Setting ourselves up to maximise data assets

Our daily lives are increasingly driven by data. It has become a defining factor in how we live. As consumers of data-driven systems at home, we are exposed to more and more complexly integrated systems. We have our heart rates on our watches and algorithms telling us what to watch on television. In the workplace we use visualisation platforms and benchmarking to understand our businesses and tools to track our commercial performance.  

With more data at our fingertips than ever before, the challenge is often understanding what data we need, how to get it at the right time, and how to best use it. Infrastructure is no exception. If the sector is to meet the considerable demand and productivity challenges we face in the coming years, we need to treat data as an asset that brings tangible value throughout a programme life cycle.  

Data is often considered a product resulting from the rest of a programme. As such, effort is typically focused on the systems of recording and reporting data with considerably less time spent thinking about how we govern, collect, store and integrate data and how this will need to evolve over time. We must pivot our mindset and start approaching data as technology companies do – seeing it as a valuable asset at the heart of a programme. 

As with any other asset of an infrastructure programme, getting value out of the data at our disposal relies on setting up a strategy for success. Right from the outset of a project, we should be thinking through the whole asset lifecycle and asking themselves three important questions:

1. Are we taking a pragmatic approach to our data strategy? 

Getting the approach to data right relies on being realistic about what is achievable and proportionate to the programme. Establishing a good process which is deliverable and whereby teams are contributing what is needed can often be preferable to setting a high bar which is at risk of not being met.

Perfect is the enemy of good and, in aiming for a perfect model, confidence in the data itself can be significantly eroded by missed targets and aspirations. 

Having set out the principle that data strategy needs to be in place from the start, we need to acknowledge this isn’t always the case. Having a vision for data at the beginning of a large major programme is difficult. What a programme needs from its data will change and we should expect this to be the case.

While we typically have long-term visibility of infrastructure pipelines, we don’t have the same luxury with technology. We only have to look at artificial intelligence (AI) and large language models such as ChatGPT as the most recent example of a technological breakthrough changing the rules in ways we don’t yet fully grasp.

It is natural, therefore, that there will be occasions when a data model or system upgrade needs to be retrofitted to enhance an existing enterprise or programme creating extra dependencies and complexity.  

What’s important is approaching these situations pragmatically with the initial data strategy front of mind, while understanding that it will need to be reviewed and adapted.

Prioritising the information required to make decisions in the near term, while following a realistic, long-term plan to develop digital maturity gradually and add value in the longer term, will be crucial to leveraging data as a valuable asset.  

2. Are we building a data-driven culture?

Establishing a culture that values the process of data collection, integration and usage is essential. To do this, we need to understand the current intricacies of how we interact with our data and how teams are approaching, collecting and using data points day-to-day.

Doing so will help define how a governance framework can be built to best serve users across the programme.

Embedding a model that engages data users and drives accountability then relies on setting out clear ownership, of both the system of data collection and actual data. This relies on good governance: ensuring processes are in place and there is a clear understanding of how the data will flow through systems, and echo ways of working.

This is crucial to minimising data discrepancies in the system and providing confidence in the data. If inconsistencies do arise, well-defined processes and ownership will also help to identify the single point of truth which should steer decision making.

3. How well are we maximising the true value of our data assets? 

The final consideration when collecting data is the tangible value it will offer within a programme. Attributing a price tag to data is much talked about but, in practice, is difficult to pin down. The traditional approach to defining the value of our data processes is centred around optimising performance on the original project itself in order to gain value by saving time or cost and improving outcomes.  

These gains are of course important, but they shouldn’t be the only markers of success.  

The second, more valuable outcome, is how this data can then support programmes in the longer-term. Whether it is a major rail network electrification project or a complex airport expansion, consistent, timely and benchmarked data can help carry through lessons to inform decision-making and enhance delivery both now and in the future.  

As an industry we should be considering how we can pool knowledge and data to benefit not just individual projects, but also the industry at large.

We need to strive towards an environment where data from major infrastructure programmes can become open-sourced and available for others to use. Some strides are being made, such as The Water Regulation Authority’s (Ofwat) ‘Open data in the water industry’ initiative to improve best practice and guide insight within the sector. 

There are significant, and at times competing, demands on infrastructure over the coming decade. A more open, cooperative approach to data collection and sharing will help the sector to meet those challenges, driving better outcomes across the board – for both the industry and our communities.