Project Proliferation: Are you leaving money on the table?
How winning companies use data analysis to make smarter business decisions.
In our hyper-accelerated business world, data is key. Companies don’t just use it for an historical snapshot anymore—instead, it’s helping them predict the future. Using the right data at the right time can help ensure success, particularly when it comes to weeding out projects in your portfolio that aren’t driving ROI or contributing to the organization’s business goals.
Companies that use robust data analysis in the project management space can strengthen their decision making and strategic execution. Those who don’t often flail under the weight of too many projects spread across too few resources. Instead of analyzing the projects they take on and making data-driven decisions about which projects truly bring value to the company, they’re leaving money on the table by not selecting the optimal group of projects. By employing better analysis techniques, companies can often get the same value from their projects for a lower budget, or get greater value with the current budget.
How can companies avoid wasting time and money on low-value projects? It takes a strong foundation for data gathering so you can conduct advanced portfolio optimization analysis. This means gathering information about the business goals that drive your organization, and assessing the metrics, analytics and reports most important to leadership. Collecting good data is essential. Having the wrong data, or poor-quality data, will only mislead your efforts. Finally, assemble a list of every project the organization is working on to get a clear picture of your constraints and resources.
Pursue three levels of analysis
Once you’ve built a stable foundation for data collection, you can embark on a progressive data analysis journey using three different types of analysis:
1. Descriptive analysis: What happened? This level of analysis is the most basic, as it is fact-based and is required for developing key performance indicators and dashboards.
2. Predictive analysis: What’s going to happen? With enough good data, you can begin to predict outcomes—particularly about project risk, project performance, and the impact to project delivery as well as to your project portfolio as a whole.
3. Prescriptive analysis: What should we do? This level of analysis is more detailed and advanced. It helps you determine the optimal path against a set of potential choices. Prescriptive analysis of your project portfolio enables you to choose the highest-value portfolio and the group of projects with a higher likelihood of success.
Know your constraints
To optimize any part of your project portfolio, you must understand the constraints that exist. For example, current budgets will limit the amount of project work approved; subject matter expert availability is a common resource constraint that can limit the actual execution of projects. These constraints are the limiting factors you need to account for in order to optimize the portfolio—using one or more of the following techniques:
- Cost-value optimization: This is the most popular type of portfolio optimization. It applies techniques similar to those used for managing financial portfolios. Cost-value optimization maximizes project portfolio value using budgetary thresholds as the primary constraint.
- Resource optimization: This is another popular technique, which uses capacity management analysis. The basic constraint of resource optimization is human resource availability.
- Schedule optimization: This type of optimization is associated with project sequencing, which relates to project interdependencies. The basic constraints of schedule optimization are project timing and project dependencies.
- Work type optimization: This type enables portfolio balancing by determining optimal investment levels according to work-type categorical designations (e.g., business units, strategic goals).
Power up the project portfolio
Use a five-step methodology for conducting PMO analytics to realize the full potential of your analytic processes. Here’s a brief look at each of the five steps:
1. Define: Determine the performance criteria for measuring PMO/PPM success and develop a set of questions/hypotheses for further modeling and investigation.
2. Transform: Gather and transform all available resource, project and business data for further visualization and analysis.
3. Visualize: Inventory all projects with related resources. Highlight key trends/insights based on project and business data.
4. Evaluate: Develop an analytic framework to test, adjust and optimize against tradeoffs between project sequencing, resource allocation and portfolio value.
5. Recommend: Develop a final set of project prioritization recommendations for your desired future state.
Portfolio optimization delivers significant strategic benefits to any organization. Having the right data can let your organization know what is happening to the portfolio (descriptive analysis), what could happen (predictive analysis), and what senior leaders should do (prescriptive analysis). By getting the processes in place to collect good data and analyze it in meaningful ways, you’ll gain the actionable insight to optimize your portfolio and achieve your business goals.