Five ways project managers can use data analytics to boost project performance

Knowing how to harness data effectively to benefit projects and programmes is key – but often, there is a lack of awareness about how to deploy it and the benefits that this can bring. In this article, Benson Mafudze and Connor Smith from AECOM Data Advisory outline five ways that project managers can use data analytics to boost performance on any project.

Given the speed with which technology and data legislation can advance and the plethora of off-the-shelf software that promises to revolutionise projects today, it’s no surprise that managers can struggle when choosing the most effective way to capture, store and visualise their data.  

That’s why with a better understanding of the process and the efficiencies that data empowerment can bring, there are exponential benefits to be gained.  

Here we share five effective uses of data analytics: 

1/ Making data more accessible 

For data specialists, interpreting vast quantities of data from tables and graphs is second nature. But for others – including project stakeholders – the information might not be so straightforward. This is where business intelligence (BI) tools for data visualisation can help.  

When used effectively, BI visualisation allows your project reporting to be communicated in a clear, digestible format. As well as making data more accessible, these tools can improve collaboration between teams by providing a more integrated workspace – with bespoke dashboards for each stakeholder group or review session.  

2/ Providing a single source of truth 

As a project manager, the overall success of the project will ultimately fall on your shoulders.  

Effective use of data analytics principles alongside your regular reporting can lead to a greater sense of control and improved accountability across the project team.  

By utilising a single reporting dashboard suite, this removes the occurrence of rogue files saved to desktop, enables better protection via cloud storage, and ensures that data access is universal across the project. 

3/ Adopting trend analytics to gain key insights 

One of the most accessible data analytics principles to apply to project data is trend analytics. This is a strategy used to forecast future outcomes based on historical data. 

Besides helping to identify positive outcomes, trend analytics can allow project managers to address issues before they escalate.  

Furthermore, custom analytics within a digital report can be configured to automatically refresh each of these key metrics with each new data refresh – instantly generating a headline view of key performance indicators.  

4/ Integrating data into a unified platform 

With data often stored across numerous databases, software packages, and legacy company servers, reporting can often mean hours of copying, pasting, and formatting of slide packs. 

BI software such as Microsoft Power BI or Tableau allow the user to load and transform data from a seemingly endless list of sources databases such as Azure. 

Combined with a robust data model, this can allow comparison and analytics of datasets that would typically present a challenge.  

5/ Early engagement means early results  

The main challenge project managers face is trying making results happen quickly, especially if we are coming into a programme project in a troubleshooting role.  

Once a project is underway, it can be challenging to devote time to adjusting processes and practices. Engaging a data specialist early and defining project-specific analytics outputs will allow for faster and more effective implementation of data-driven solutions. Stakeholders can expect more informed decision-making, increased efficiency, and better team integration from the outset – leading to improved outcomes and successful delivery.