TUM Library

Cover; Half Title; Series Page; Title Page; Copyright Page; Contents; About the Editor; Contributors; Chapter 1 Introduction; Chapter 2 Why Data Analytics in Project Management?; Taking Root; The View from 10,000 Meters; Data Warehousing; Project Manager; Project Office; Chief Operating Officer (COO); Executive Committee; Descriptive, Predictive, and Prescriptive Data Analytics; Data Analytics 3.0?; Once Again: Why Data Analytics in Project Management?; References; Chapter 3 Data Analytics Risk: Lost in Translation?; The Risk Management Process; Establishing Tolerance.

Risk and Data Analytics LanguageData Collection Risk; What Data Do We Need and Why? (Risk #1-The Need); Is the Data Properly Sourced? (Risk #2-The Source); Is the Data Consistent (Risk #3-Consistency Risks); Risk Collection; Engage Meaningful Stakeholders; Gather Risk Insight Consistently; Focus on the Mission; Exploratory vs. Confirmatory vs. Predictive; Exploratory Risk in Data Analytics; Availability; Integrity; Exploratory Trends; Confirmatory Analytics Risks; Data Availability; Data Integrity; Data Trends; Past Performance Is Not Inherently Indicative of Future Results.

Predictive Risk in Data AnalyticsThe Future Is Unknown; The Future Environment Is Only Partially Knowable; Predictive Analysis and Consequences; Risk in Communicating Results; When to Share; How to Share; With Whom to Share the Message; Solving and Resolving Our Data Analytics Risks; Will It Work Consistently?; Does It Generate More Harm than Good?; Does It Allow for the Same Outputs as Other Data in the Analysis?; Success!; Chapter 4 Analytical Challenges of a Modern PMO; Toward an Analytically Matured PMO; Methods and Tools; Human Resources; Project Environment; Project Knowledge Management.

The PMO as the Multilevel Data Analysis CenterProjects, Portfolio, and Organization; Project Level; Portfolio Level; Organization Level; Operations, Tactics, and Strategy; Operational Level; Tactical Level; Strategic Level; The Challenge of Multi-Sourced Data; References; Chapter 5 Data Analytics and Project Portfolio Management; Introduction; Project Portfolio Management and Data Analytics; Levels of Analysis; Descriptive analysis-this helps answer the question, "What has happened?"; Predictive analysis-this helps answer a more important question, "What will happen?"

Prescriptive analysis-this helps answer a more difficult question, "What we should do?"Approach; Portfolio Reports: Portfolio Bubble Charts; Benefits of Portfolio Bubble Charts; Data Needed; PPM and Decision-Making; Project Portfolio Management as a Rational Decision- Making Process; Project Portfolio Management: Practice and Context; Main Roles in Data Analytics; Role; Responsibilities; Requirements; Data Analytics and Project Portfolio Performance; Conclusions; References; Chapter 6 Earned Value Method; Introduction; EVM Methods; Descriptive EVM; Predictive EVM; Earned Value Graphs.

Interpreting Earned Value Results.

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.

OCLC-licensed vendor bibliographic record.