Project data can often challenge biased points of view and assumptions. Data and analytics are the differentiators that can deliver the truth about projects. Once this is accepted throughout an organization, project data will be viewed as a valuable asset.

Project management professionals are constantly producing valuable data that they themselves should benefit from. If those professionals aren’t diligent with data handling and management, they’re effectively limiting the potential benefits of the same data. And that’s why managing it diligently and impartially is crucial. There are three facets of effective project data management:

• Collaboration to ensure assumptions and current ‘truths’ are challenged
• The leverage of data through effective sharing and management strategies
• The collection and effective use of data

Raw project data can be construed in a number of ways. Some data may be incomplete, and in some cases, be used to tell untruths or outright lies. This misuse of data is often inspired by working cultures fueled by fear, shame, or the search for success at any cost.

Only when trust and transparency are intrinsic in an organization’s culture can data deliver its full potential and value. Truthfulness across the board is key, and a lack of it is a barrier to successful data-driven project management

Some failures should be used as a tool for improvement. So-called failure ceremonies ensure future project performance is fine-tuned and improved over time. Making mistakes with data is often crucial to delivering insights and driving innovation.

When it comes to data-informed decision-making, admitting mistakes and leading by example in this regard is the key to long-term improvements in project performance.

Insights from human experience and intuition and those derived from data should work in harmony with one another. In other words, each should act as a stressor to the other.

Effective data culture in any organization should always be driven by critical thinking. Effective data literacy involves asking the right questions, productive curiosity, and data-informed decision-making. Such an approach to project management can improve forecasting and overall project performance.

Effective data management involves simplifying messages and stripping away irrelevant information. Upskilling project managers in this regard involves coaching visualization and storytelling skills while moving away from ineffective project status reports and misleading dashboards.

This coaching program breaks down effective data-informed decision-making into three simple instructions:

• Avoid the misuse of data — don’t avoid data, but don’t become a data activist, either.
• Manage the risks involved in projects with a series of data-driven risk controls.
• Ensure all project communications developed from data insights are action-driven.

The proper use of project data doesn’t just aid risk avoidance and management, but it also helps to identify business opportunities. That’s why it’s important to synchronize risks and innovation initiatives with particular business areas and their strategies.

To maximize project performance, it’s vitally important to enhance the collective knowledge of an organization. Project managers can achieve this by seeking new data sources, forging productive partnerships, and broadening the scope of data and the resultant insights.

Project management intelligence evolves over time — but only when key individuals preserve and actively share insights with the relevant stakeholders. To aid that evolution, managers should continually look for improvements to their data-informed decision-making processes. Such improvements might be driven by the use of emerging technologies such as artificial intelligence (AI).