Enterprises recognise the need to move on from reporting-centric, retrospective business management to real-time decision making. The challenge to achieving the transformation is one of data access and security. Currently, data is hidden in silos, often obscured by legacy technology and fragmented across departments and supply chains. Traditionally, solving this problem focused on centralising information into ever greater data stores, such as data lakes, data caves, data platforms – take your pick. But with the burden of storing vast quantities of data and the complications of transferring and duplicating the data, this isn’t the solution.
Security fears prevent data interactions across corporate boundaries and fishing for insights in vast data lakes is no more fruitful. In order to make the most informed decisions, to fully utilise the data we already have, data must be able to interact. Companies need to ensure that they are looking at meaningful events and information across their ecosystem, in context and considered together with all related factors.
The rise of streaming dynamic data is progress, but it still needs to interact with other data. To be described, enriched and contextualised, we need data about our data, the metadata, to enable our systems to communicate with each other. The FAIR data principles (Findability, Actionability, Interoperability and Reusability) are a practical approach to enabling this. FAIR allows us to build the data interactions that not only increase the value of data assets, but also multiply the value of the assets the data represent. Overlaying data with descriptive metadata gives it meaning that other machines can understand, while semantic web technologies, such as Resource Description Framework (RDF), allow intelligence to be embedded within the data itself.
Taking a fresh look
Contrary to popular belief, the challenge to most businesses isn’t technology or budget, but mindset. We have the data we need and we have the technologies we need; we just need a different approach to how we think about, model and understand our data systems and assets. Leveraging legacy technologies, by enabling data to interact, avoids the ‘rip and replace’ approaches that are frankly not an option during the long-tail financial and sociological recovery from Covid-19.
Covid-19 has also shown us the importance of rapid flexible developments, delivered in weeks or months, not years. We all recognise that we need to prove and develop evolving approaches, while not focusing on fixed use, case point solutions that lack scalability, portability and viability.
This approach has already delivered results across the rail ecosystem, for example, via data interactions across partner networks. When key, meaningful, data is routed from the networks of data sources – both internal and external – silos are turned into a web of sources delivering actionable insights for multiple stakeholders across supply chains, clients and partners.
Rebuilding your data analytics capabilities in a post-Covid world
Back on track
Running and maintaining a train service is a major challenge, even more so during these unprecedented times. Using an overlay network and Digital Twin approach to enable data interactions between engine and power systems providers, train manufacturers and train operating companies, it is now possible to provide actionable insight into real-time divergence from planned operations.
It can and has been done. Rolls-Royce Power Systems created digital twins of their power packs, accessing data across many silos, improving customer service and providing a single access point for information. Digital twins communicate with digital twins of Hitachi trains, providing real-time insights into location, usage and performance of the whole train. Digital twins of powerpacks and trains, communicate with digital twins of train operating company schedules, the operational requirements, network infrastructure and combined maintenance priorities.
But this flexible, collaborative approach can also adapt to unexpected circumstances. For example, in the current pandemic the rail Digital Twin Ecosystem has helped to provide insights into the normalisation of rail passenger services and the implications of various pandemic risk mitigation strategies. It’s time to break down the silos and open up the data lakes so that data can interact, be fully utilised and deliver its real value.
