“Data-driven” is an organizational mantra these days. Leaders want to make decisions based on objective metrics, not hunches. Insights, not instincts.
But which data should do the driving? It’s not all created equal. Traditional project management office (PMO) data, for example, tends to focus on whether projects were on schedule and within budget. That’s useful, of course, but it’s limited both in scope and value.
If PMOs want to have a more strategic role within their organizations, they must expand the data they collect and analyze. Going deeper with data analytics can enable project leaders to identify and dodge constraints. It can also equip sponsors and executives with insights capable of guiding strategic decisions, including project prioritization and funding.
For example, a PMO that tracks and forecasts resource allocation across its portfolio might anticipate a looming shortage or constraint. Spotting it early could enable the company to proactively make the hires or purchases it needs to stay on track. Or data regarding market conditions might convince an executive team to pause a project under way, even if it’s on track, if it’s no longer likely to realize its intended benefit.
However, there is a caveat: There is absolutely such a thing as too much data. And there are circumstances when the resources required for gathering and processing data or overstuffed, confusing executive dashboards are counterproductive.
That means there’s a sweet spot for PMOs looking to maximize analytics to aid strategic decision-making. Here are three considerations to help you find it.
Track Data Across a Range of Categories and Considerations
Using data to guide strategic decisions means incorporating data from all the different vectors that might influence those decisions. That includes traditional metrics that track progress and project health, as well as scored risk factors that assess the likelihood a project might veer off track and acknowledge areas of uncertainty.
Financial data is also essential—projecting a project’s ROI, for example, as well as operating and capital expenses. Then there’s assessing and projecting resource capacity across the project portfolio, an area where many PMOs have traditionally struggled.
It’s also useful to grade and categorize projects based on several factors, such as the departments or business functions they represent and their potential impact. This can help with portfolio balancing decisions, as organizations might opt for some modest projects focused on business improvement alongside large, transformational efforts.
Identify and Elevate the Most Important Metrics
Executives often want access to every possible data point. But project portfolios typically are better served when stakeholders and sponsors settle on a few key North Star metrics tied to the primary benefits each project is supposed to deliver. Those objectives could be financial or strategic. Agreeing on them can equip PMO and organizational leaders to gauge how important a given project is to the company, how to prioritize it against other potential projects, and, later, how to measure its effectiveness.
A shared dashboard can provide executives with visibility and maintain their confidence in the ongoing progress. Common dashboard elements include progress to goals, a project health summary (on track, needs attention, not recoverable), individual project performance, top risks, the pipeline of projects, and areas for decision.
Use AI to Surface Insights from Complex Data Sets
As data sets get larger and more complex, there’s a real danger of becoming overwhelmed. Artificial intelligence can help sift through all that data and score various attributes based on organizational priorities, portfolio balance targets, and external factors such as market conditions.
That sort of evaluation could assist PMOs in figuring out which projects to tackle during a particular year and how many it could reasonably take on before running into significant resource constraints.
Be sure to keep humans in the equation, however. AI is not capable of making independent decisions.
Realizing the Promise of Data-Driven Decisions
Adopting a more data-driven approach to decision-making holds immense promise for PMOs. Realizing that promise means focusing on the right data and ensuring you have the analytics systems in place to pull out key insights.
