Introduction to Project Management Analytics: A Data-Driven Approach to Making Rational and Effective Project Decisions
- What Is Analytics?
- Why Is Analytics Important in Project Management?
- How Can Project Managers Use Analytics in Project Management?
- Project Management Analytics Approach
- Key Terms
- Case Study: City of Medville Uses Statistical Approach to Estimate Costs for Its Pilot Project
- Case Study Questions
- Chapter Review and Discussion Questions
- “Information is a source of learning. But unless it is organized, processed, and available to the right people in a format for decision making, it is a burden, not a benefit.”
- —William Pollard (1828–1893), English Clergyman
Effective project management entails operative management of uncertainty on the project. This requires the project managers today to use analytical techniques to monitor and control the uncertainty as well as to estimate project schedule and cost more accurately with analytics-driven prediction. Bharat Gera, Line Manager at IBM agrees, “Today, project managers need to report the project metrics in terms of ‘analytical certainty.’” Analytics-based project metrics can essentially enable the project managers to measure, observe, and analyze project performance objectively and make rational project decisions with analytical certainty rather than making vague decisions with subjective uncertainty. This chapter presents you an overview of the analytics-driven approach to project management.
What Is Analytics?
Analytics (or data analytics) can be defined as the systematic quantitative analysis of data or statistics to obtain meaningful information for better decision-making. It involves the collective use of various analytical methodologies, including but not limited to statistical and operational research methodologies, Lean Six Sigma, and software programming. The computational complexity of analytics may vary from low to very high (for example, big data). The highly complex applications usually utilize sophisticated algorithms based on statistical, mathematical, and computer science knowledge.
Analytics versus Analysis
Analysis and analytics are similar-sounding terms, but they are not the same thing. They do have some differences.
Both are important to project managers. They (project managers) can use analysis to understand the status quo that may reflect the result of their efforts to achieve certain objectives. They can use analytics to identify specific trends or patterns in the data under analysis so that they can predict or forecast the future outcomes or behaviors based on the past trends.
Table 1.1 outlines the key differences between analytics and analysis.
Table 1.1 Analytics vs. Analysis
Analytics can be defined as a method to use the results of analysis to better predict customer or stakeholder behaviors.
Analysis can be defined as the process of dissecting past gathered data into pieces so that the current (prevailing) situation can be understood.
Per Merriam-Webster dictionary, analytics is the method of logical analysis.
Per Merriam-Webster dictionary, analysis is the separation of a whole into its component parts to learn about those parts.
Analytics look forward to project the future or predict an outcome based on the past performance as of the time of analysis.
Analysis presents a historical view of the project performance as of the time of analysis.
Use analytics to predict which functional areas are more likely to show adequate participation in future surveys so that a strategy can be developed to improve the future participation.
Use analysis to determine how many employees from each functional area of the organization participated in a voice of the workforce survey.
Types of Analysis
Prediction of future audience behaviors based on their past behaviors
Target audience segmentation
Target audience grouping based on multiple past behaviors
Statistical, mathematical, computer science, and Lean Six Sigma tools, and techniquesbased algorithms with advanced logic
Sophisticated predictive analytics software tools
Business intelligence tools
Structured query language (SQL)
Identify specific data patterns
Derive meaningful inferences from data patterns
Use inferences to develop regressive/predictive models
Use predictive models for rational and effective decision-making
Develop a SharePoint list to track key performance indicators
Run SQL queries on a data warehouse to extract relevant data for reporting
Run simulations to investigate different scenarios
Use statistical methods to predict future sales based on past sales data
Develop a business case
Conduct risk assessment
Model business processes
Develop business architecture