Big Data in the Arts and Humanities

Theory and Practice

Giovanni Schiuma, Daniela Carlucci

• Publisher: Auerbach Publications
• Year: 2018
• ISBN:
  9781351172585
• Edition: 1
• Topic: Business
• Subtopic: Government & Business
• Index: Business

As digital technologies occupy a more central role in working and everyday human life, individual and social realities are increasingly constructed and communicated through digital objects, which are progressively replacing and representing physical objects. They are even shaping new forms of virtual reality. This growing digital transformation coupled with technological evolution and the development of computer computation is shaping a cyber society whose working mechanisms are grounded upon the production, deployment, and exploitation of big data. In the arts and humanities, however, the notion of big data is still in its embryonic stage, and only in the last few years, have arts and cultural organizations and institutions, artists, and humanists started to investigate, explore, and experiment with the deployment and exploitation of big data as well as understand the possible forms of collaborations based on it.

Big Data in the Arts and Humanities: Theory and Practice explores the meaning, properties, and applications of big data. This book examines therelevance of big data to the arts and humanities, digital humanities, and management of big data with and for the arts and humanities. It explores the reasons and opportunities for the arts and humanities to embrace the big data revolution. The book also delineates managerial implications to successfully shape a mutually beneficial partnership between the arts and humanities and the big data- and computational digital-based sciences.

Big data and arts and humanities can be likened to the rational and emotional aspects of the human mind. This book attempts to integrate these two aspects of human thought to advance decision-making and to enhance the expression of the best of human life.

IIIManaging Big Data with and for Arts and Humanities

10Toward a Data Culture in the Cultural and Creative Industries

Cimeon Ellerton

Introduction

Barriers Are Cultural Not Technical

Aggregation and Collaboration

Skills and Silos

The Implementation Gap

Evidence versus Intuition

Conclusion

References

Introduction

The word “data,” in my opinion, has some challenges in the context of the cultural and creative industries. It is such a generic term, covering any type of information, and it can be perceived as rather dry and extensive, almost the opposite of creativity. Yet those with the right skills can bring these data to life for people and find stories within it that have an interest or practical meaning for others. I have been engaged in ongoing exploration into the challenges, trends, and opportunities of big data in order to put audiences at the heart of our cultural institutions. This exploration has taken the form of technical experimentation, action research, and ethnography, in particular the following:

• Aggregating and analyzing data for audience development
• Rapid-prototyping through collaboration between tech and culture specialists
• Ethnography into the cultural and structural barriers to data driven decision-making

When given the right tools, big data can help organizations build resilience while supporting creative risk taking. Furthermore, the rapid expansion in available information about who our audiences are (and are not) sets the stage for a radically transparent understanding of whom our public arts organizations are serving, who decides what goes on in them and how we communicate that to the public. Evidencing approaches to cultural democracy versus the democratization of culture should impact how we interpret the mission and policies of those that create and fund artistic activity. If, for example, Arts Council England’s vision is centered on achieving “great art, for everyone,” a conversation needs to be had about what “great” and “everyone” really means and how we measure it. Until then, we will lack as a sector the strategic framework and vision to use big data, so even the best audience, technological and organizational development will have limited impact. In the following, I discuss some learning from key projects that led me to this viewpoint. It should also be noted that while the findings have relevance across the creative and cultural industries, much of our exploratory work has focused on the subsidized touring companies, theaters, concert halls, museums, and galleries in England.

Barriers Are Cultural Not Technical

Throughout all the research projects, we have found that the most significant barriers to effective use of data within the organizations are not technical, but cultural and attitudinal. The data exist, but it is not always clear who has the means and rights to use them. We have found that this uncertainty is a common challenge across organizations, both large and small, and in every art form. Equally, there are pockets of excellence, but the cultural sector and, in particular, the subsidized part thereof are not necessarily of great scale or easily scalable. This is a fundamental challenge to the adoption of truly big data techniques, where size does sometimes matter when it comes to the data set. Many cultural organizations whose existence involves presenting new work to the public must also constantly seek to find a market for what are effectively new products. We are not like a supermarket or grocery store. Nobody needs what we offer in the same way they need food, and individually, we do not have hundreds of products on offer to suit every mood or situation. So while our analysis shows that the loyal core audience remains relatively stable across art forms and organizations, the churn of new audiences to any one venue is huge, in the region of 60% to 80% per year. The question of whether this is good or not cannot be answered by the data. The cost of maintaining such high churn rates can. We have found that business and technical terminology is itself a barrier. Terms such as “data-driven decision-making” (DDD) can be off-putting to many leaders in the subsidized arts sector, who believe that the use of such language and techniques is the top of a slippery slope leading to a purely commercial, transactional relationship with audiences and the cultural sector becoming just another part of consumer society. When delivering a talk on DDD to chief executives of arts organization, Anthony Lilley1 was asked:

You’re telling me a computer should programme my venue?

Participant in a leadership workshop

This is not a silly question. The hype of big data has promised extraordinary levels of automated decision-making. However, experts such as Nate Silver make a strong case for the human intervention in understanding the meaning of data analysis. In his book, The Signal and the Noise,1 Silver writes about the limitations of automated analysis techniques to prescribe the action that should be taken on the basis of any analysis.

We’re not that much smarter than we used to be, even though we have that much more information—and that means that the real skill now is learning how to pick out the useful information from all this noise.

We at The Audience Agency have come to call this the “so what” question, and I will talk in more detail about this later in the chapter. What is clear is the importance of context and experience that, combined with values-driven business models, cannot—at least for the foreseeable future—replace people as the final decision-makers. We are some way off the prescriptive analytics of the “Gartner Model of Data Management Maturity.”2 What is within reach is the application of big data techniques to move beyond the merely descriptive and toward the predictive.

Aggregation and Collaboration

Forecasting the likely interest in an exhibition or event and the likely impact of a marketing intervention could drastically improve resilience in arts organizations while simultaneously increasing the capacity for creative risk-taking. However, truly big data in the arts—of the scale available to telecoms and finance—cannot be generated by a single organization within the cultural sector. There are large theater operators that might come close, but even they would be paddling in the shallow water in comparison to, for example, a supermarket chain. Furthermore, for the analysis to be of use, it must be actionable. In the case of theaters, our research shows that it is usually the local venue with which audience members feel they have a relationship and not some larger parent organization or visiting company, which has implications for who is best placed to use any analysis of captured aggregated data, as well as the previously described difficulty in achieving the volume o...

Table of contents

• Cover
• Half Title Page
• Series Page
• Title Page
• Copyright Page
• Contents
• Editors
• Contributors
• Introduction
• Section I Understanding Big Data in Arts and Humanities
• Section II Digital Humanities
• Section III Managing Big Data with and for Arts and Humanities
• Index