Use Data to Revolutionize Project Planning
Planning projects accurately is notoriously difficult. According to the 2018 “Pulse of the Profession” study conducted by the Project Management Institute, between 2011 to 2018 only 50% of projects where completed on time and 55% were within budget. Even though firms have been investing in project management techniques since the 1970s, the accuracy of their project plans have not improved much. Inaccurate forecasts of project durations, costs, resources, and benefits are a major source of risk and can affect leaders’ careers, as well as organizations’ growth opportunities and the health of the economy at large. But today, data-driven prediction and decision-making offer unprecedented opportunities in the field of project planning. Using historical data on projects’ initial forecasted completion dates and total costs, among other measures, accuracy estimates can be established. Such accuracy estimates can then be used when forecasting and setting new projects’ goals.
The California bullet train between San Diego and San Francisco. Lockheed Martin’s Joint Strike Fighter program. Berlin’s Brandenburg Airport. Apple’s AirPower wireless charging pad. These are just a few examples of projects that suffered severe schedule delays and cost overruns, or that were unable to deliver on their promised scope.
Planning projects accurately is notoriously difficult, whether they’re publicly or privately funded, or in domains like construction, technology, pharma, or infrastructure. According to the 2018 “Pulse of the Profession” study conducted by the Project Management Institute, between 2011 to 2018 only about 50% of projects where completed on time and approximately 55% were within budget. Even though firms have been investing in project management techniques since the 1970s, the accuracy of project plans has not improved much.
Inaccurate forecasts involving durations, costs, resources, and benefits are clearly major source of risk for leaders’ careers and organizations’ growth opportunities. For example, firms waste an average of $119 million for every $1 billion spent (11.9%) on projects due to poor project performance. Late or pricey projects can also affect the health of the economy at large. Gross domestic product (GDP) contributions from project-oriented industries are forecasted to reach $20.2 trillion by 2027; major missteps have the potential to chip away at this number.
Forty years ago, psychologist and Nobel prizewinner Daniel Kahneman, along with long-term collaborator Amos Tversky, noted that humans tend to suffer from a planning fallacy: they overpromise and underdeliver by offering unrealistic forecasts of projects’ objectives. Kahneman and Tversky suggested using an outside view to develop more realistic project plans. They proposed using a forecasting technique called reference class forecasting, by which projects’ durations or costs are predicted by comparing a project of interest to a set of past similar projects. Such as outside view is in contrast to the inside view that’s more often taken, where the project is planned with little regard to historical performance and its ability to meet set targets.
Today, changing attitudes toward data collection, data-driven prediction, and decision-making offers unprecedented opportunities in the field of project planning. With data, firms can now operationalize Kahneman and Tversky’s ideas, going beyond their original vision. Using historical data on projects’ initial forecasted completion dates and total costs, in addition to realized or actual expenditures and durations, accuracy estimates can be established. Such estimates can then be used when forecasting and setting new projects’ goals.
