Past Triggers for Data Insights
It is true that there are several emerging technologies for data analysis right now, but it really isn’t the case that we didn’t already appreciate the high value of data per se, or that we didn’t already undertake analyses using that data. In fact, we already have a very strong track record in identifying new questions and using data to explore them. Looking back at some of the triggers for recent data insights reveals important aspects of the ways we work and how our data insights add value.
Recent data insights have addressed a wide range of topics within rail. From depot safety, station safety, to objects on the line, track worker safety, or SPADs, our data insights have built on the industry’s pre-existing understanding of important factors, and taken new approaches to explore follow-on questions.
Follow-on questions can arise from operational experience or strategic need, or emerge from data analysis itself. Crucially however, whichever approach we have used, we have worked collaboratively with industry. Whether that’s via formal research partnerships or working groups focused on tackling a particular issue, we’ve facilitated collaboration so that the collection and analysis of data improves for the benefit of the whole industry.
Take the issue of ‘objects on the line’ for instance. The Train Accident Risk Group (TARG) already had questions about objects left on the line that were contributing to accidents, and therefore commissioned the University of Birmingham to undertake research to answer these questions. The team there used the data to categorise four types of objects on the line: vandalism, vehicles, animals, or objects left after maintenance work. Exploring relevant data about time of day, time of year, railway region, or animal type all helped generate a rich set of insights that enabled informed action by industry. For instance, data insights about the type of animals found on the line was revealing, because it also indicated whether there was a person or organisation that rail could work with to address this risk. You might not think it matters very much whether it’s sheep, cattle, or deer that are on the line. If they’re on or near the track, they’re in the wrong place and a potential accident risk, end of story. However, sheep and cattle are owned animals while the vast majority of deer are not. These two categories had very different incidence rates. 63% of animals found on the line were sheep or cattle, compared to 16% that were deer. They also required different risk management processes and stakeholder engagement. Further data analysis including the time of day and time of year when different animals were found on the line also improved understanding of what was happening and why. These data insights were a vital starting point for engaging with the appropriate stakeholders effectively.
Another area where our data insights have helped recently was track worker safety. This showed the collaborative approach to data gathering and analysis even more clearly. In order to help Network Rail and its contractors, we gathered data from our own SMIS, Network Rail’s own investigation reports, and reports of close calls to Network Rail. Using data from these three sources, our analysis was able to produce data insights that enriched understanding of the safety risks track workers face from moving trains. Our data insights were able to identify different sources of potential risk, including impeded safe walking routes and cess, difficulties in the processes for creating and using Safe System of Work (SSoW) documents, ways in which our own forms for digital data could be improved, and the potential use of geo-fencing technologies worn by track workers to help improve their situational awareness. In addition, these data insights were the basis for investments made by Network Rail to improve track worker safety, such as improving the cess and walking routes and changes to their accident investigation forms. Furthermore, since the data insights were produced, we have been working with Network Rail, the Infrastructure Safety Leadership Group, and other industry partners to improve the data that is collected and the way it is used. From this it’s clear that the data insights we have already produced were based on a collaborative approach to problem solving, and that they inform ongoing actions to improve safety.
It’s clear that a trusted and collaborative approach to data collection, sharing, and analysis is the vital starting point for our data insights. They have already added value to industry, and can inform further data insights too. Whatever the precise form of the methods used to produce data insights in the future, we’ll continue to work collaboratively to develop and apply them.
