When it comes to persuading the stakeholders on the trustworthiness of the R&D data sources, one must consider multiple aspects. Being transparent, carrying out data quality assessment, data management, communication with the stakeholders, all these factors enhance the trustworthiness of the data and make the research results more reliable. Also, note that data quality is not only ‘set and forget’ but rather a continuing process that should be monitored and improved over time. Thus, by addressing data quality and governance, the R&D capabilities would be enhanced which may translate to increased levels of innovativeness.

When I’ve faced skepticism about R&D data sources, the key has always been transparency and trust. Instead of jumping straight into technical details, I first address the concerns head-on. I explain how the data was collected, what tools and methods we used, and why we chose those specific sources. I’ve also found it helpful to involve stakeholders in the process, letting them see the validation steps for themselves. Whether through reproducibility tests or cross-referencing with other reliable sources. Being open and making the data accessible, usually clears up doubts and builds confidence in our approach.

Listen to the skepticism and concerns! Data sources, including R&D, can be problematic or not properly applied. Make sure in your enthusiasm you did not add assumptions that may have caused some of this skepticism. Double check everything.