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5 Predictions on the Future of R&D
Industry Insight

5 Predictions on the Future of R&D

5 Predictions on the Future of R&D
Industry Insight

5 Predictions on the Future of R&D


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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. At ACD/Labs, we are continually refining and producing innovative software based on the latest technology and trends to ensure scientists are successful in the lab and able to rely on data-driven decisions when addressing R&D challenges. Based on what we’ve seen from the evolution of the R&D industry thus far, here are five predictions on what we may see in the future:

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.

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