Oakland Group

The Evolution of Project Analytics

Welcome to the first in a five-part series examining the fast-changing world of Project Analytics and its vital role in the major projects sector.

This first article explores how the major projects sector is responding to a need for more intelligent project analytics. We explore how recent advances are beginning to dramatically improve the decision-making ability of major projects.

Where are we today with major projects reporting?

Today, major projects get delivered with the help of a wide range of project management, reporting and coordination systems.

This collection of software tools have one thing in common – they create large amounts of disparate data that mostly provides reporting on the ‘here and now’.

Although these systems contain data relating to the same or similar projects, there is a lack of data integration between project applications. Large projects create disconnected ‘islands of data’ that make it hard to convert the raw data into actionable intelligence, particularly for trend analytics and forecasting.

Another challenge project teams face is a lack of standardisation for key terms and data definitions. For example, trying to create a report on a common term such as ‘profit’ across a portfolio of projects can open up a can of worms when each project has a slightly different profit calculation.

Many organisations are striving to make data sharing a priority across their projects, and introduce more advanced data analytics tools, such as Power BI, for cross-project analysis, but despite the desire to move forward, there are still major obstacles to overcome.

Due to the nature of these project ‘data silos’, creating project analytics has always required considerable manual effort and a great deal of technical skill to pull data together for meaningful decision-making. As a result, most projects are forced to rely on the standard reports from their existing project tools, or whatever analysis they can pull together with Excel.

Traditionally, these standard reports have satisfied the needs of project stakeholders. But stakeholder demands are changing, driving the need for more intelligent project analytics.

Why do we need more intelligent project analytics?

With the amount of money and risk tied up in major projects, stakeholders clearly need more intelligence from their project analytics to help make smarter decisions.

As a result of the challenges discussed earlier, it’s fair to say that major project analytics has lagged behind the mainstream data analytics innovations we’ve observed in other sectors.

For example, at The Oakland Group, we’ve been deploying advanced data analytics across many industries for over twenty years so it was only a matter of time before the information locked away in major projects initiatives would begin to fall under the analytics spotlight.

But what can we do with better data analytics, and how will this benefit those endeavouring to deliver more successful project outcomes?

Here are some typical use cases:

Improving project analytics presents obvious challenges, but as you’ll learn in this series, these challenges are starting to be overcome within a growing number of UK organisations.

Advice for those starting their project analytics journey

Throughout this four-part series, we’re going to introduce practical examples and stories of project analytics success. For those interested in embarking on this journey, here are some pointers we can share right now that will act as your project analytics ‘North Star’.

#1: Begin with the end in mind

Instead of ‘shooting for the moon’, strive to deliver something achievable that satisfies some of the most pressing use cases – those that typically fulfil a need for improved efficiency.

Obvious candidates here will focus on use cases such as:

#2: Develop an end-to-end appreciation of the journey

It’s tempting to aim for a single, technical ‘silver bullet’ that provides a tactical fix, but it’s only by taking a holistic view that you reap the rewards of project analytics.

Improve your raw material: On major projects, staff typically have to update multiple systems and are often not fully briefed or incentivised on the importance of entering good quality data at source.

An end-to-end focus must begin with your sources of data. Project staff need the right training, support and tools, to record and update data quickly and accurately. If the raw material lacks quality, the output from your project analytics efforts won’t be trusted.

Deliver real-world impact: Your project analytics initiative has to create real-world outcomes that stakeholders and project staff value. The goal is not to develop colourful dashboards but to solve real problems.

When looking at the end to end processes required to deliver a project analytics capability, start with the most valuable linkages between systems (e.g. projects and maintenance) so you can provide some immediate, short-term benefits.

Consistently measure and communicate the benefits your initiative is delivering to help grow the momentum for change.

#3: Assemble the skills required

We occasionally see organisations hiring a data scientist in a bid to ‘fix the project reporting problem’. Whilst data scientists add value, they can’t deliver on the goal of project analytics single-handedly.

You need a specialist team, that typically requires:

You need to create a coalition of experienced staff and senior management, all working towards a common goal of translating your vision for project analytics into reality.

#4: Deliver value quickly to reinforce the business case

Building the next generation of project analytics is a considerable undertaking. However, most stakeholders will not sanction a three-year analytics project with some nebulous offer of a reward in the future.

You therefore need to balance a need for the appropriate frameworks, technologies and controls, with the demand for delivering some short-term value. Otherwise, your business case will soon run out of steam.

For example, you’ve no doubt witnessed projects that have gradually moved from a status of green to amber then eventually red, but what if you could have predicted that trend much earlier?

What if you could reduce risk across an entire portfolio of projects without requiring weeks of manual labour and ‘Excel-hell’ to spot the project warning signs?

Your organisation may have a desire to improve material or workforce efficiency, but whatever the focal point, try and achieve some small wins that attract early plaudits and approval.

What next in this series?

Coming up in the next article, we’re going to discuss how to ‘Know Your Data’ when it comes to project analytics.

We’re going to take a deeper look at where organisations go wrong when assembling a project analytics team and how they can start building some practical uses cases that help create early traction.