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This Year’s Grammys Demonstrate Technology Innovation Driving Engagement Across the Music Industry

EDB Team4/4/2022
Thought leadershipPostgreSQL

The 64th Annual Grammy awards were a showcase for disruption. As an industry that once viewed technologies such as digitization and streaming as threats to creativity and profitability, the Awards demonstrated quite the opposite across the music industry. Technology has opened new doors for artists whose work is now within easy reach of the consumer. This is the Golden Age of accessible music.

One of the key reasons for this accessibility is the industry’s increased focus on digital innovation, both when it comes to producing awards shows or providing listeners different options for finding their new favorite artists. Technologies like AI, cloud services, and data management solutions have become a central part of the modern music industry, and are key aspects of driving revenue and engagement. 

Perhaps the best example of technologies driving engagement are streaming services, like Spotify, Apple Music, and Tidal. These platforms, now the standard for enjoying music, began as oddities and outliers. In fact, many music industry leaders considered digital streaming to be a threat to their business models. Remember Napster?

Digital innovation has restructured the priorities of the industry, which creates both opportunities and challenges. 

 

The essentials of engagement

One of the key metrics of success in any industry is engagement—ensuring that your brand or product is getting regular and robust interaction. Now fans can follow (and directly communicate with) their favorite artists like never before. Managers and labels want fans to take advantage of that opportunity because it drives sales. Follower counts have replaced record sales as the key metric, as more artists have risen to fame via platforms like Soundcloud. Perhaps the most famous example is rapper Lil Nas X, who promoted himself via a TikTok meme, to turn his song ‘Old Town Road’ into a massive hit. 

Even established acts are now taking advantage of TikTok. Singer-songwriter Florence + the Machine posted a video captioned “The label are begging me for ‘low fi tik toks’ so here you go. pls [sic] send help.”

The more interaction that platforms—streaming or social—see from users, the better equipped those platforms are to analyze those interactions to anticipate user behaviors and desires. That enables both artists and labels to deliver more personalized experiences that drive more engagement, and the cycle goes on and on. In order to achieve this, two critical tools have taken center stage in digital innovation: open source database management systems (DBMS) and AI/Machine Learning.

 

Tracking your tracks with DBMS

DBMS are foundational to cultivating effective interaction, and open source solutions like Postgres (one of the most popular relational database management systems, or RDBMS) have proved to be some of the best options.

Because of all the data streaming platforms want to collect and leverage, they need databases that are:

  • Agile—so they can interact with users and make recommendations in real time
  • Flexible—so they can leverage a wide range of additional tools and solutions
  • Scalable—so that their database can keep up with the ever-growing track libraries and subscriber numbers
  • Performant—so users know they’ll actually be able to listen when they want. 

Take Spotify as an example: they need a DBMS that can store information (artist, genre, album, era) on over 82 million tracks and on the behavior of 406 million users, across 184 markets! That requires managing a lot of data points.

Open source databases, and especially open source cloud databases make managing multiple clusters of massive datasets simple to store, simple to monitor, and simple to leverage. Furthermore, because a database like Postgres isn’t wedded to a single provider—as opposed to AWS or Azure—you don’t need to worry about vendor lock-in, or restrictions on what tools you can integrate. Finally, because RDBMS are built by a community of experts focused on innovation, these platforms are constantly evolving based on the experience of their users.

Once streaming platforms have found the best database to store all their data, it’s time to build out the tools to put that data to use.

 

AI provides music just for you

Once all of this data is stored and sorted, open source DBMS solutions can begin to use AI and machine learning technology to leverage it into meaningful insights for them, and suggestions for their listeners. The ease of integration we previously mentioned makes it easier than ever for developers to connect their databases to a variety of tools, tailored to specific types of insights and automations, all working off the vast knowledge base of collected data.

As a result, the prevalence of AI in streaming has become a given in recent years—the all knowing “algorithm”—empowering music platforms to be flexible and versatile in how they interact (and encourage interaction) among users.

With all that information about who you’re listening to, AI technologies can build you customized playlists or just tell you which new albums might suit your tastes. Artists, especially up-and-coming ones, on massive streaming platforms depend on this technology to ensure that people are listening to them. Otherwise, it’s hard to cultivate a fanbase and all too easy to get drowned out by the deluge of other songs available.

But AI is not limited to just streaming. In fact, the Grammys made use of it this year to help promote artists and drive investment in the ceremony before its premiere! In order to help viewers get to know the many nominees and performers who would appear on the show, the Recording Academy partnered with IBM Watson to build out what they called GRAMMY Insights. This project used AI to trawl over 20 million articles online and compile the most interesting tidbits on every single key figure at the Grammys!

 

An ever-evolving model

So, with all this innovation happening both before our eyes and behind the scenes, where does the music industry go next? That’s a complicated question, and many are trying to find the answers for it. 

Streaming platforms remain somewhat contentious, especially among artists who argue that they’re shortchanged for the amount of plays their songs get. Meanwhile, despite what one might assume would be higher-profit margins as a result of that controversial model, the numbers don’t entirely bear this out. According to a New York Times blog article written by Shira Ovide in mid-March, total revenue industry for 2021 was $15 billion dollars, as opposed to $24 billion (adjusted for inflation) in 1999—the year Napster came on the scene.

Similarly, the Grammys has wrestled with the same issue as many other awards shows, seeing declining viewership that the Recording Academy is searching for innovative ways to address. This year, the show partnered with Mastercard and online gaming platform Roblox for the first ever virtual GRAMMY week, during which participants could see virtual concerts, compete for merch and prizes, and more.

Still, digital innovation has undoubtedly opened up new avenues for bringing music to the people. Finding effective ways to leverage this within an ever-evolving industry is an exciting and ongoing project.

 

Innovation in the front row

While there’s so much going on in the music industry right now, it’s clear that platforms and labels are constantly looking for the best ways to deliver the best musical experiences possible to legions of fans and performers. With technologies like open source DBMS, cloud services, and AI tools, the digital era of the industry is more layered and exciting than ever before. 

With the versatility of all these solutions, naturally come both opportunities and challenges—transformation is never just linear—and we’re thrilled to see the dynamic ways in which the industry will seize on them. In the meantime, last night proved that, when industries value innovation, the show will always go on.

Want to learn more? Watch our on-demand webinar on Postgres and the Artificial Intelligence Landscape.