Chapter overview

As our cultural heritage institutions find themselves collecting and storing more and more of the usage data created by patrons’ and users’ interactions with the systems and services of the library, archive, museum or gallery, so there is an increase in the potential to extract value from this largely unstructured data.

This chapter will use the concepts of ‘big’ and ‘small’ data as a way to explore some of the analytics developments in academic and cultural heritage institutions. Rather than simply assuming the promises of big data and the desire to collect and analyse more and more data, small data provides us with a balance, a refocus on the localized, contextual and manageable data and can provide a fertile environment for the development of data analysis at ever greater scale; from individual institutions to groups or entire sectors and from individual data sets through to multiple and disparate datasets.

The chapter provides an overview of two early-stage analytics developments that promise to show how we can use both small and big data to shape our services and user interactions with them, both at an organizational level and at a sector level (although the case studies do not use the concepts of big and small data in articulating the work they do). The two case studies featured are:

But before we explore the potential of big and small data for libraries and cultural heritage organizations it is worth briefly unpacking these terms and attempting to arrive at a definition for each.

Defining big and small data

Big data is commonly thought of as a collection of datasets that are so large and complex that they are difficult to process using standard database management and processing tools. Typically, the term is applied in areas such as physics or astronomy, where the data collected from experiments and observations is enormous and the burden of storage, analysis and visualization is tremendous.