Smart Data, Smart Library: Assessing Implied Value through Big Data

The growing expenditure on electronic resources has become a new norm for academic libraries. It is crucial for library administration to measure the impact of such investment consistently and persistently, and then develop collection strategies. Big data technology provides such an arena for management to gain insights through meaningful data and allow libraries to optimize collection operations in real time. The purpose of this study is to assess the implied value of a research library by analyzing Cost per Use with BigQuery a cloud-based data warehouse. The authors developed a systematic approach to process structured data including e-resource usage and interlibrary loan transactions, and then analyzed the data in BigQuery. Google Data Studio was employed to visualize the results. The findings of this study not only manifest the implied and exchange values of the research library but also offer an innovative approach to predict the future collection needs. The methodology employed in the study also provides a new opportunity for libraries to adopt big data technology and artificial intelligence to tackle intricate problems and make smart and informed decisions in this big data era. Introduction Academic libraries have been working tirelessly to sustain library collections to meet the needs of teaching and research. However, the exponential cost increase of electronic resources has surpassed what the library budget can afford. Meanwhile, the open-access movement has made more and more scholarly publications freely available to the public. It is impossible to develop an effective collection strategy without assessing the values that library collections have brought to users. The impact of electronic resources on teaching and research has changed the landscape of collection development. For example, 89% of the overall use of library collections at the University of Massachusetts Amherst happens outside of the library buildings and 53% of the use is to support teaching or class work. In STEM disciplines, 92% of use by graduate engineering students also occurs outside of the libraries and 45% of the use is for their theses or dissertations. Academic libraries have met these requirements by subscribing to electronic resources and creating digital collections over the last decade. Researchers and students have been enjoying convenient access to these rich and diverse resources. However, with the growth of digital resources, subscriptions to online resources have become the primary consumption of the collection budget. It has never been so crucial for libraries to examine their collection strategies critically and seek solutions to this challenge which libraries face today and tomorrow. Measurement It has been 16 years since the COUNTER initiative was launched. The COUNTER Code of Practice provides a mechanism for libraries and publishers to gather usage statistics consistently across publishers and libraries. One of the metrics listed in the code is Journal Report 1 (JR1) which is the number of successful full-text articles requested by month and journal title. Therefore, it is possible to calculate the Cost per Use (CPU) with JR1 and journal cost. CPU is a widely accepted criterion to assess electronic journal subscriptions, but is by no means the only metric for libraries to adopt. Every library is different and unique. Libraries must conduct assessment and interpret findings in their respective contexts. It is complicated to measure the value that an academic library brings to the university or college. Student success might be the results of contributions from many campus constituents. Studies have been conducted