In this digital era, the power of data is unquestionable, but it has to be good quality data to be valuable. Industries ranging from consumer goods to food and drink to pharma and biotech are dependent on the quality and context of their data: this is the lifeblood of R&D. Historically there have been a number of challenges in structuring, managing, securing and normalizing the processes around R&D’s “scientifically demanding” data.
The key to overcoming this lies in understanding the ‘data value chain’, and then making the most of technology to enhance and optimize it. The bulk of what R&D researchers in a given scientific domain do is very similar across all market verticals – it’s the language of how they describe their work that typically varies, not what they do. The commonality?
The concept of a data value chain applies to all of them. The output of this chain impacts the key common drivers for R&D – accelerated innovation, improved margins and getting products to market faster. It is possible to affect the common drivers through technology which offers one or a combination of:
- Searchable and actionable knowledge base
- Increased data security and IP protection
- A platform that enables internal and external collaboration and efficient reporting
Collaboration is critical even if it’s sometimes challenging, whether it being between different organizations or within a single organization. We often see issues around trusting the data someone else has produced, which can result in costly (in both time and money) repetition of work.
Another element is the common frustration with not knowing what is going on, where the data is, and how far along the process we all are. Technology around the R&D data value chain can repair the broken links and provide visibility of where in the process you are. If done properly it can guarantee accurate and trustworthy data. It must also document the background to the work that is producing the data – this is hugely valuable as it provides the critical context to results.
Organizations looking to improve their R&D data value chain need to be able to deal with change. Those that can adopt a more dynamic concept of collaboration and a defined set of rules enabling flexibility, will truly reap the benefits.
Building and then connecting the constituent parts of an R&D data value chain means organizations can make the most of their R&D and collaborative efforts whilst also focusing on efficiency. Data and process modelling across the various domains that are collaborating in the enterprise opens a new world of possibilities – connecting data to data, data to people and people to people.