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Four challenges facing the future of scientific computing

As Scientific Computing World marks its 20th anniversary and IDBS celebrates 25 years of creating advanced software for research and development (R&D), we are braced for change. Businesses are on the brink of significant disruption, with four key forces set to reshape the world of scientific informatics.

The social network

Millennials and Gen Z will represent 40 per cent of the US working population by 2020. This cohort has never known a world without the internet and will be more technology-savvy than any previous working generation. This will drive a seismic upshift in expectations when it comes to technology at work. Scientific computing will be no exception.
Employees will expect more efficient and effective technology to create and develop products that can change the world. They will instigate a shift from work/life ‘balance’ to work/life ‘integration’. Mobile and remote access to scientific informatics will be more important than ever before. These new expectations, and subsequent challenges, will be at the forefront of new thinking as software evolves over the next decade and beyond.

Technology is ready to face this new world. It has the power to change the way we interact with the world. It enables us to create, access, and share an immense amount of data. This should allow researchers to make better decisions. Technology will continue to drive scientific advances and the development of products that have a positive impact on R&D, from social networking to mobile and cloud.

Working outside your walls

Recent research by Ovum highlights that externalisation of what were previously in-house capabilities is on the rise across the globe. Firms must look at how they can complete work outside the walls of their own organisation without compromising data quality and security. Much of the responsibility here falls to these external connections. Technology will prove key in generating actionable scientific results, wherever the data originates, in the right place and in the right format.

Data inundation

Another key force in this revolution is data. Facilitated by the increasing entrenchment of technology in everyday life, the world has surrounded itself with mountains of information. The amount of technical data we create is now doubling annually. Simply finding a way to store that data will be a challenge for R&D teams, as will quantifying, accessing, and managing it.
Separating ‘good’ data from ‘bad’ will always be the first step in building an R&D value chain. For example, questions were recently raised about the possible efficacy of scientific insights gained from Twitter’s data. A compelling thought, but one that holds little value until there is a way of verifying the accuracy and quality of the data, let alone the technology to analyse it. The potential that ‘big data’ promises will be fulfilled only if the technology is there to harness it.
Teams must also understand the importance of having a data strategy. Data cleansing and enrichment will become more important. Scientific computing will play a key role in connecting data to data, data to people and people to people.

Cost vs productivity

Cost vs productivity will continue to be a huge challenge for life sciences. As Michael Elliott of Atrium Research noted recently, R&D spend in many parts of the world has hit a plateau. Battelle research suggests that tepid economic recovery in Europe and the US means imminent increases in R&D investments are unlikely. Meanwhile, R&D spend in Asia will soon overtake Europe and North America, representing a global swing. This, coupled with the continued globalisation of R&D, will place a strain on technology regarding issues such as collaboration, data security, and IP management.

R&D productivity is down. In addition, figures show that clinical development timescales are increasing, and the cost of R&D is growing. Organisations must leverage technology to improve success rates and improve profitability as a result.

Looking to the future

The future for scientific computing is at once uncertain and challenging but it is also promising and full of possibilities. Its importance will become even greater, with the next generation likely to be even more willing to use technology to enable science. If these forces can be harnessed positively, a new wave of innovation and world-changing developments are in store.


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