Identifying Key Terms

The main technologies for data analytics that I will be looking at are centered around artificial intelligence (AI), predictive analytics, and the use of big data. AI involves the use of machines to mimic the actions and decision-making processes of humans. AI is helpful in that it is able to identify patterns that emerge from data. The end goal of predictive analytics is to predict future outcomes; these outcomes are predicted using past data, machine learning, and algorithms. Big data is tied to both predictive analytics and AI, and generally refers to big amounts of complex data that can be analyzed for producing insights. Most of the examples found through my research involve a combination of these three processes for the purposes of assisting operations.

Arts Nonprofit Organizations

Arts nonprofits can use (and have used) data analytics and AI for assistance in internal operations. Museums specifically have been encouraged to utilize data analytics as a way of creating cases for support to existing and potential funders, and as a result, maintain relevancy for future museumgoers. AI and machine learning have both been utilized by nonprofits in general as a way of encouraging fundraising. GiveCentral suggests creating an algorithm that looks at data from funders as a way of being more in touch with donors. Similarly, AI can be used to quickly identify both individuals who are more likely to donate, and recently lapsed donors. In addition to fundraising, these technologies can also be used for general administrative tasks. Machine learning can be used for tracking data, rather than having a human employee do it. The use of chatbots for daily interactions with customers can also be helpful in making sure that employees can direct their attention elsewhere. When it comes to marketing, AI and machine learning is described as being able to help an organization develop better marketing strategies by analyzing data from different regions. While “traditional arts centers” may not be as advanced as digital platforms in their usage of data and data analytics, institutions such as the Mondavi Center for the Performing Arts in Davis, CA have used data for  creating socially-distanced seating models, predictive pricing, and profitability.

Aside from instances where AI has been used in both creative processes and in the restoration of art, these technologies have also been utilized for audience-facing experiences. This technology, while in some cases adds to the audience member’s experience, also helps management in the long-run by providing the institution with more interpretable data. It is worth noting that the visual arts world seems to have more pertinent examples of usage compared to the performing arts. One use of AI/machine learning has been deployed to predict the amount of time that an audience member will spend looking at a specific exhibit; the data helps the museum understand which exhibits attract the attention of audiences the most. Maintaining and understanding the interests of audiences is part of the ongoing work of a museum; the National Gallery in London, UK, utilized big data and machine learning for understanding the experiences and visitors as a way of “forecasting future engagement.” Similar institutions such as the British Museum have interpreted big data for the purposes of better understanding visitors and designing exhibits and programs that are more aligned with the interests of their patrons. The British Museum collected data from “audio guides and interactive exhibits” with the consent of visitors. In a slightly less clandestine way of collecting visitor feedback, many Swiss museums have used an app where visitors are able to share their experiences with museum directors directly while they are walking through the museum, as opposed to a completing a post-experience survey.

Entertainment & Sports Industries

There are significant uses of data analytics for the purposes of understanding and retaining audiences in the nonprofit arts, and within the larger media & entertainment industry, there seems to be an even larger emphasis placed on this. AI, predictive analytics, and big data are all utilized for the purposes of attracting customers, understanding what audiences like and dislike, and personalizing content for individual customers. Broadway uses a predictive analytic, big data utilizing software called PatronLink360, which is designed to assist in audience attraction and subscriber retention. In the film industry, executives have utilized big data as a way of better understanding what audiences do and don’t like in order to develop both creative and marketing strategies, and predict what audiences might be interested in. AI and predictive analytics can be useful in the process of personalizing content suggestions. One of the most prominent examples is through streaming sites such as Netflix; data on previous viewings are analyzed through AI and machine learning to give the customer suggestions on what to watch next. The common idea is that personalization can serve as an effective way of retaining customers, and as a result, creating a sense of loyalty. However, there are most likely other factors that play into whether a customer chooses to maintain a subscription beyond the content that is made available (such as pricing).

Aside from using these technologies for improving the performances and nutrition of athletes, and management of personnel, the sports segment of the entertainment industry uses AI and big data in ways that are similar to the arts, especially as it relates to the overall fan experience. The sports industry in general places a large emphasis on the relationship with fans. By looking at social media, in addition to attendance records, teams can use big data to understand fan sentiments and create marketing strategies as a result. Teams and stadiums have used data to analyze merchandise sales, and to see which events are more likely to sell higher numbers of tickets. With regard to ticketing, some teams, such as the soccer team Columbus Crew, have used facial recognition in place of utilizing physical tickets for entry. Additional examples of current uses of AI include facilitating stadium entry, and, similarly to some arts organizations, chatbots for customer inquiries. An example that is used in sports that has not been seen in other examples is the usage of data to analyze day-of conditions (including the weather) as a way of better preparing staff and management for the event. Aside from the facial recognition-ticketing, it seems like it may be difficult to utilize AI when attending a sports event in person; however, AI can also be used for advertising and adding to the viewing experience for people who may be watching at home.

Takeaways and Points for Consideration

One of the main similarities that I saw across these leisure industries was that the customer is generally at the forefront of consideration. These respective industries all have the goal of attempting to better understand their customers/patrons and effectively market to them, while also making sure that their audiences have a beneficial experience. A similarity between the nonprofit arts and the sports industry that I found both interesting and unexpected was the utilization of chatbots. However, it makes sense considering that both of these industries place a large emphasis on ticketing and making sure that the customers’ needs are met. A key difference is how nonprofit arts organizations have an added aspect of fundraising and developing/maintaining relationships with donors in their operations. Overall, there were more similarities between these industries than what I had initially expected. However, it seems that there are still differences in the scales at which these technologies are implemented.

Some of the implications or things to consider focus on the idea of big data, and data utilization in general. In order to derive results on audience insights, for example, an organization has to obtain a significant amount of data that is actually worth paying attention to and making decisions off of. Additionally, successfully implementing these measures requires a fair amount of both time and training. Arts nonprofits—and businesses across other industries—should consider what their organizational capacities are prior to delving in to large-scale changes that involve these technologies. At the same time, it is very possible to develop and utilize chatbots as an ‘introduction’ to incorporate AI and data. Lastly, when it comes to the collection of data, one aspect that actors across all industries should be aware of is increasing concerns over data privacy and recent laws regulations that require companies to protect personal data, such as the European Union’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). A recommendation is for businesses to consider these public policies and any new developments surrounding data compliance.  

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What is Google Arts and Culture?

Launched by Google in 2011 as a nonprofit initiative, Google Arts and Culture was created with the purpose of increasing the accessibility of art and culture. The platform describes itself as a new way of experiencing art and culture, with the main attraction being that the user is able to access various types of art regardless of the their location, or the location of an institution. The platform currently has over 2,000 global partners and over 100,000 works of art, giving museums, performing arts venues, and historical landmarks the ability to showcase various exhibitions, photographs and information about their institutions. Some prominent institutions include The British Museum, The Museum of Modern Art (MoMA) and the Sydney Opera House. When an institution becomes a partner, they obtain access to tools that allow for paintings and other materials to be digitized and uploaded to the platform. The Art Camera, which performs the digitization, is a high-resolution camera that allows for users of the platform to zoom in and observe up-close details.

Another digitization feature allows institutions to add virtual, 360 degree tours to their pages, similar to a view you would see in Google Maps.

AI, AR, and Experiments

Building off a substantial number of partnerships, Google Arts and Culture utilizes technologies such as artificial intelligence (AI)/machine learning and augmented reality (AR) for a variety of experiments that allow the user to establish personal connections to art, primarily by participating in the creative and art-making processes themselves.

One of the platform’s most prominent experiments is its selfie feature. Using the Google Arts and Culture app, users are able to use selfies to see what piece of art they resemble. When the user uploads their photo, Google’s software works to scan the selfie and go through the variety of paintings in the databases for a potential match. Though created with good intentions, this experiment did not come without controversy. Many BIPOC individuals found that when they used the feature, their results did not come with the same depth as those of White individuals; the works of art that displayed people of color were already limited, and when they were matched, it was often with figures that displayed “stereotypical tropes.”

This is not a unique instance of artificial intelligence reflecting human biases; Google as a larger enterprise—along with the artificial intelligence industry in general—has come under fire for its general lack of diversity. The reproduction of racist stereotypes is not necessarily the algorithm’s fault, but rather the fault of the people behind the algorithm. Some have also acknowledged how the platform’s mishap specifically reflects the lack of diversity in the art world.

In a less harmful way of establishing a personal connection to art, Google Arts and Culture introduced its Pet Portraits feature, which is also utilizes AI. The process is similar to that of the selfie feature: the user takes a picture of their pet, and the algorithm attempts to find an artistic match. AI has also been used for the purposes of preservation. Along with one of their partners, the Belvedere Museum in Vienna, Austria, they utilized machine learning to restore the paintings of Austrian painter Gustav Klimt in color.

Google Arts and Culture also makes prominent use of augmented reality (AR). The video below, uploaded by NASA and displayed on Google Arts and Culture’s website, utilizes AR and allows the user to “fly through a galaxy.”

Additionally, using the above painting, Fruto No. 1, I was able to use the Art Projector feature on the app, which lets you see what an piece of art would look like up close.

The leaders behind Google Arts and Culture, and the larger Google Cultural Institute, work regularly to explore and encourage the uses of AI and AR in the art-making process. In partnership with the Barbican Centre in London, a virtual exhibit detailing the “evolving relationship between humans and technology” and its implications for art, culture, and other aspects of our daily lives, is available to users.

Experiments such as the Blob Opera are fun way to receive deeper exposure to certain art forms (in this case, opera). There is also the Assisted Melody experiment, which allows the user to engage in a special music project. The user is able to write their own music, and the machine transforms it to make it sound like that of famous composer. On a different side of the musical spectrum is the Hip Hop Poetry Bot, an AI that was trained on rap and hip hop music, designed as a way to create a deeper appreciation for the art form. In my opinion, experiments such as these play a large part in regards to the access model that is integral to Google Arts and Culture’s platform and offerings.  

Disruptions and Implications

A form of disruption that I believe affects the art world is the aspect of personalization. In addition to the variety of AI and AR-based experiments that were mentioned earlier, a user can essentially curate their own art collections without having to leave their homes to do so. Because there are works of art from nearly every segment of the world, you can regularly see works of art that you otherwise might have never seen or heard of.

My initial perception of the Google Arts and Culture platform was that its presence would disrupt the visual arts world more than that of the performing arts. While the platform does showcase performing arts venues such as the Sydney Opera House and the Dance Theatre of Harlem, it is difficult to incorporate the entire experience of attending a ballet or a theatre production in an app such as Google Arts and Culture. At the same time, I still believe that many people visit museums not just to see the art, but to participate in the entire experience of being able walk around the physical building and engage with others who have come for a similar purpose.

To that end, I think that Google Arts and Culture serves more as a supplement, as opposed to a replacement, for cultural institutions. By utilizing the platform, a user is able to have a better idea of what they would like to see and experience if they were to visit the institution in person. In a way, the platform can serve as a marketing tool for partner institutions, as many of them do not publish the entirety of the collections that reside in their physical buildings. For larger artistic and cultural institutions, it could be worthwhile to look into establishing a partnership with the platform. It could also benefit smaller institutions, as your exhibitions and what you have to offer can be put on display for users from all around the world to see.

On the patron end, it allows users and potential visitors to be more informed of what the experience at a particular museum may be like. Because the app and the website are easily accessible, Google Arts and Culture lives up to its purpose of providing a new way of experiencing art, culture, and history: the app and website are both easily accessible, enabling users to engage in and view art whenever they want.

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