by Amy L. Affelt
Information Today, Inc., 2015, 222 pp.
This book demonstrates how librarians can compete and fight back in the digital world, using traditional librarianship skills. In particular, it suggests ways to apply these same skills to add value to data extracted from big data sets.
Affelt defines ‘big data’, a term first used by computer code writers and mainframe network administrators, then popularised by journalists world-wide. Since 2011 its growth has been exponential, but few people really understand what it is. It refers to large data sets that cannot be handled by normal D/P software tools. These data sets are captured, managed and analysed, for use in private, public and non-profit organizations to provide predictive insights, but its real value is often over-looked. To date, mainly data scientists are employed to analyse these resources.
Based on research from various studies, Affelt compares and contrasts the roles of librarians and data scientists, and makes a case for integrating their roles. Librarians analyse data in the context of past activities using verification skills, whilst big data views it from the future. The latter is tabulated in real time, quantified, evaluated, and used to make various predictions; thus, data is characterized by volume, velocity and variety, but not quality and value. By targeting the last two, librarians can show they know more than just finding information, and how to make sense of it. Instead, they need to market themselves as instruments of big data. Offering deliverables, and not just explaining databases, by emphasising that relevance, timeliness and accuracy of information are more important than process or methodology.
In the big data world, organisations use predictive modelling to monitor customer’s needs. Algorithms used are limited, as data is not compared with other sources to present a true picture. Skills exist to write computer algorithms, perform statistical analysis, and forecasting, but they are weaker on predictions, as data alone has no context. Here, librarians have the unique skills to extract narratives, and uncover causes and contexts surrounding a dataset.
In the current scenario, big data appears to be valuable when used to analyse patterns and trends of behaviour, rather than to predict specific events or occurrences. Librarians have ready skills in analysis, and creation of knowledge deliverables, but they need to be embedded in organizations and be part of data science teams to influence thinking.
Affelt’s own tool is a template for analysing data in the context of research challenges, followed by case studies from different projects to show how basic data can be used to address complex problems. She concludes with a plea for a shift in thinking so that we train a new breed of librarians, skilled to fill the role of data scientists for the future. A challenging and exciting read for librarians.