Data Analytics In Project Management
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Data Analytics In Project Management
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Author : Seweryn Spalek
language : en
Publisher: Taylor & Francis Group/CRC Press
Release Date : 2019-01-01
Data Analytics In Project Management written by Seweryn Spalek and has been published by Taylor & Francis Group/CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-01 with Business & Economics categories.
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.
Project Management Analytics
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Author : Harjit Singh
language : en
Publisher: FT Press
Release Date : 2015-11-12
Project Management Analytics written by Harjit Singh and has been published by FT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-12 with Business & Economics categories.
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
The Data Driven Project Manager
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Author : Mario Vanhoucke
language : en
Publisher: Apress
Release Date : 2018-03-27
The Data Driven Project Manager written by Mario Vanhoucke and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-27 with Business & Economics categories.
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 For Engineering And Construction Project Risk Management
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Author : Ivan Damnjanovic
language : en
Publisher: Springer
Release Date : 2019-05-23
Data Analytics For Engineering And Construction Project Risk Management written by Ivan Damnjanovic and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-23 with Technology & Engineering categories.
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.
Project Management Analytics
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Author : Harjit Singh
language : en
Publisher:
Release Date : 2016
Project Management Analytics written by Harjit Singh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Data mining categories.
Handbook Of Research On Expanding Business Opportunities With Information Systems And Analytics
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Author : Jamil, George Leal
language : en
Publisher: IGI Global
Release Date : 2018-07-20
Handbook Of Research On Expanding Business Opportunities With Information Systems And Analytics written by Jamil, George Leal and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-20 with Business & Economics categories.
Recent advancements in data collection will affect all aspects of businesses, improving and bringing complexity to management and demanding integration of all resources, principles, and processes. The interpretation of these new technologies is essential to the advancement of management and business. The Handbook of Research on Expanding Business Opportunities With Information Systems and Analytics is a vital scholarly publication that examines technological advancements in data collection that will influence major change in many aspects of business through a multidisciplinary approach. Featuring coverage on a variety of topics such as market intelligence, knowledge management, and brand management, this book explores new complexities to management and other aspects of business. This publication is designed for entrepreneurs, business managers and executives, researchers, business professionals, data analysts, academicians, and graduate-level students seeking relevant research on data collection advancements.
Deriving A Big Data Analytics Framework Approaching The Project Management Process For Big Data Initiatives
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Author : Theres Mitscherling
language : en
Publisher:
Release Date : 2019-08-31
Deriving A Big Data Analytics Framework Approaching The Project Management Process For Big Data Initiatives written by Theres Mitscherling and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-31 with categories.
Master's Thesis from the year 2018 in the subject Computer Science - Commercial Information Technology, grade: 7, Queensland University of Technology (Faculty of Science and Engineering), course: Master in Business Process Management, language: English, abstract: This thesis investigates the project management approach for big data projects for industry partner Red Rocks Company. The aim of this project is to understand best practice project management for big data initiatives and to develop a framework to help such projects to deliver the expected advantages. A brief literature review is undertaken to find out how big data projects are managed. From this, a Big Data Analytics Framework is derived which is based on CRISP-DM. The framework is validated through interviews with stakeholders from the corporate sector. For this case study, the first three phases of the Business Process Management Lifecycle are applied: process discovery, analysis and design. Key findings of the case study are that literature recommends an agile project management approach for big data initiatives. On the contrary, the majority of interviewed industry stakeholders confirmes a waterfall approach is conducted more often to deliver such projects. The developed Big Data Analytics Framework will add significant benefits to Red Rocks Company as it will help to successfully deliver big data initiatives in future. Big data is considered a key enabler for future decision making and process automation. The topic is however very new and not well understood yet. Hence 50% of big data projects are not delivering the expected benefits and are costing more than initially planned.
Data Analytics Initiatives
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Author : Ondřej Bothe
language : en
Publisher: CRC Press
Release Date : 2022-04-20
Data Analytics Initiatives written by Ondřej Bothe and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-20 with Computers categories.
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
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Author : Robert Keane
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-12-04
Data Analytics written by Robert Keane and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-04 with categories.
This Book Includes 3 Manuscripts Data Analytics Master The Techniques For Data Science, Big Data And Data Analytics If your business is like most, it is already generating a staggering amount of data on a regular basis. Understanding what this data truly means is key to succeeding in the marketplace these days and if you are looking for a way to give yourself an edge then Data Analytics is the book you have been waiting for. Inside this book you will find: Everything you need to know to get started completing the right kind of data analysis to benefit your business regardless of what that business is The best ways to utilize predictive analysis effectively Easy to use machine learning and regression techniques The reasons why you need discrete choice models in your life Agile Project Management Focus On Continuous Improvement, Scope Flexibility, Team Input, And Delivering Essential Quality Products Agile Project Management has grown in popularity over the past several years. Change is occurring so fast that many organizations are unable to keep up with the demands of a changing global world. Your ability to quickly change and adapt to your environment will make or break, not only your career but could be the deciding factor as to whether your company survives in the coming years. Those that have implemented the Agile strategies you will learn in this book are the ones that are succeeding and will be around for years to come. Look around at your peers. How many of them are looking to take that next step? The answer is probably very few but not you. You are an action taker. The fact that you are looking for a book like this says so. Here is some of what you will learn: The Benefits of Agile for you and your organization Agile strategy and making Agile work within an organization What is Scrum and how to implement it Explanation of ITIL and how it relates to Agile Tools of the trade Case Studies to show you Agile in action And an added BONUS - THE SECRET WEAPON Machine Learning Master The Three Types Of Machine Learning Machine learning is vital to the world of information technology. While many people may have no idea what machine learning is, they have probably used it sometime in their daily lives. For example, if you have ever done a search query on a search engine, you have worked with one form of machine learning. The program to do your search query has been trained to find the best results based on what you are looking for and it will also learn from the choices that you make. In this book you will find: Understanding the Basics of Machine Learning Why should I Use Machine Learning? Machine Learning Applications How Artificial Intelligence and Machine Learning are Different Statistics and Probability Theory The Building Blocks of Machine Learning Formal Statistical Learning Framework PAC Learning Strategies Generalization Models in Machine Learning Supervised Machine Learning Unsupervised Machine Learning Support Vector Machines Issues That Can Come Up In Machine Learning Now is the time! Get started on your Project Management journey today. Scroll Up And Click The "BUY" Button!
Managing Your Data Science Projects
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Author : Robert de Graaf
language : en
Publisher: Apress
Release Date : 2019-06-07
Managing Your Data Science Projects written by Robert de Graaf and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-07 with Computers categories.
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