Review: Big Data in the Arts and Humanities—Theory and Practice

Big Data in the Arts and Humanities: Theory and Practice #

Edited by Giovanni Schiuma and Daniela Carlucci CRC Press / Taylor & Francis, 2018 (Data Analytics Applications series)

Schiuma, Giovanni, and Daniela Carlucci, eds. 2018. Big Data in the Arts and Humanities: Theory and Practice. Boca Raton, FL: CRC Press. ISBN 978-1-4987-6585-5.

It matters where a book comes from. Big Data in the Arts and Humanities sits in CRC Press's "Data Analytics Applications" series, alongside titles like Big Data Analytics in Cybersecurity, Sport Business Analytics, and Actionable Intelligence for Healthcare. That placement is not incidental. The volume treats arts and humanities as one more industry vertical waiting to be brought into the big-data fold, the same way the series treats hospitals, governments, and sports franchises, and the framing chapters read exactly like what that pedigree would predict: a business-consulting register applied to museums and literature departments. The surprise is that buried in the middle of the book is a chapter that has nothing to do with that register at all, and it is good enough to make the surrounding material look worse by comparison.

Three parts, two registers #

The book collects fifteen chapters into three sections: understanding big data in arts and humanities, digital humanities proper, and managing big data with and for the sector. The first and third sections are dominated by what can fairly be called cultural-management consultancy: chapters on "Culture Metrics" as a quality-assessment tool for arts organizations, a case study of Arts Council England's data practices, museum provenance databases, "data culture" in the creative industries, and smart-city art initiatives. These chapters are competent as institutional reporting—the Arts Council England chapter, for instance, is a genuinely useful account of how one major funding body tried to operationalize data-driven decision-making—but they rarely rise above description of programs and tools. The introduction sets the tone early with language like "the datification of human life" and repeated insistence that arts and humanities need to build a "big data culture," phrases that get restated across several chapters without much argumentative work being done between restatements.

The middle section, "Digital Humanities," is a different book. Ian Milligan and Robert Warren's chapter on historians moving "from black boxes to models" takes seriously the methodological stakes of computational history—not just that historians can now use big data, but what habits of mind they need to develop to use it responsibly, rather than trusting opaque tools they don't understand. It is the first chapter in the volume that treats its subject as an intellectual problem rather than an adoption curve.

The chapter that justifies the book #

The standout, though, is Federica Perazzini's "The English Gothic Novel: Theories and Praxis of Computer-Based Macroanalysis in Literary Studies." Working in the tradition of Franco Moretti's distant reading and Matthew Jockers's computational macroanalysis, Perazzini builds a large corpus of digitized Gothic novels and runs it through stylometric analysis—most-frequent-word comparisons, type-token ratio for vocabulary richness, and motif mining—to trace how the genre's "genome" evolved across decades. What makes the chapter work is that Perazzini treats her quantitative results as data to be interpreted rather than conclusions that speak for themselves: the chapter closes with a section of "epistemological considerations" that asks directly what it means to have discovered a pattern in word frequencies, and what interpretive work still has to happen before a pattern becomes a literary-historical claim. This is exactly the question the framing chapters never ask about their own material. Culture Metrics and audience-development dashboards are presented as though the hard part is generating the data; Perazzini's chapter understands that generating the data is the easy part.

An uneven case for the thesis #

The book's implicit thesis is that arts and humanities can benefit from big data approaches while also having something distinctive to contribute back to those approaches—qualitative depth, narrative sense-making, what Paul Moore's chapter calls "thick data." That is a defensible and interesting claim. But the volume makes its case unevenly: the chapters that actually demonstrate humanistic method enriching computational analysis (Perazzini, and to a lesser extent Milligan and Warren) are outnumbered by chapters that gesture at the idea of "data culture" without showing what a genuinely humanistic engagement with data analysis looks like in practice. A reader who wants to see the thesis proven has to do some of that comparative work themselves, chapter by chapter, rather than being handed a synthesis by the editors.

The book also shows its age in a way that is hard to avoid noting from 2026. Published in 2018, its touchstones are Amazon, Facebook, and Google as the paradigm big-data companies, and its framing questions—should museums adopt big data? what does a data culture look like?—belong to a moment before generative AI reorganized the entire conversation about computation and the humanities. None of this is the editors' fault, but it means the book now reads less as a live intervention than as a snapshot of a specific, already-superseded stage of the digital humanities conversation, useful mainly for readers doing the history of that conversation rather than looking for a current one.

Verdict #

This is a mixed book whose value is concentrated rather than distributed. Read it for Perazzini's Gothic novel chapter, which is a small, well-executed model of what computational literary analysis owes to interpretive judgment, and for Milligan and Warren's argument against black-box historical method. Read the surrounding chapters on data culture and cultural-sector metrics for institutional context if you need it, but don't expect them to meet the same bar. As a series entry in business-analytics publishing applied to a new vertical, it does what that genre does. As a sustained argument about what big data means for arts and humanities specifically, it is a partial success carried by a couple of chapters that clearly belong to a more demanding tradition than the one the book was packaged in.