How Big Data is Reshaping the Future of Music Industry
The music industry has needed a makeover for an extended period. With the advent of big data, it might revolutionize this sector and provide musicians with a more successful revenue model. It is undoubtedly one of the most significant technological shifts that the music industry has witnessed in decades. Let’s explore how big data is contributing to the development of this industry.
• New Revenue Model
The entire revenue model of music industries has been modified. Some applications have helped in curbing music piracy, yet the music industry hasn’t clearly defined the rates for streaming music.
Big data is helping artists and corporations by bringing them together more effectively via rapid data analysis such as Hadoop cluster. Data from the streaming music sites provides the organizations with exciting insights into the genres and styles that are preferred by their target demographics and pulling in the most revenue and minimizing the cost of licensing music albums.
Check out: Top Media and Entertainment Tech Companies
• Fine-tuning record charts to sell music
Major decisions for music charts are now automated. Many industries have their algorithms for recalling the specific songs people listen too. Moreover, there are applications in the smartphones that reveal the artist, the album and the song; these apps instantly identify music that’s playing.
Big data analytics also generates data on what motivates a listener and finds out why certain artists are more popular than others and help organizations to identify which of their business models have become outdated.
• Big data reveals about listeners
Big data provided a solution to the lack of fan engagement issues that the music industry deals. Therefore, companies keep track of every minute detail of their listeners from where they live to what are their education or income levels. To enhance the overall user experience, streaming platforms would have to integrate AI systems that could create a personalized playlist based on the listener’s mood and also matching their tastes depending on the current scenario.