Unlocking data in enterprise systems: a Network Rail case study

Unlocking data in enterprise systems can deliver terrific value, but it is a challenge. At Big Data LDN, Network Rail explained its transformation journey

Data is being generated at unprecedented and exponential levels. By all accounts, the data economy is booming, or is it? Unlocking data in enterprise systems and gaining value from it remains a significant challenge, but it can be overcome with “hard graft”, as Murray Leach — head of infrastructure projects systems and supports at Network Rail — explained during his presentation at Big Data LDN.

Network Rail is responsible for the infrastructure across the UK railway system. It spends £130 million every week on improvements for passengers and 22% of the UK’s entire infrastructure spend is delivered by Network Rail — it is Britain’s biggest builder, supporting 117,000 jobs throughout the supply chain.

The company, like so many, has amassed a huge amount of data. And by using it, “we can say what we did last month very accurately,” said Leach. “The problem is we’re not very good at using it to look into the future.”

Responding to the challenge, Network Rail’s infrastructure projects are on a journey to become more data-centric. Part of this journey was to develop a ‘single version of the truth’ based on reports and the organisation’s large data warehouse, which impacted three key areas; business operations (using data to make business work better), report consumers (allowing project managers to look at the relevant data and make decisions) and analytics experts (to help them do their job better).

There were some common pain points that hindered this process; siloed reporting, departments, regions and executives not singing from the same hymn sheet was the problem and the process was frustrated by the inconsistent use of data.

However, the start of the data journey did glean some results; “over the last three years we have been able to work in a more controlled and automated way and improve the quality of our data from 55% to 90%. Reporting, as well, has moved from Excel and Powerpoint, and is now more specific — it takes you down to source of data,” revealed Leach.

‘Simply’ consolidating Network Rail’s existing data for analysis was a start. But, “we wanted more,” continued Leach. And, by more, he meant the organisation wanted to unlock the value of data through machine learning (and artificial intelligence) and prescriptive analytics to predict the future — to understand where projects might succeed or fail, and what could be done to ensure the latter doesn’t happen.

Network Rail began the next phase of its data-centric journey by investigating it’s existing projects and “the information suggested there were kernels of good analysis,” according to Leach. From there, it moved to a proof-of-concept focusing on a machine learning approach.

“Critical outputs with significant data quality were used first; we cherry picked areas so that we could show the most immediate value, initially,” he explained.

But, even this approach encountered challenges.