by guest blogger Nancy Graham
Why do data analytics matter in the world of art?
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The rise of data in nearly every field has transformed practically every industry imaginable — even art. This is partly due to how art has now become more accessible, with the online art market projected to grow by as much as 5.2% this year. This shows that more people are looking at art, and they are evaluating it in different ways thanks to the availability of more data. The marriage of data and art seems like an unlikely one, and yet today’s technology has made it possible to objectively measure the level of technique, color use, and other attributes of visual art through art analytics. With such a transformative new way of looking at art, how exactly has art analytics impacted the industry?
Defining Art Analytics
Today’s technological capabilities have made it possible to gain a massive amount of information in the form of data. And thanks to analytics, that data can be processed and analyzed to provide insights that help with decision making and predictions across all industries. Data analysts face the challenge of turning fast-changing information into better business outcomes across industries, such as healthcare, city management, sustainability, and technology.
In fact, careers in data analytics have become some of the most sought-after today, with an expected growth of 33% between 2016 and 2026. These analysts work with large datasets to spot patterns before developing insights that can benefit their organization or industry. In art analytics, these skills are applied to add quantifiable value and identify patterns and trends in the industry.
Analytics uses data to bridge the objective and subjective aspects of art, allowing artists, curators, collectors, and even enthusiasts to gain a better understanding of its intrinsic value. Data allows people to see art in more concrete terms, especially when measuring its social, cultural, and educational impact.
Art analytics can also provide explanations for why some works are more visually appealing than others. For instance, quantitative models can measure visual complexity to support previously established findings that intermediate levels of complexity are most appealing. By quantifying these artistic elements, it becomes easier to understand precisely which elements of a piece make it good or bad artwork, thus leading the way to artistic standards in a new age.
How Analytics is Transforming Art
Art analytics continues to define the intrinsic value of art pieces in several significant ways, such as the development of clearer pricing models thanks to data analysis tools. For instance, algorithms such as the random forest model can help predict price points of art pieces by classifying basic relationships using data to predict new outcomes. This enabled machine learning models to price famous pieces by artists like Georgia O’Keefe and Vincent Van Gogh close to estimates made by auction house appraisers.
The ability to pinpoint the worth and subsequent risk of art with more precision also helps curators and artists better understand the market as more consumers make investments. Accurate data serves as a benchmark for art valuation across the industry, making it possible to provide a more well-rounded estimate of a piece’s true value depending on the context, including rarity, technique, and the artist’s other completed works.
Art analytics can also provide insights on how to improve art sales. Recent data has revealed that artists’ reputations were more important in sales relative to the quality of the art itself. This is apparent in the successes that artists found in high-prestige versus low-prestige galleries, and it has also been studied in the social media sphere using algorithms such as the k-nearest neighbors model. Using text analysis, the model generated classifications based on similarities, finding that artists active on social media generated more sales based on artwork description word counts and artist biographies. This suggests that visual components are not the only determining factor of an artwork’s value and that textual aspects should also be taken into account.
Lastly, art analytics can help in the development of new business models involving virtual experiences, especially during a time when collectors are more reluctant to attend previews and galleries due to health risks. Apart from the creation of online databases for collectors to track prices and artist details online, data can make predictions on how customers will respond to these new mediums. Whether it’s a virtual tour or live-streamed exhibitions, data analytics is key in designing effective ways for art to reach its intended audience, especially with the culture of art collecting evolving into the digital sphere.
Using art analytics is just one way that you can turn your business art goals into a reality. Combined with setting specific, measurable, and attainable goals, artists and industry players can use analytics as a tool to stay ahead and stay relevant.
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