As a source of data to support greater efficiency and cost-savings at a micro and macro level, projects are definitely underutilised. In many instances, this can be due to the ‘black box’ effect coming from traditional methods of recording and retrieving project data. When information is stored across multiple spreadsheets, or even in hard copy on white boards and post-it notes, it’s just too hard to collect and compile all these inputs, let alone put them through the next four phases of the data workflow – review & selection, normalisation & ‘cleaning’ of data, analysis and – finally –making an operational change informed by data insights.

Andrew Thomlinson-Munn

With the rise of the ePMO and PPM products, enterprise organisations are being handed a new opportunity to gain visibility of their project-related data. But bringing the benefits of data insights to projects – and other areas of operation – takes more than getting stuck into data sets and dashboards from your PPM. It definitely takes support from data experts to manage that five step workflow. But it also introduces some interesting challenges that our Hummingbird team have seen as we’ve helped our clients develop their capacity and skill to use their project data to make efficiency and performance improvements.

Here are three important insights that can help organisations to better understand their project data with the ultimate aim of delivering value:

These days there are few organisations without a data strategy. For organisations to use data from their CRM to inform a better sales journey and from their sales and customer services channels to support an enhanced customer experience is considered fundamental for running a successful operation. But just as PPM technology is only now starting to catch on, it’s taking time for organisations to grasp the possibilities for data to improve project efficiency and outcomes.

Just like the funnel from awareness to sale to advocacy that we see with the marketing journey, the five phases of the data workflow also act as a funnel. And to get more value at the pointy end – making data-informed operational changes – you need to be putting more in at the start of the process. Unfortunately for ePMOs, your typical project lifecycle doesn’t deliver the data lakes that organisations have come to expect from their customer or supply chain sources. Instead they’re dealing with a data drought.

To open up your ePMO to the project data that’s relevant can take a little detective work. Having a PPM system to gather and collate the data is a good start but you need to either set up systems to collect the data or find the ones that matter and integrate these with your PPM.

This leads us to the question of what data can be collected that’s going to be meaningful. It’s often the case that project teams don’t start collecting data until project execution begins and this overlooks data from the demand intake stage which can be critical in understanding why projects succeed or fail. One example that can make a significant positive impact on project monitoring and compliance is collecting essential data at the contract phase and using this to track milestones, scope, schedule and other project obligations across suppliers.

There isn’t a one-size fits all for project data that’s beneficial across different industries or operational models. There will be some commonalities ePMOs can learn from their industry peers and this is certainly an area where Hummingbird can help organisations to identify valuable data that might be less obvious. This might be data that comes from working with many different contractors or deploying project deliverables across multiple sites. We’ve worked with other organisations running projects under the same circumstances and this gives us a head start in understanding what matters most from a particular data pipeline. Not only can we pinpoint these potentially valuable data sources, we can help design reliable processes for collection of this data.

When we describe the data workflow as a funnel, it’s a reminder that making a data-informed change to how projects run can’t happen on day one of a new data collection cycle. Data must be gathered over time before it can be analysed and deliver an insight that is meaningful. This is particularly the case when you have projects running across multiple timelines. Depending on the length of different project lifecycles it could be several years before you have the data needed to challenge the status quo with confidence and make an effective change.

While this might sound like treading water, it doesn’t mean you’re not getting value from all that data in the meantime. Reviewing and analysing data you already have can help surface new data sources that can help confirm or bring more depth to this early analysis. Plus you’ll learn whether the data you’re already collecting is relevant, and whether processes for collection are as effective as they can be. This all helps to fill the data funnel with quality input so the eventual output can be useful and trusted.

It might seem that making an investment in mining data from projects could test the patience of stakeholders looking for quick wins. But even limited data on a handful of projects can lead to short-terms gains on the next project and the next. Each data point can contribute to insights that increase the chances of a successful project. By looking at the data behind projects executed on time comparing with those with significant schedule creep, we can look at commonalities to identify risks early on.

These patterns and insights can lead to more effective changes in documentation and resource management to drive better project outcomes. As well as pushing these realisations across multiple projects, these incremental data points and changes can lay the foundations for analysis that takes a more holistic view of projects. This can, in turn, support ongoing refinement for methodologies and cadence, driving ever greater efficiency and performance from projects and resources.

Over the longer term, this drive for efficiency and performance can inform realistic benchmarks for contract negotiations. More and more we’re seeing projects move away from the time and materials cost model and towards defined outcomes. Project expectations informed by data can help both sides come to an agreement on contract terms and better manage costs and risks during execution.

Data insights for improving project outcomes and performance are just one of the superpowers of project success that an ePMO and PPM can deliver. Collaboration is another and there are a host of benefits organisations can enjoy when data insights are shared with other business units or support teams. Data points from projects can provide vital information on resourcing and schedules for all sorts of functions from technology to logistics. This takes an organisation’s data analytics to a new level of maturity that can deliver additional value from the project data funnel without an extra outlay of effort or cost.