Museums, Smart Cities and Big Data: How Can We Transform Data Excess Into Data Intelligence?

Abstract

This chapter discusses the challenges and opportunities of integrating big data generated by contemporary museums into data ecology and data fabrics of smart cities. First, it exposes that smart cities could enhance their global reputation, visibility and image by building on closer collaborations with museums. Second, it demonstrates that museums in the 21st century have transformed into hyper-connected cultural hubs, spreading their reach and impact beyond their immediate urban locations. Finally, this chapter discusses creative approaches to data-curation mechanisms that stress the role of museums and cultural heritage sites in supplying data for a more strategic and proactive smart city co-design and management. Specifically, this chapter offers a three-dimensional framework for integrating heritage data in the design of smart city data ecosystems, which includes such components as Data Resources, Data Republics and Data Impacts. Data Resources stresses museum collections’ data and meta-data as a strategic resource to empower creative public data-curation practices to tell meaningful stories about the city and enhance place-making. Data Republics focuses on big data generated by visitors online or on-site as a foundation for evidence-based urban research, design and management, empowering more sustainable, safe and enjoyable tourism. Data Impacts details data-driven methodologies that museums could employ to measure public sentiment and opinion to offer new human-centred indicators to understand the performance of smart cities. This chapter shares a conceptual framework for repurposing museum data within a smart city data ecology to translate the current data excess into data intelligence.

Citation

Grincheva, N. (2024), "Museums, Smart Cities and Big Data: How Can We Transform Data Excess Into Data Intelligence?", Hendry, N.A. and Richardson, I. (Ed.) Data Excess in Digital Media Research, Emerald Publishing Limited, Leeds, pp. 123-138. https://doi.org/10.1108/978-1-80455-944-420241009