'Big data' patentometrics for R&D decision-making. Issue 4 (9th July 2021)

• Record Type: Journal Article
• Authors: Verma, Charu; Suri, Pradeep Kumar
• Is Part Of: Digital policy, regulation and governance. Volume 23:Issue 4(2021)
• Page Start: 317
• Page End: 336
• Publication Date: 2021-07-09
• Subjects: R&D management -- Big data -- Patentometrics -- Patentometry -- R&D decision-making; Telecommunication -- Economic aspects -- Periodicals; 384.04105
• DOI: 10.1108/DPRG-09-2020-0126
• Languages: English
• ISSNs: 2398-5038

Abstract

Abstract : Purpose: The purpose of this paper is to highlight the use of big data through patentometric insights for R&D decision-making. Design/methodology/approach: This study assesses the inventive activity through 'big data' patents, registered by inventors worldwide, using WIPO Patentscope database. The objective is to use the insights from patentometrics for R&D decision-making. The data from WIPO PatentScope (https://patentscope.wipo.int/search/en/search.jsf ) was searched for current patent scenario in area of 'big data'. The data was further organized and cleaned using the Google 'OpenRefine'. Data was pre-processed to remove all null values. Cleaned data was analyzed using programming language 'R', MS Excel (charts and Pivot tables) and free data visualization tool called 'Tableau Public', to get insights for R&D decision-making. Findings: The key insights included trends (patents with years of publication), top technologies trending the current space, top organizations leading in these technologies and the top inventors who are publishing patents in these technologies through leading organizations were drawn. Details in Section 5 in the paper. Research limitations/implications: Global patent data is multi-lingual and spreads across a set of multiple databases. Domain experts may be required to assess, identify and extract the relevant information for analysis and visualization of multi-lingual distributed data sets. Government organizations generally have multi-dimensional goals that may be more toward societal benefits. On the other hand, the commercial companies are more focused on profit. Therefore, the performance management process has to be really effective because it is critical for getting value in the government sector. Practical implications: Insights from patent analytics serve as the important input to R&D managers as well as policymakers to assess the global needs to plan the national orientation according to the global market. This will help further for R&D projects prioritization, planning, budget allocations, human capital planning and other gamut of R&D management and decision-making. Social implications: Facilitation for R&D institutions (government as well as private) to formulate the research strategy for the domains or research areas to delve into. R&D decisions will be completely data-driven making them more accurate, reliable, valid and informed. These insights are very relevant for policymakers as well to facilitate the need assessment to determine the National priorities, make improvements in meeting societal country-level challenges during the resource allocation at top and subsequently at all other levels. Originality/value: Data analytics of global patents in "big data" till 2019 to get insights to facilitate R&D decision-making.