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Author: Marcus Glowasz
Publisher: Marcus Glowasz
ISBN: 3033095224
Category : Business & Economics
Languages : en
Pages : 341
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Book Description
The use of data and analytics significantly improves project performance, but it requires a cultural foundation that connects and engages people, enables evidence-based thinking and facilitates new capabilities. In an era of rapid change and an ever-increasing flow of information, data is a highly-valued asset. Organizations are transforming business areas into data-driven practices to make better and faster decisions and respond accurately to fast-changing market behaviors and demands. The project management domain cannot afford to be left behind. Old practices will not serve the sector in the twenty-first century. That means project delivery functions must embrace new and innovative ways to deliver change. In this book, Marcus Glowasz argues the urgent need to employ data and analytics for improved project performance. Leading Projects with Data is full of actionable insights to drive the behaviors and culture shifts necessary to ensure a successful transition to data-informed project delivery practices. A thriving practice needs people with the mindset to collaborate across boundaries, learn from failure, adapt to a new normal of frequent disruption and change, and value knowledge. Diversity, transparency, and critical thinking are key drivers in the new world of project management. The future is here. Embrace it.
Author: Marcus Glowasz
Publisher: Marcus Glowasz
ISBN: 3033095224
Category : Business & Economics
Languages : en
Pages : 341
Get Book Here
Book Description
The use of data and analytics significantly improves project performance, but it requires a cultural foundation that connects and engages people, enables evidence-based thinking and facilitates new capabilities. In an era of rapid change and an ever-increasing flow of information, data is a highly-valued asset. Organizations are transforming business areas into data-driven practices to make better and faster decisions and respond accurately to fast-changing market behaviors and demands. The project management domain cannot afford to be left behind. Old practices will not serve the sector in the twenty-first century. That means project delivery functions must embrace new and innovative ways to deliver change. In this book, Marcus Glowasz argues the urgent need to employ data and analytics for improved project performance. Leading Projects with Data is full of actionable insights to drive the behaviors and culture shifts necessary to ensure a successful transition to data-informed project delivery practices. A thriving practice needs people with the mindset to collaborate across boundaries, learn from failure, adapt to a new normal of frequent disruption and change, and value knowledge. Diversity, transparency, and critical thinking are key drivers in the new world of project management. The future is here. Embrace it.
Author: Edward W. Merrow
Publisher: John Wiley & Sons
ISBN: 111938219X
Category : Business & Economics
Languages : en
Pages : 261
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Book Description
Quantitative analysis of outcomes vs PMs at the individual level Leading Complex Projects takes a unique approach to post-mortem analysis to provide project managers with invaluable insight. For the first time, individual PM characteristics are quantitatively linked to project outcomes through a major study investigating the role of project leadership in the success and failure of complex industrial projects; hard data on the backgrounds, education, and personality characteristics of over 100 directors of complex projects is analyzed against the backdrop of project performance to provide insight into controllable determinants of outcomes. By placing these analyses alongside their own data, PMs will gain greater insight into areas of weakness and strength, locate recurring obstacles, and identify project components in need of greater planning, oversight, or control. The role of leadership is to deliver results; in project management, this means taking responsibility for project outcomes. PMs are driven by continuous improvement, and this book provides a wealth of insight to help you achieve the next step forward. Understand why small, simple projects consistently outperform larger, more complex projects Delve into the project manager's role in generating successful outcomes Examine the data from over 100 PMs of complex industrial projects Link PM characteristics to project outcome to find areas for improvement Complex industrial projects from around the world provide a solid basis for quantitative analysis of outcomes—and the PMs who drive them. Although the majority of the data is taken from projects in the petroleum industry, the insights gleaned from analysis are widely applicable across industry lines for PMs who lead complex projects of any stripe. Leading Complex Projects provides clear, data-backed improvement guidance for anyone in a project management role.
Author: Jike Chong
Publisher: Simon and Schuster
ISBN: 1617298891
Category : Computers
Languages : en
Pages : 510
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Book Description
Lead your data science teams and projects to success! To make a consistent, meaningful impact as a data science leader, you must articulate technology roadmaps, plan effective project strategies, support diversity, and create a positive environment for professional growth. This book delivers the wisdom and practical skills you need to thrive as a data science leader at all levels, from team member to the C-suite. "How to lead in data science" shares unique leadership techniques from high-performance data teams. It's filled with best practices for balancing project trade-offs and producing exceptional results, even when beginning with vague requirements or unclear expectations. You'll find a clearly presented modern leadership framework based on current case studies, with insights reaching all the way to Aristotle and Confucius. As you read, you'll build practical skills to grow and improve your team, your company's data culture, and yourself.
Author: Danette McGilvray
Publisher: Elsevier
ISBN: 0080558399
Category : Computers
Languages : en
Pages : 353
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Book Description
Information is currency. Recent studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. In this important and timely new book, Danette McGilvray presents her "Ten Steps approach to information quality, a proven method for both understanding and creating information quality in the enterprise. Her trademarked approach—in which she has trained Fortune 500 clients and hundreds of workshop attendees—applies to all types of data and to all types of organizations.* Includes numerous templates, detailed examples, and practical advice for executing every step of the "Ten Steps approach.* Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices.* A companion Web site includes links to numerous data quality resources, including many of the planning and information-gathering templates featured in the text, quick summaries of key ideas from the Ten Step methodology, and other tools and information available online.
Author: Ralph L. Kliem
Publisher: J. Ross Publishing
ISBN: 9781932159103
Category : Business & Economics
Languages : en
Pages : 282
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Book Description
The number one cause of failure on the people side of project management stems from ineffective or inadequate leadership. Leading High Performance Projects explains how project managers can adopt the most appropriate style under a given set of circumstances, encourage greater teaming, become more effective decision-makers, reduce incidences of negative conflict and eliminate opportunities for 'negative energy' to permeate a project. Ralph L. Kliem, President of Practical Creative Solutions, is an accomplished author of hundreds of articles and 12 books primarily in project management.
Author: Jike Chong
Publisher: Simon and Schuster
ISBN: 1638356807
Category : Computers
Languages : en
Pages : 847
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Book Description
A field guide for the unique challenges of data science leadership, filled with transformative insights, personal experiences, and industry examples. In How To Lead in Data Science you will learn: Best practices for leading projects while balancing complex trade-offs Specifying, prioritizing, and planning projects from vague requirements Navigating structural challenges in your organization Working through project failures with positivity and tenacity Growing your team with coaching, mentoring, and advising Crafting technology roadmaps and championing successful projects Driving diversity, inclusion, and belonging within teams Architecting a long-term business strategy and data roadmap as an executive Delivering a data-driven culture and structuring productive data science organizations How to Lead in Data Science is full of techniques for leading data science at every seniority level—from heading up a single project to overseeing a whole company's data strategy. Authors Jike Chong and Yue Cathy Chang share hard-won advice that they've developed building data teams for LinkedIn, Acorns, Yiren Digital, large asset-management firms, Fortune 50 companies, and more. You'll find advice on plotting your long-term career advancement, as well as quick wins you can put into practice right away. Carefully crafted assessments and interview scenarios encourage introspection, reveal personal blind spots, and highlight development areas. About the technology Lead your data science teams and projects to success! To make a consistent, meaningful impact as a data science leader, you must articulate technology roadmaps, plan effective project strategies, support diversity, and create a positive environment for professional growth. This book delivers the wisdom and practical skills you need to thrive as a data science leader at all levels, from team member to the C-suite. About the book How to Lead in Data Science shares unique leadership techniques from high-performance data teams. It’s filled with best practices for balancing project trade-offs and producing exceptional results, even when beginning with vague requirements or unclear expectations. You’ll find a clearly presented modern leadership framework based on current case studies, with insights reaching all the way to Aristotle and Confucius. As you read, you’ll build practical skills to grow and improve your team, your company’s data culture, and yourself. What's inside How to coach and mentor team members Navigate an organization’s structural challenges Secure commitments from other teams and partners Stay current with the technology landscape Advance your career About the reader For data science practitioners at all levels. About the author Dr. Jike Chong and Yue Cathy Chang build, lead, and grow high-performing data teams across industries in public and private companies, such as Acorns, LinkedIn, large asset-management firms, and Fortune 50 companies. Table of Contents 1 What makes a successful data scientist? PART 1 THE TECH LEAD: CULTIVATING LEADERSHIP 2 Capabilities for leading projects 3 Virtues for leading projects PART 2 THE MANAGER: NURTURING A TEAM 4 Capabilities for leading people 5 Virtues for leading people PART 3 THE DIRECTOR: GOVERNING A FUNCTION 6 Capabilities for leading a function 7 Virtues for leading a function PART 4 THE EXECUTIVE: INSPIRING AN INDUSTRY 8 Capabilities for leading a company 9 Virtues for leading a company PART 5 THE LOOP AND THE FUTURE 10 Landscape, organization, opportunity, and practice 11 Leading in data science and a future outlook
Author: Danette McGilvray
Publisher: Academic Press
ISBN: 0128180161
Category : Computers
Languages : en
Pages : 378
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Book Description
Executing Data Quality Projects, Second Edition presents a structured yet flexible approach for creating, improving, sustaining and managing the quality of data and information within any organization. Studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. Help is here! This book describes a proven Ten Step approach that combines a conceptual framework for understanding information quality with techniques, tools, and instructions for practically putting the approach to work – with the end result of high-quality trusted data and information, so critical to today's data-dependent organizations. The Ten Steps approach applies to all types of data and all types of organizations – for-profit in any industry, non-profit, government, education, healthcare, science, research, and medicine. This book includes numerous templates, detailed examples, and practical advice for executing every step. At the same time, readers are advised on how to select relevant steps and apply them in different ways to best address the many situations they will face. The layout allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, best practices, and warnings. The experience of actual clients and users of the Ten Steps provide real examples of outputs for the steps plus highlighted, sidebar case studies called Ten Steps in Action. This book uses projects as the vehicle for data quality work and the word broadly to include: 1) focused data quality improvement projects, such as improving data used in supply chain management, 2) data quality activities in other projects such as building new applications and migrating data from legacy systems, integrating data because of mergers and acquisitions, or untangling data due to organizational breakups, and 3) ad hoc use of data quality steps, techniques, or activities in the course of daily work. The Ten Steps approach can also be used to enrich an organization's standard SDLC (whether sequential or Agile) and it complements general improvement methodologies such as six sigma or lean. No two data quality projects are the same but the flexible nature of the Ten Steps means the methodology can be applied to all. The new Second Edition highlights topics such as artificial intelligence and machine learning, Internet of Things, security and privacy, analytics, legal and regulatory requirements, data science, big data, data lakes, and cloud computing, among others, to show their dependence on data and information and why data quality is more relevant and critical now than ever before. - Includes concrete instructions, numerous templates, and practical advice for executing every step of The Ten Steps approach - Contains real examples from around the world, gleaned from the author's consulting practice and from those who implemented based on her training courses and the earlier edition of the book - Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices - A companion Web site includes links to numerous data quality resources, including many of the templates featured in the text, quick summaries of key ideas from the Ten Steps methodology, and other tools and information that are available online
Author: Robert de Graaf
Publisher: Apress
ISBN: 1484249070
Category : Computers
Languages : en
Pages : 146
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Book Description
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
Author: Howard Steven Friedman
Publisher: Columbia University Press
ISBN: 0231556691
Category : Computers
Languages : en
Pages : 271
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Book Description
Whether you are a newly minted MBA or a project manager at a Fortune 500 company, data science will play a major role in your career. Knowing how to communicate effectively with data scientists in order to obtain maximum value from their expertise is essential. This book is a compelling and comprehensive guide to data science, emphasizing its real-world business applications and focusing on how to collaborate productively with data science teams. Taking an engaging narrative approach, Winning with Data Science covers the fundamental concepts without getting bogged down in complex equations or programming languages. It provides clear explanations of key terms, tools, and techniques, illustrated through practical examples. The book follows the stories of Kamala and Steve, two professionals who need to collaborate with data science teams to achieve their business goals. Howard Steven Friedman and Akshay Swaminathan walk readers through each step of managing a data science project, from understanding the different roles on a data science team to identifying the right software. They equip readers with critical questions to ask data analysts, statisticians, data scientists, and other technical experts to avoid wasting time and money. Winning with Data Science is a must-read for anyone who works with data science teams or is interested in the practical side of the subject.
Author: Joseph A. Cazier
Publisher: John Wiley & Sons
ISBN: 1119800994
Category : Computers
Languages : en
Pages : 327
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Book Description
A step-by-step guide for business leaders who need to manage successful big data projects Leading in Analytics: The Critical Tasks for Executives to Master in the Age of Big Data takes you through the entire process of guiding an analytics initiative from inception to execution. You’ll learn which aspects of the project to pay attention to, the right questions to ask, and how to keep the project team focused on its mission to produce relevant and valuable project. As an executive, you can’t control every aspect of the process. But if you focus on high-impact factors that you can control, you can ensure an effective outcome. This book describes those factors and offers practical insight on how to get them right. Drawn from best-practice research in the field of analytics, the Manageable Tasks described in this book are specific to the goal of implementing big data tools at an enterprise level. A dream team of analytics and business experts have contributed their knowledge to show you how to choose the right business problem to address, put together the right team, gather the right data, select the right tools, and execute your strategic plan to produce an actionable result. Become an analytics-savvy executive with this valuable book. Ensure the success of analytics initiatives, maximize ROI, and draw value from big data Learn to define success and failure in analytics and big data projects Set your organization up for analytics success by identifying problems that have big data solutions Bring together the people, the tools, and the strategies that are right for the job By learning to pay attention to critical tasks in every analytics project, non-technical executives and strategic planners can guide their organizations to measurable results.
