for each stage Delve into the details of analytical team processes and process optimization Utilize a robust toolkit designed to support analytic team effectiveness The analytics life cycle includes a diverse set of considerations involving the people, processes, culture, data, and technology, and managers needing stellar analytics performance must understand their unique role in the process of winnowing the big picture down to meaningful action. The Analytics Lifecycle Toolkit provides expert perspective and much-needed insight to managers, while providing practitioners with a new set of tools for optimizing results.
  analytics in project management: The AMA Handbook of Project Management Paul C. Dinsmore, Jeannette Cabanis-Brewin, 2014-06-12 A must-read for any project management professional or student. Projects are the life blood of any organization. Revised to reflect the latest changes to A Guide to the Project Management Body of Knowledge (PMBOK(R)) and the Project Management Professional Exam(R), the fourth edition of The AMA Handbook of Project Management provides readers with a clear overview of a complex discipline. Covering everything from individual projects to programs and strategic alignment, it addresses: Project initiation and planning Communication and interpersonal skills Scheduling, budgeting and meeting business objectives Managing political and resource issues Implementing a PMO Measuring value and competencies. The book compiles essays and advice from the field's top professionals and features new chapters on stakeholder management, agile project management, program management, project governance, knowledge management, and more. Updated with fresh examples, case studies and solutions to specific project management dilemmas, it remains an essential reference to the critical concepts and theories all project managers must master.
  analytics in project management: Agile Practice Guide , 2017-09-06 Agile Practice Guide – First Edition has been developed as a resource to understand, evaluate, and use agile and hybrid agile approaches. This practice guide provides guidance on when, where, and how to apply agile approaches and provides practical tools for practitioners and organizations wanting to increase agility. This practice guide is aligned with other PMI standards, including A Guide to the Project Management Body of Knowledge (PMBOK® Guide) – Sixth Edition, and was developed as the result of collaboration between the Project Management Institute and the Agile Alliance.
  analytics in project management: Business Analysis for Practitioners Project Management Institute, 2015-01-01 Recent research has shown that organizations continue to experience project issues associated with the poor performance of requirements-related activities a core task for the practice of business analysis. In fact, poor requirements practices are often cited as a leading cause of project failure in PMI's Pulse of the Profession surveys. Business Analysis for Practitioners: A Practice Guide provides practical resources to tackle the project-related issues associated with requirements and business analysis and addresses a critical need in the industry for more guidance in this area.
  analytics in project management: Managing Your Data Science Projects Robert de Graaf, 2019-06-07 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
  analytics in project management: Measuring Performance and Benchmarking Project Management at the Department of Energy National Research Council, Division on Engineering and Physical Sciences, Board on Infrastructure and the Constructed Environment, Committee for Oversight and Assessment of U.S. Department of Energy Project Management, 2005-08-01 In 1997, Congress, in the conference report, H.R. 105-271, to the FY1998 Energy and Water Development Appropriation Bill, directed the National Research Council (NRC) to carry out a series of assessments of project management at the Department of Energy (DOE). The final report in that series noted that DOE lacked an objective set of measures for assessing project management quality. The department set up a committee to develop performance measures and benchmarking procedures and asked the NRC for assistance in this effort. This report presents information and guidance for use as a first step toward development of a viable methodology to suit DOE's needs. It provides a number of possible performance measures, an analysis of the benchmarking process, and a description ways to implement the measures and benchmarking process.
  analytics in project management: The Oxford Handbook of Project Management Peter W. G. Morris, Jeffrey K. Pinto, Jonas Söderlund, 2012-07-19 The Oxford Handbook of Project Management presents and discusses leading ideas in the management of projects. Positioning project management as a domain much broader and more strategic than simply 'execution management', this Handbook draws on the insights of over 40 scholars to chart the development of the subject over the last 50 years or more as an area of increasing practical and academic interest. It suggests we could be entering an emerging 'third wave' of analysis and interpretation following its early technical and operational beginnings and the subsequent shift to a focus on projects and their management. Topics dealt with include: the historical evolution of the subject; its theoretical base; professionalism; business and societal context; strategy; organization; governance; innovation; overruns; risk; information management; procurement; relationships and trust; knowledge management; practice and teams. This handbook is of particular relevance to those interested in the research issues underlying project management.
  analytics in project management: AI and the Project Manager Peter Taylor, 2021-10-27 Enabling project managers to adapt to the new technology of artificial intelligence, this first comprehensive book on the topic discusses how AI will reinvent the project world and allow project managers to focus on people. Studies show that by 2030, 80 percent of project management tasks, such as data collection, reporting, and predictive analysis, will be carried out by AI in a consistent and efficient manner. This book sets out to explore what this will mean for project managers around the world and equips them to embrace this technological advantage for greater project success. Filled with insights and examples from tech providers and project experts, this book is an invaluable resource for PMO leaders, change executives, project managers, programme managers, and portfolio managers. Anyone who is part of the global community of change and project leadership needs to accept and understand the fast- approaching AI technology, and this book shows how to use it to their advantage.
  analytics in project management: Project Management in Libraries Carly Wiggins Searcy, 2018-09-06 A recent study showed that only 53 percent of projects come in on budget and only 49 percent on schedule. So what does it take to be an on-budget, on-time finisher? Successful project management may seem like a quixotic pursuit, but it doesn't have to be.
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