In 2021, TikTok updated its privacy policy which allowed it to collect biometric data on its users, including faceprints and voiceprints. Rather than explicitly informing its users about this change, they only communicated that they were issuing a “privacy update” upon opening the app. Once people found out what the update entailed, they started growing concerned, and rightfully so. It’s a significant shift from companies collecting behavioral data on their consumers to something a little more invasive and without consent. Only 36% of Americans trust tech companies using facial recognition technology. In general, public trust in big tech has been steadily falling in the United States. Yet most of us still click “accept” to the Terms & Conditions on any website without actually knowing what we agree to. There’s been an apparent disconnect between what we expect from US businesses and what we blindly agree to.

What is Data Privacy

Everyone has data – name, age, gender, birthday, interests, browsing habits, etc. These aspects of a person’s identity fall under the Personal Data category. That data is valuable to big corporations, whether they sell the data to gain revenue for free services to their consumers or buy the data to advertise their products to potential consumers in a tactical, albeit intrusive, way. In the notorious  2018 Senate Hearing on Facebook, Mark Zuckerberg’s retort, when posed with the question of how Facebook remains profitable when its service is free, gave us a glimpse into an obvious yet disturbing truth behind the operations of a big tech company. “Senator, we run ads,” followed by a cheeky smirk. The purpose of data in the capitalist sphere we’ve grown all but too familiar with in the US has become predictive modeling of human behavior.

Data privacy can be defined as the careful handling of data throughout its lifecycle, from data creation to data deletion, based on its relative importance. It’s a field that has grown exponentially in the digital age, dealing with the management of data, governance, compliance, laws around it, consents, notices, and regulatory obligations. The Health Insurance Portability and Accountability Act, better known as HIPPA, is a policy in the healthcare sector that protects against institutions misusing patients’ medical and health data. It prevents healthcare organizations from giving out patient information on their physical and mental health and requires that healthcare and healthcare insurance industries protect this information from fraud or theft. This policy prevents insurance companies, pharmaceutical companies, and doctor’s offices from directly targeting people with personalized ads for products or services. HIPPA is the only sector-specific policy in the United States that protects people from having their personal information abused or exploited by large corporations. With only three states having adopted some form of data privacy law, it’s unclear how soon Congress will propose bi-partisan data privacy legislation. For the time being, it’s up to the individual states.

Legislation

It’s no surprise that the United States government is behind on data privacy regulation. As with many contemporary issues, it often takes a while for the government to create bi-partisan cooperation toward implementing change. Currently, the European Union is leading the data privacy venture with the General Data Protection Regulation (GDPR). Its creators intended for it to be the toughest privacy and security law in the world to date. Much like the right to the pursuit of liberty or free speech, the new EU regulation is stalwart in the idea that “the protection of natural persons concerning the processing of personal data is a fundamental right.” Under this regulation, EU citizens have the right to ask companies how their personal data is collected, stored, and used. They also have the right to request that their data be deleted from a company’s database (or CRM). Companies must get consent from a consumer before collecting and storing their data. With the exception of HIPPA, there is no centralized regulation that protects US citizens’ personal information from being abused by companies.

The closest piece of legislation we have exists only in California, whose government passed the California Consumer Privacy Act (CCPA) in 2018. Under CCPA, citizens have similar rights to those established in GDPR, except for a few differences. But it can be inferred by the name that CCPA only applies to California residents. Similar pieces of state legislation were passed in Virginia (the Consumer Data Protection Act) and Colorado (the Colorado Privacy Act), both in 2021. The biggest hurdle appears to be the extent to which Democrats and Republics want big companies to be penalized for mishandling personal consumer data. While Democrats want to enable consumers to hold businesses accountable for abusing their data, Republicans opt for more business-friendly legislation. Without centralized federal regulation, having varying data privacy laws by state makes the issue much more complicated. It leaves consumers unsure or unaware of their rights.

The Artist Experience

In 2016, Mozilla partnered with Tactical Tech to bring the Glass Room to London and New York City. This immersive exhibit, made to look like a tech store, was created to teach people about who is collecting their data online and why. The Glass Room attempts to demystify the world of data privacy in a vast digital world where one can lose themselves without knowing it. It does so by getting participants to think about how they interact with online platforms and helps visualize and contextualize otherwise abstract ideas.  

To move through the Glass Room is to be reminded of the many ways we unwittingly submit ourselves and one another to unnecessary surveillance, with devastating consequences

The New York Times

As with any other complex social issue, artists have been quick to be the messengers of social and moral concern. The beauty of the arts is that they can convince people that they need to care about something. Humans are inherently visual creatures, but it sometimes takes more than just showing an audience why something is a problem. Some artists have gone so far as to make an audience live a problem, either in an immersive exhibition or through some “alternate universe Black Mirror” way. One such experience is German artist Tobias Leingruber’s Facebook ID card project. In 2012, Leingruber created Facebook ID cards for his guests at an art event. This concept was somewhat influenced by George Orwell’s 1984. Leingruber’s project lends commentary to how social media pervades our everyday lives. If we allow digital platforms to consume our identity little by little, where is the line drawn? And because we give such importance to having an online presence, is it really unimaginable that one day we base our value on the digital content we so meticulously curated to achieve some level of social status?

Netflix’s Black Mirror episode Nosedive also played with this idea in 2016, in which a woman’s life unravels over her slowly deteriorating “social media score” as she desperately tries to cling to the small amount of social standing she has. While these are two extreme examples (thankfully, in the ten years since, Leingruber’s fake project hasn’t become a reality), the main question coming to my mind is what happens to us when nothing is private anymore? In the United States, citizens are very well aware of their rights as established by the Constitution. Yet when a significant data breach at a big company occurs, collectively, we aren’t as outraged by it. This isn’t to say that civil and constitutional rights aren’t as necessary. But as the EU established in the GDPR, “the protection of natural persons in relation to the processing of personal data is a fundamental right.” As more of our personal lives become exposed and our habits become exploited by big business, how does the nonprofit sector factor into all this?

Arts Nonprofits’ Adaptability

In a society where technological trends feel almost ephemeral, it’s understandable why many nonprofit art organizations are slow to adopt the most up-to-date data collection, storage, and management technologies. Several factors come into play, like lack of funding, tech-hesitant Boards of Directors, and a general lack of awareness of systems that would make the work more efficient. Even many for-profit entities are behind the curve when it comes to having a data strategy. But as opposed to for-profit businesses, nonprofits could be using data from their communities to create more effective programming tailored to their target audience’s needs. These efforts are crucial to achieving its mission. One author noted that when organizations lack the data architecture to fully leverage the data available to them, the actual opportunity cost is innovation.

Knowing how to use the data is only part of the equation, though.

As if investing in data collection and management alone wasn’t a whole new world for nonprofits, we now add the complex issue of consumer privacy into the mix. The lack of US regulation around data privacy begs the question: what responsibility do the private and nonprofit sectors have in this realm? While there is little US legislation for the private sector, there is even less for the nonprofit sector, leaving organizations even more in the dark. Yet, data has become more crucial than ever in understanding their target demographic, creating appropriate programming, and effective marketing materials in nonprofits. However, the lack of data architecture and policies leaves nonprofit organizations vulnerable to data breaches equally, if not more. The New Jersey Shakespeare Theater experienced a ransomware attack in 2019. Hackers disabled the organization’s access to its ticketing system and patron database. Several other breaches have occurred in which hackers gained access to an organization’s funder information, costing them tens of thousands of dollars, practically forcing some to shut down.

Because nonprofits don’t operate as a traditional “business” and because those kinds of data breaches don’t typically make headlines, this may be seen as a trivial issue. But as entities that hold personal identifying information on patrons, donors, employees, and Board members, nonprofits have just as much an obligation to protect their data as do for-profit businesses. The following are starting points nonprofits should heavily consider in developing a long-term strategy for data storage and protection:

• Understand the legislation (discussed above) that exists to develop protection plans
• Form a data and cyber security governance committee
• Embrace cloud-based storage software
• Properly educate employees on data security

Conclusion

The arts can be used to inform the public about this issue and make it understandable through a more tangible medium. With the current lack of legislation in the United States, the arts can bring awareness to this issue and empower the public to push the federal government to implement change. All in all, nonprofit arts organizations have the same responsibility as for-profit and government entities to protect personal consumer data and use it ethically. The relationship between people, their data, and business is significantly unbalanced. But artists and arts organizations can help restore the balance by acting in the best interests of the public and using legislation such as the GDPR as a reference.

Resources

Alexander, Alistair. “The Glass Room: Big data, privacy and interactive art.” Mob Lab. April 28, 2018. https://mobilisationlab.org/stories/big-data-privacy-interactive-art/.

Crittenden, Elizabeth. “Let’s Talk: TikTok’s Privacy Update And Incubator For Black Creatives, Spotify’s Speech Recognition Technology, And More.” Arts Management & Technology Laboratory. June 22, 2021. https://amt-lab.org/podcasts-interviews/2021/6/lets-talk-tiktok-privacy-update-and-incubator-for-black-creatives-spotify-speech-recognition-technology.

DalleMule, Leandro and Thomas H. Davenport. “What’s Your Data Strategy?” Harvard Business Review. May-June 2017. https://hbr.org/2017/05/whats-your-data-strategy.

“Data Privacy – Definitions, Importance, Legislations / Privacy laws.” Data Privacy Acts. May 12, 2020. https://dataprivacyacts.com/data-privacy-everything-you-need-to-know/.

Fang, Jiashun. “What Makes Facial Recognition Controversial?” Arts Management & Technology Laboratory. February 13, 2020. https://amt-lab.org/blog/2020/2/what-makes-facial-recognition-controversial.

Frankfurt, Tal. “How To Avoid Security Breaches In The Nonprofit Sector.” Forbes. March 31, 2021. https://www.forbes.com/sites/forbestechcouncil/2021/03/31/how-to-avoid-security-breaches-in-the-nonprofit-sector/?sh=1be2a0095ce0.

Fried, Ina and Mike Allen. “Exclusive: Trust in tech craters.” Axios. March 31, 2021. https://www.axios.com/edelman-trust-barometer-tech-5787acea-8ef5-4d0b 96946e4f8eb006c4.html.

GDPR.eu. “General Data Protection Regulation (GDPR).” Accessed April 6, 2022.  https://gdpr.eu/tag/gdpr/.

Jehl, Laura and Alan Friel. “CCPA and GDPR Comparison Chart.” Baker Hostetler LLP.  Accessed April 30, 2022.     https://www.bakerlaw.com/webfiles/Privacy/2018/Articles/CCPA-GDPR-Chart.pdf.

“Legislative Preview: Data privacy”. Congressional Quarterly Magazine. February 14, 2022. https://advance-lexis-com.cmu.idm.oclc.org/api/permalink/73df9d80-2c49-4f29  a233-3ca744502911/?context=1516831.

Leingruber, Tobias. “FB Bureau Berlin: Get Your Fb Identity Card!!” Free Art and Technology Lab. February 24, 2012. http://fffff.at/fb-bureau-berlin-get-your-fb-identity-card/.

NBC News. “Senator Asks How Facebook Remains Free, Mark Zuckerberg Smirks: ‘We Run Ads’ | NBC News.” April 10, 2018, 1:00. YouTube video. https://www.youtube.com/watch?v=n2H8wx1aBiQ.

“Nosedive.” IMDb. Accessed April 30, 2022. https://www.imdb.com/title/tt5497778/.

Pardes, Arielle. “What Is GDPR and Why Should You Care?” Wired. May 24, 2018. https://www.wired.com/story/how-gdpr-affects-you/.

Pyne, Lydia. “A Data Artist’s Guide to Putting People (and Privacy) First.” Hyperallergic. May 6, 2021. https://hyperallergic.com/641187/living-in-data-jer-thorp/.

Rippy, Sarah. “Colorado Privacy Act becomes law.” IAPP. July 8, 2021. https://iapp.org/news/a/colorado-privacy-act-becomes-law/#:~:text=Sarah%20Rippy%20IAPP%20Member%20Contributor%20On%20July%208%2C,earlier%20this%20year%2C%20to%20enact%20comprehensive%20privacy%20legislation.

Rippy, Sarah. “Virginia passes the Consumer Data Protection Act.” IAPP. March 31, 2021. https://iapp.org/news/a/virginia-passes-the-consumer-data-protection-act/.

Schenker, Dylan. “Artist Explores Online Identity and Privacy With Facebook ID Cards.” VICE. March 5, 2012. https://www.vice.com/en/article/z4y5z8/artist-explores-online-identity-and-privacy-with-facebook-id-cards.

State Of California Department of Justice Office of the Attorney General. “California Consumer Privacy Act (CCPA).” Accessed April 22, 2022. https://www.oag.ca.gov/privacy/ccpa?msclkid=7dc2492cbd9411ec867bf5984af6282d.

“The Week in Breach: 12/04/19 – 12/10/19.” DeckerWright Corporation Blog. December 11, 2019. https://www.deckerwright.com/blog/the-week-in-breach-1-5.

“What is CRM?” Keap. Accessed May 2, 2022. https://keap.com/product/what-is-crm.

Younanzadeh, Emanuel. “Why Your Company’s Data Architecture Is More Important Than the Data Itself.” Forbes. April 13, 2022. https://www.forbes.com/sites/forbescommunicationscouncil/2022/04/13/why-your-companys-data-architecture-is-more-important-than-the-data-itself/?sh=609e81e85311.

We live in a world full of data. Data brings information, knowledge, and wisdom, as presented by the DIKW pyramids. Based on large sets of data, we can add value and enrich it through productive analysis, and finally, we can get wisdom. With technology developing, getting trends and insights from unorganized raw data becomes easier and easier for individuals and organizations. Today, most for-profit companies collect data and use analytical tools to target customers, reduce costs, and make optimal choices. As nonprofits, arts organizations also rely on databases to collect audience information and get a better understanding of them. People are still exploring ways to apply new technologies to analyze data precisely. As one of the hottest emerging concepts at the moment, AI can do more than simulate human behavior. In the article, we will explore how to integrate machine learning, a type of artificial intelligence that has strong analytical and forecasting skills, into art organizations, better “learn” from your data of audience and achieve their mission.

What is machine learning?

Machine learning is a branch of artificial intelligence that applies more to the data analysis field. One of its core concepts is to use machines to learn from data and make predictions. It is a way to “use computers to answer questions”. The computer will learn from a training dataset, which is the historical data we already had, build models and find patterns, and use the model in the test dataset to evaluate if it fits the data. For example, we have a dataset of three different species of iris and their features of sepal length and sepal width. We can use this dataset to let computers get the correlation model between the features of sepal and species. By observing the sepal width and length, the computer can help us to identify the species if we get a new flower. We may simply find the pattern and classify the species ourselves, but sometimes we may not have the ability to handle data with many features and seemingly no pattern, so machine learning can be helpful.

Machine learning is commonly used in fields of image and video recognition, prediction, and personalized push notification on social media platforms. Companies mainly apply this method to build price strategies by mining historical price data and making customer segments. In the arts and entertainment industry, it is also useful in marketing research. It would be helpful to get insights into who our customers are, why they choose us and how they will be more satisfied and loyal in any organization.

Data in art organizations

Arts organizations already find benefits in using data-driven methods to engage the audience. Around 90% of nonprofits are collecting data but half of them are not sure how to use it according to The State of Data in the Nonprofit Sector report. 85% of respondents in the Nonprofit Trend report say they “use insights from marketing and engagement data to target outreach efforts. Organizations are used to applying data tools to collect customer data and build stronger relationships. It is easy to find the information of ticket buyers such as email, address, what kinds of performances they like, or how much they spend on your organization’s events. And that helps organizations to make customer segments, know preferences, and reach out to them directly. Based on a research of nonprofits, the most common use of audience data is using the contact details to send out newsletters. And less than 40% of the respondents use the audience data to personalize campaigns and inform the process of creating artworks.

Machine learning in audience analysis

Art organizations should analyze data more deeply. With new technologies, organizations can get data that is bigger and multiple forms and find better insights in audience analysis. Machine learning makes it possible to find patterns in disorganized audience behavior data. We will mainly focus on several applications this technology can bring to art organizations, build better relationships, make recommendations and help improve the services and productions. 

Profile your customers for a better relationship

Customer Relationship Management(CRM) is software to manage all the relationships in a company. Arts organizations are benefiting from it by managing all the disparate activities such as ticketing, fundraising, and data reporting, which makes it easier to target the audience. For example, it allows organizations to turn the transaction record to the relationship. You can easily find the first-time buyers and send them emails about the upcoming event to improve their return rates.

Machine learning offers more possibilities for CRM. It can leverage the data from “what” to “why” based on the CRM platform. Besides using the sales data to predict future profits, it is also useful in profiling your audience. Accurate customer segments bring better connections. Integrating the big data and machine learning method into CRM enables more types of data to the platform and plays an important role in understanding audience behaviors. By mining deeply the historical ticketing sales data, it helps to find more patterns in buyers’ habits and predict the probabilities of whether they will return. 

Organizations can use the results for more accurate segmentation and send different newsletters to audiences with different return rates or buying habits, which enables them to make deeper connections and improve their loyalty. For unstructured data and free text fields, It is difficult to extract common points with traditional statistical methods. However, it is possible to set an algorithm to find out any inquiries, complaints, and references for specific shows. It is also useful in fundraising. Donors are giving because they want the organizations to meet their needs. By getting an accurate profile, we can know more about donors’ interests and explore how the organizations can introduce programs to donors and help them to pursue their needs. It helps to cultivate long-term relationships and encourage giving.

The arts industry has already applied it in customer segmentation. Purple Seven is the leading theater and art data analytics company to use big data to help find insights for organizations. By combining an organization’s CRM with external big data of performing art consumption, Purple Seven can identify which bookers have the greatest likelihood of returning, what their audience is interested in, how to reach them, and where to find new audiences. 

Make personalized recommendations

Machine learning’s application in CRM makes customization possible. A personalized recommendation system is a technique to provide recommendations based on historical behavior data. It is a common application by using big data to analyze user behavior, particularly for online streams like YouTube and Netflix. Netflix uses the watch history of other users who have similar tastes to recommend what you may be most interested in watching next. It excels in analyzing audience profiles and pushing personalized products. To maximize the satisfaction of viewers with different preferences, it even cut out several different versions of the trailer and distributed them to different people through the user profile mastered by big data. The benefit is also significant. Netflix Over 75% of its viewer activity is based on personalized recommendations. They earned over 1 billion due to the recommendation system accounting for over 80% of the content streamed on the platform. 

Unlike the entertainment industry, traditional art organizations focus on off-line events. Currently, the personalized recommendation system is used for galleries. There is software using big data and machine learning to predict the artworks’ price for customers and make personalized recommendations, such as Arternal, Artbase, and Artcloud. Although few recommendation systems are widely used in the theater industry, data scientists are making models to gain insights into the artistic preferences of customers and similarities between performances to make personalized recommendations.

Improve content and service

Organizations want to know not only why the audience chooses us, but also their whole experience, and which parts or details raise their interests or make them satisfied. Analyzing audience behaviors in the events can help us achieve this goal. For the online platform, the user’s clicks, length of stay, and comments will be analyzed by algorithms to find out the pattern of which types of plots are popular. The investment of productions is also based on the prediction of the main characters and plots. The millions of visits per day can bring a large number of data samples to the platform to support the analysis and prediction, to provide the audience with more desired works. 

Museums use a similar method in exploring how the visitors interact with the venue. The British Museum uses machine learning to collect data of visitor experience. They can find out how people experience its exhibitions: what routes they take, what they engage with, how many minutes they take at each installation, and which pieces they choose to ignore. By doing this, the museum can get the point of interest of the audience, know what is working and what is not.  It can be more targeted to making the promotion and event and exhibition design in the future. For example, if more people stay a long time during similar topic paintings, it may be interesting to consider an education tour for these artworks. It helps to increase visitation, harness social outcomes and deliver efficiencies.

Theaters can hardly get the audience’s review during the events, but show lovers love to post their feelings on social media. Theater can get a view of what the audience likes and dislikes about the show by analyzing the unstructured comments on the internet.

Conclusion

After introducing the applications, we found that their commonality is to analyze the behavior of the audience, discover the characteristics of the audience, and use this to engage more audiences, build loyalty and expand the impact of the organization. That is why we use data analytics tools. We want to find a measurable way to know our audience, to discover who they are, why they are here and what they want, and broaden it and connect with it in innovative ways. Arts organizations always value audience feedback, but previously we could only do this using common sense, observation, and interviews of individuals. Now machine gives us a chance to get more comprehensive information and discover hidden insights from a large amount of data. With machine learning, it is easier to get a clear profile of the audience and their story with your organizations and help improve the organization and build deeper relationships in a targeted way. A better understanding of your audience is also a better understanding of your organization. For small art organizations, there may be barriers such as being hard to get a big data sample. Although the pattern will be more accurate with more data, it will still be useful to know more about your audience. 

It is the trend in the world to use big data. Art organizations will not miss the chance to be involved in this trend for an efficient way to make connections with audiences. Arts organizations will gradually discover the importance of data and get used to using analytical tools to discover insights and understand the audience.

Resources

Abernethy, Jacob D., Cyrus Anderson, Alex Chojnacki, Chengyu Dai, John Dryden, Eric M. Schwartz, Wenbo Shen, Jonathan C. Stroud, Laura Burdick, Sheng Yang and Daniel T. Zhang. “Data Science in Service of Performing Arts: Applying Machine Learning to Predicting Audience Preferences.” ArXiv abs/1611.05788 (2016): n. Pag.

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Carlsson, Rebecca. “Big Data and Museums.” MuseumNext, March 30, 2021. https://www.museumnext.com/article/big-data-and-museums/.

Day, Adrienne. “Data-Driven Connections for a Better World (SSIR).” Using Data and Technology to Create World-Changing Connections Between Nonprofits and Their Supporters, 2020. https://ssir.org/articles/entry/data_driven_connections_for_a_better_world.

“How CRM Can Help You Outperform National Arts Industry Revenue Benchmarks.” NAMP, May 15, 2019. https://namp.americansforthearts.org/2019/05/15/how-crm-can-help-you-outperform-national-arts-industry-revenue-benchmarks.

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Midura, Danine. “Leveraging Machine Learning Using CRM Data.” Technology Advisors, July 14, 2021. https://www.techadv.com/blog/leveraging-machine-learning-using-crm-data.

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“The British Museum Is Using Big Data to Help Visitors Learn More about History.” Microsoft News Centre UK, July 4, 2017. https://news.microsoft.com/en-gb/2017/07/04/the-british-museum-is-using-big-data-to-help-visitors-learn-more-about-history/.

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Villaespesa, Elena. “Digital Culture 2014 – How Arts Organisations Use Audience Data.” arts&metrcs, December 8, 2014. https://www.artsmetrics.com/en/how-arts-organisations-use-audience-data/.

“What Is the Data, Information, Knowledge, Wisdom (DIKW) Pyramid?” Ontotext, October 22, 2020. https://www.ontotext.com/knowledgehub/fundamentals/dikw-pyramid/.

Yu, Allen. “How Netflix Uses AI and Machine Learning.” Medium. Becoming Human: Artificial Intelligence Magazine, October 1, 2019. https://becominghuman.ai/how-netflix-uses-ai-and-machine-learning-a087614630fe.