R&D costs are increasing. In the pharma industry alone, the average cost of developing a new prescription medicine and getting it approved for market is estimated to be $2.6 billion.
Faced with prohibitive costs and lengthy timeframes – this process often takes over a decade – companies are naturally looking for better value. Many are turning to a data-driven discovery approach, where existing data is reused to address new problems and provide new insights
Capturing the right data for insights
For this approach to work, organizations need to produce, capture and record more data. Any information that could lead to an insight should be captured, and this means more than raw and processed data. Organizations need to capture context.
It sounds easy, right?
Unfortunately, it’s not that simple. The scale, diversity and complexity of the data being created within scientific R&D has dramatically increased over the years and this brings fresh data capture challenges.
Some organizations are turning to technology to help, but it can be hard to know where to start. There’s a whole host of solutions available across a number of sectors, many with ambiguous, overlapping, capabilities – just to add to the confusion.
A laboratory information management system (LIMS) may suit a QA lab, but a similar solution could be unsuitable for a research lab, where an electronic laboratory notebook (ELN) may seem more appropriate. With the possibility of having to contend with a range of systems to capture increasing amounts of data, scientists are understandably pushing back.
Providing context through integration
To gain insights, data needs to be both structured and connected, and this is only truly possible with integrated systems.
R&D organizations need a single platform that allows engineers and scientists to work seamlessly together – ensuring that data, and important contextual information, sit side-by-side.
Take inventory information, for example. Whether it’s the samples we are testing, or the lab materials and equipment we are using for tests, knowing this information provides us with an understanding of where data has come from. It helps bring it to life. With this added layer of contextual information, data becomes even more valuable, increasing the opportunities for data-driven discoveries.