The advancement of technology used in everyday life occurs at a tremendous rate. Now take that speed of change and apply it to the laboratory. Keeping pace is a challenge for anyone creating informatics for scientists, researchers, engineers and analysts. This is particularly hard with expectations for IT and infrastructure systems higher than ever before. We see this first hand: one of the key drivers for our customers across market verticals is to improve efficiency and throughput, and the way they use our technology in the lab makes a real impact. Essentially, automation is more often than not a positive thing, but it mustn’t come at the expense of usability, scalability and an environment people can really work in.
The best technology for the lab is simple to use and implemented without requiring a great deal of training. Those needs are a constant: from the lab of yesterday through to the lab of tomorrow. The quicker scientists and researchers can reap the benefits of the ‘new’ system – be it software, hardware or both – the better.
The ability to integrate different domains will remain key. Science in the future will be about cross-disciplinary teams and be reliant on working efficiently and effectively together. A biologist might need to integrate work with a chemist, with microbiologist scientists, with a process scientist, with a manufacturing engineer and so on. Looking ahead, it is about being connected, with data properly integrated, so that scientists can drive decisions, research and technology forward.
Next generation ELNs will play an important role in aggregating data, both historical and real-time. The ELNs will also be used to provide direct feedback to scientists in real time. Needless repetition of work will become a thing of the past, as systems will be there to explain: ‘Someone else has already done this. Here are the results they got and here are 10 similar experiments they tried’. This transition, from IP protection to IP leveraging or consumption, will be a very subtle but very significant step forward for research and development (R&D) driven organizations. There is an automation element to this but, crucially, it is also about using existing knowledge and information to make better decisions in the lab that exist centrally – not just in someone’s head.
The temptation for many is to embrace new technologies of the moment, and then think how to apply them to the existing platforms that support scientists and R&D teams use. Instead, progress should center on what users are doing right now in their day-to-day work, and how technology can add value. This philosophy isn’t new! It’s a pragmatic view, but it’s a strong foundation which helped us shape the lab of yesterday and today. If we can combine that process with the knowledge of today and technology of tomorrow, the lab of the future will be in very good hands.