Data Visualization and Data Granularity in Delivering Research Data in African Libraries

Introduction

The global per-capita capacity to generate data has increased in leaps and bounds in the last two decades. More technological platforms have emerged more than ever before, and the oil they run with is simply data. A cross knowledge from information science noted that data is at the bottom of all experience. As a matter of fact, the lower rung of the knowledge ladder is the Raw Data, it then goes through the transformation from being raw to becoming information to knowledge and then to wisdom. Based on this background, we can boldly say that the foundation of any decision that would be made by humans is a result of the Data that was at the beginning of the decision ladder.

Commenting on the right tools to manage data for the purpose of easy access and use, Panoho (2019) stated that data is one of the most valuable assets a business can have and potentially has a tremendous impact on its long-term success. That's why it's vital to utilize the right tools and technologies to fully leverage all available data and make it as accurate as possible. Data in itself is not meaningful and not useful until the right tools have been used in ensuring its quality, accuracy, arrangement, visualization and accessibility. Data management needs tools such as normalization tools, filing tools, accuracy and cleaning tools, storage tools and visualization tools.

The role of data visualization in the presentation of data is as important as the data content itself. A data user will not benefit from the input if he or she does not understand how to generate insight from the data. According to Marastats (2019), Data visualization is useful for data cleaning, exploring data structure, detecting outliers and unusual groups, identifying trends and clusters, spotting local patterns, evaluating modelling output, and presenting results. It is essential for exploratory data analysis and data mining to check data quality and to help analysts become familiar with the structure and features of the data before them. This is a part of data analysis that is underplayed in textbooks, yet ever-present in actual investigations.

Data is being used for different reasons and purposes across various industries. However, in academia, researchers, students and faculty are always in need of data to generate insights for research outputs and to validate their hypothesis. More often than not, one of the first places they turn to is the academic libraries in their various institutions. They expect to get the Data that will help them in their tasks in a presentable and understandable format, hence, the need for data visualization by academic libraries and academic librarians. The rapid development in technology and the massive growth in the world's capacity to generate data has made it required skill to be able to manage, curate and make sense from raw data. As managers of knowledge, Librarians are at the very center of managing and creating access to useful data for all kinds of organizational and societal decision making. Data is being presented with different innovative visualization tools these days and librarians are well-positioned to master the tools to help the library patrons better.

Given those as mentioned above, this chapter used a qualitative expository methodology in exploring the various skills and tools needed by African Librarians in delivering granular and visualized data to library patrons. Besides, this chapter emphasized the types of African data libraries are meant to be collecting. This will help the librarianship profession to begin to appreciate the concepts of open data, naked statistics, data granularity and data visualization.