Feeding big data innovations in rail

It is no secret that data is becoming an increasingly important commodity. Across all industries, companies are looking for ways to collect and analyse data to improve their performance. In the rail industry, big data plays an increasingly important role in helping to drive innovation and digitisation progress. In this article, we will explore the correlation of big data employment roles within the rail industry and the impact on innovation and digitisation progress. We will also discuss some key challenges railway professionals face regarding big data.

Europe is seeing a hiring jump in railway industry big data roles. Some parts of the world invest more heavily in big data roles than others. The global big data market is expected to grow from $42.50 billion in 2018 to $103.44 billion by 2027, at a Compound Annual Growth Rate (CAGR) of 11.0% during the forecast period. The Americas are anticipated to be the largest revenue-generating region in the global big data market, followed by Europe, Asia Pacific (APAC), and the Middle East & Africa (MEA).

The railway industry is under pressure to improve operational efficiency and reduce costs to compete with other modes of transport. Big data can help railways achieve these goals by providing insight into asset utilisation, energy consumption and maintenance requirements.

It’s one thing to recognise that big data initiatives and the employment rates of big data professionals are increasingly important and receiving more investment. It is critical, though, to any big data program that it is recognised that no matter how many big data scientists you have (even if they are the best in the world) if you can supply high-quality, high-frequency structured asset information, the return on investment will never be realised.

“Feeding the data beast” is now one of the most significant ways to realise gains in efficiency, safety and cost reduction for railways. Big data can no longer be an afterthought or something “nice to have” but should be at the forefront of every railway’s mind.

Some of the critical challenges facing railway professionals when it comes to big data include:

  • Ensuring data quality
  • Data context and relvancy
  • Data consistency and frequency
  • Data governance and accountability
  • Data security across the enterprise, government and SME value chain

We will cover in a bit more detail the critical challenges, specifically with a focus on Cordel’s domain of speciality. Cordel is the global leader in big data capture from vehicle-mounted edge sensors, edge and cloud data processing and network-wide AI-powered analysis and reporting.

Using our proprietary big data platform, AI/ML processing and unrivaled volumes of high-quality training data and models, Cordel can deploy complete end-to-end condition inspections and monitoring tasks more frequently, more accurately and at a lower cost per inspection. This results in exponential gains in data quality, consistency and frequency, equating to high-quality data for decision-making ability, proactive quantifiable maintenance activities, and an overall improvement in asset performance.

Ensuring data quality

For big data analytics to be effective, the data must be high quality. This can be a challenge in the railway industry due to rail assets’ diverse and vast, distributed nature. Big data initiatives must focus intensely on data quality to ensure that the collected data is fit for its purpose and that data collection can be closely aligned to quantifiable business pains and gains.

Data context and relevancy

Trying to comprehend what the numbers mean when presented in a new way can be difficult, and often misinterpretation occurs. This can have negative consequences. I’ve seen data scientists/analysts who didn’t understand reports on geometry, weather conditions, tonnage, etc., and rail workers who were frustrated because they couldn’t explain the information properly.

Big data consistency and frequency

Another challenge big data railway professionals face is ensuring data consistency, especially when working with low frequency or low accuracy data points. This is often because different parts of the railway network use different systems, sensors and even reporting systems, making it challenging to obtain accurate, up-to-date and consistent data that are fit for purpose. Also, a critical point in ensuring consistency is being able to control and optimize the frequency of data capture, because it really doesn’t matter how accurate, or consistent your data is if it’s 4 years old, would you trust it to run critical infrastructure?

Big data governance

There must be transparent governance around who has access to what data and how it can be used. This is particularly important in the railway industry, where safety is a key concern. Big data governance should also consider the stakeholders involved in the railway network, such as operators, maintenance providers and infrastructure managers. The governance processes and accountability is paramount for the success of programs like this to be successful.

Data security

Big data analytics often involves the use of sensitive data. This can pose a security risk if the data is not adequately protected. Railway professionals must consider protecting data when designing big data initiatives and even more so when multiple stakeholders from different divisions and enterprises are involved in generating value. Rigorous governance and data security process will assist here.

There’s no point in having a great team of data scientists if there’s no overarching strategy to increase the frequency and quality of asset information collection and if there isn’t a process to turn those insights into action. Significant data initiatives are only as good as the team that’s running the data beast and the input data that’s feeding it.

Better quality and higher frequency enable more data points that data professionals can use to make better decisions, better analysis and trending, and generally provide better customer service. Big data is not a silver bullet, but it’s a powerful tool that, when used correctly, can significantly impact innovation and digitisation progress.

Prediction of conditions over time and the deterioration rates and other external data points (like weather, traffic and weights) can enable optimal intervention points to be determined, resulting in less unplanned downtime, lower maintenance costs and improved service levels. Big data can help us get there.

Despite these challenges, big data analytics can offer many benefits to the railway industry. Big data analytics can help railways improve operational efficiency, reduce costs, and improve safety when used effectively. Big data can also help railways understand better and predict and recommend optimal maintenance tasks and intervention points.

But we need to ensure that we have the right team to make it happen. Do you have what it takes to join the big data revolution in rail? The Cordel team are big data experts in the railway industry; with our team of experienced professionals and class-leading technology infrastructure, we can assist you in your next big data project and help “feed the beast”.

Big data is not a quick fix; it’s an ongoing journey.

Big data can help railways to achieve many things, but it’s important to remember that it’s not a quick fix. It’s an ongoing journey that requires investment, commitment and the right team to make it a success. With the right approach, big data can help railways to improve efficiency, reduce costs and become more competitive.

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