by Sanji Bhal, Director of Marketing & Communications (ACD/Labs)
The R&D industry has been evolving for decades to make the process of discovering new compounds and formulations in the laboratory easier and more effective. Today, innovative trends, focused predominantly around data and technology, encourage changes that aim to improve efficiencies across the industry. A couple of current trends we’re observing include open innovation (or externalization), as well as big data.
With other emerging trends on the horizon, guessing what’s next can help organizations enhance their informatics landscape to better help scientists move towards innovation and success. An example of that is our ACD/Spectrus Platform designed to help scientists assemble live data through effective analytical knowledge transfer across techniques and geographical locations. I decided to ask Graham McGibbon, Manager of Scientific Solutions and Strategic Partnerships, and Andrew Anderson, Vice President of Business Development, to share their top five predictions on what the future of R&D may look like and what changes we could experience.Here’s what they had to say:
- Searchable Data: Big data has led to seas of information that in turn have been difficult to sort through when trying to find a certain result or figure. Stores of analytical data, including interpretations and chemical context metadata, will look to searchable features in order to solve this problem. The ability to find and trace a piece of information will offer support to intelligent results assembly, data analytics for data science, and create better quality processes overall that will reduce risk.
- Cloud Storage and Larger Hard Drives: The growth of data and analytical knowledge will also require more storage space, no matter the technique or type of operation used. From accurate MS to High-Content Imaging and even modest laboratory operations, an increase in information will urge for the use of cloud storage and larger hard drives.
- Variation of Software: There is a plethora of different types of software and techniques available to assist scientists in analyzing their data. From commercial-off-the-shelf to completely custom software, and combinations in between. Decision makers in scientific organizations will have to make tactical choices about their approaches to analytical data and knowledge management to ensure business success in the future.
- Shift to “Data-Driven” Decision Making: The days of rigid information management systems and long analysis reports will be replaced by flexible decision support interfaces. To properly leverage R&D output in the future, leaders will start to transition decision making from documents to live data results. Interoperability will play a role in the way reporting is presented to stakeholders across the innovation lifestyle.
- Data Security Challenges in Global Ecosystems: As the industry moves towards open innovation ecosystems from a “core facility” model, the security of data exchange between organizations is an increasing concern. With global collaborations, the transfer of information across time zones, methods and other factors could compromise the data. Organizations will have to implement strategies to restrict access of data from unwanted third parties and increase security.
Do you agree or think other significant changes will occur within the industry? Comment below and let us know your thoughts. In time, we’ll see what the future of R&D brings!