Project Management Analytics: A Data-Driven Approach to Making Rational and Effective Project Decisions

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

Table of Contents:
Part 1: Approach Chapter 1: Project Management Analytics     1 Chapter 2: Data-Driven Decision-Making     25 Part 2: Project Management Fundamentals Chapter 3: Project Management Framework     45 Part 3: Introduction to Analytics Concepts, Tools, and Techniques Chapter 4: Chapter Statistical Fundamentals I: Basics and Probability Distributions     77 Chapter 5: Statistical Fundamentals II: Hypothesis, Correlation, and Linear Regression     117 Chapter 6: Analytic Hierarchy Process     151 Chapter 7: Lean Six Sigma     183 Part 4: Applications of Analytics Concepts, Tools, and Techniques in Project Management Decision-Making Chapter 8: Statistical Applications in Project Management     229 Chapter 9: Project Decision-Making with the Analytic Hierarchy Process (AHP)     265 Chapter 10: Lean Six Sigma Applications in Project Management     291 Part 5: Appendices Appendix A: z-Distribution     321 Appendix B: t-Distribution     325 Appendix C: Binomial Probability Distribution (From n = 2 to n = 10)     327 Index     329

About the Author :
Harjit Singh earned his MBA from University of Texas and his master’s degree in Computer Engineering from California State University, Sacramento. He is a Certified Scrum Master, Lean Six Sigma professional, and holds PMP (Project Management Professional) credentials. He has more than 25 years of experience in the private and public sector as an information technology engineer, project manager, and educator. Currently, he is working as a data processing manager III at the State of California. In addition, he is also a visiting professor/adjunct faculty at Keller Graduate School of Management, DeVry University and Brandman University, where he teaches project management, business management, and information technology courses. Prior to this, he worked at Hewlett-Packard for 15 years as a systems software engineer and technical project manager. He is also a former member of the Board of Directors for the Sacramento Valley Chapter of the Project Management Institute (PMI) where he served in the capacity of CIO and vice president of relations and marketing.