- What Is Analytics?
- Why Is Analytics Important in Project Management?
- How Can Project Managers Use Analytics in Project Management?
- Project Management Analytics Approach
- Summary
- 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
- Bibliography
This chapter is from the book
This chapter is from the book
This chapter is from the book
How Can Project Managers Use Analytics in Project Management?
Analytics finds its use in multiple areas throughout the project and project management life cycles. The key applications of analytics in this context include, but are not limited to, the following:
Assessing feasibility: Analytics can be used to assess the feasibility of various alternatives so that a project manager can pick the best option.
Managing data overload: Due to the contemporary Internet age, data overload has crippled project managers’ capability to capture meaningful information from mountains of data. Analytics can help project managers overcome this issue.
Enhancing data visibility and control via focused dashboards: An analytics dashboard can provide a project manager a single view to look at the big picture and determine both how each project and its project team members are doing. This information comes in handy for prioritizing project tasks and/or moving project team members around to maximize productivity.
Analyzing project portfolios for project selection and prioritization: Project portfolio analysis is a useful application of analytics. This involves evaluating a large number of project proposals (or ideas) and selecting and prioritizing the most viable ones within the constraints of organizational resources and other relevant factors.
Across all project organizations in general, but in a matrix organization in particular, multiple projects compete for finite resources. Organizations must select projects carefully after complete assessment of each candidate project’s feasibility based on the organization’s project selection criteria, which might include, but not be limited to, the following factors:
- Technical, economic, legal, political, capacity, and capability constraints
Cost-benefits analysis resulting in scoring based on various financial models such as:
Resource requirements
- Internal resources (only functional department resources, cross-functional resources, cross-organizational resources, or any combination of the preceding)
- External resources
- Both internal and external resources
- Project complexity
- Project risks
- Training requirements
Analytics can help organizations with selecting projects and prioritizing shortlisted projects for optimal allocation of any scarce and finite resources.
Improve project stakeholder management: Analytics can help improve project stakeholder management by enabling a project manager to predict stakeholder responses to various project decisions. Project stakeholder management is both art and science—art because it depends partly on the individual skillset, approach, and personality of the individual project manager, and science because it is a highly data-driven process. Project managers can use analytics to predict the outcomes of the execution of their strategic plans for stakeholder engagement management and to guide their decisions for appropriate corrective actions if they find any discrepancy (variance) between the planned and the actual results of their efforts.
Project stakeholder management is much like customer relationship management (CRM5) in marketing because customers are essentially among the top-level project stakeholders and project success depends on their satisfaction and acceptance of the project outcome (product or service). Demographic studies, customer segmentation, conjoint analysis, and other techniques allow marketers to use large amounts of consumer purchase, survey, and panel data to understand and communicate marketing strategy. In his paper “CRM and Stakeholder Management,” Dr. Ramakrishnan (2009) discusses how CRM can help with effective stakeholder management. According to him, there are seven Cs of stakeholder management:
- Concern
- Communicate
- Contribute
- Connect
- Compound
- Co-Create
- Complete
Figure 1.1 illustrates the seven Cs of stakeholder management.
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Figure 1.1 Seven Cs of Project Stakeholder Management
The seven Cs constitute seven elements of the project stakeholder management criteria, which can be evaluated for their relative importance or strength with respect to the goal of achieving effective stakeholder management by utilizing the multi-criteria evaluation capability of the Analytic Hierarchy Process (AHP).6
Web analytics can also help managers analyze and interpret data related to the online interactions with the project stakeholders. The source data for web analytics may include personal identification information, search keywords, IP address, preferences, and various other stakeholder activities. The information from web analytics can help project managers use the adaptive approach7 to understand the stakeholders better, which in turn can further help them customize their communications according to the target stakeholders.
Predict project schedule delays and cost overruns: Analytics can tell a project manager whether the project is on schedule and whether it’s under or over budget. Also, analytics can enable a project manager to predict the impact of various completion dates on the bottom line (project cost). For example, Earned Value Analytics (covered in Chapter 8, “Statistical Applications in Project Management”) helps project managers avoid surprises by helping them proactively discover trends in project schedule and cost performance.
Manage project risks: Another area in a project’s life cycle where analytics can be extremely helpful is the project risk management area. Project risk identification, ranking, and prioritization depend upon multiple factors, including at least the following:
- Size and complexity of the project
- Organization’s risk tolerance
- Risk probability, impact, and horizon
- Competency of the project or risk manager
Predictive analytics models can be used to analyze those multiple factors for making rational decisions to manage the risks effectively.
Improve project processes: Project management involves the execution of a multitude of project processes. Thus, continuous process improvement is essential for eliminating waste and improving the quality of the processes and the product of the project. Improvement projects typically involve four steps:
- Understand the current situation.
- Determine the desired (target) future situation.
- Perform gap analysis (find the delta between the target and the current situations).
- Make improvement decisions to address the gap.
Analytics can help project managers through all four process improvement steps by enabling the use of a “Project Management—Lean Six Sigma” blended or hybrid methodology for managing the projects with embedded continuous improvement.