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

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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.
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  • “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

Criterion

Analytics

Analysis

Working Definition

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.

Dictionary Definition

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.

Time Period

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.

Examples

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

Tools

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)

Typical Activities

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

Elicit requirements

Document requirements

Conduct risk assessment

Model business processes

Develop business architecture

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