Improving the fan experience with fandom data

The challenges of unlocking fan insights within their data

Weverse Company is a global fandom platform company with the vision of innovating the fandom culture through expanding customer experience. As the influence of fandom continues to grow, Weverse Company felt the desperate need for a solution to extract and actively use meaningful fandom insights based on the huge amount of data being generated from Weverse.

However, the reality was that data had been collected in fragments and there was no infrastructure to manage the fragmented data. Adding to the data challenge was siloed data platforms brought on by Weverse Company which had been operating their own services within their own AWS accounts. This caused data redundancy and stunted collaboration across teams. In severe cases, the same data would be extracted several times, increasing operational complexity and infrastructure costs as the teams dealt with EMR, which offered poor data ingestion performance at scale.

Given their highly complex data environment, Weverse needed an innovative data platform that could collect and manage all the data generated in an integrated way for fan analytics. With redundant data across siloed platforms within separate AWS accounts, Weverse’s data teams struggled to not only access consumer data, but were also hamstrung in their ability to extract meaningful insights to help influence business decisions.

Building an integrated data platform with lakehouse architecture

Weverse Company turned to the Databricks Data Intelligence Platform on AWS to eliminate data silos and deliver data management and performance without driving up operational costs as data volumes continued to rise. Delta Lake, an open format storage layer that delivers reliability, security and performance on your data lake, has provided a unified view into all of Weverse data. And with Delta Live Tables, they can confidently provide high-quality data into Delta Lake as they build reliable data pipelines for downstream analytics.

From a collaboration and productivity standpoint, infrastructure is no longer a barrier, as features like autoscaling allows data teams to focus on their data rather than their infrastructure. And support for multiple languages, including SQL, Python and Scala, provides engineers, data scientists and analysts the ability to seamlessly collaborate and work with the data from the same platform. And native support for AWS services like S3 and Glue catalog ensure the data teams can access the data they need when they need it.

Integration with Tableau allows Weverse’s data analysts to easily create reports and visualizations without having to worry about data quality or throughput. With Databricks firmly embedded in their data technology stack, Weverse is able to provide a reliable, single source of truth for all data to enable better business decision making.

New service using fandom data becomes reality

Since implementing the Databricks Data Intelligence Platform, Weverse’s data teams have seen dramatic gains in operational efficiency and productivity in development and data management. In fact, through faster data processing, their data analysts have been able to eliminate 80-90 percent of the pre-work needed to access and collect data insights. As Weverse collects more user data, they are able to better explore the meaning behind their data across all facets of their business with the end goal of delivering customer-centered services that meet the demands of their fans.

Weverse is only at the beginning of its data analytics journey and will continue to strive and make smarter business decisions based on their consumer’s needs. With the help of Databricks, Weverse Company is now well positioned to achieve its mission of creating a world where fans and artists can come closer together through the use of their most important asset — the voice of the fans.