Client Overview

In today’s fast-paced world, personalization has become a driving force behind consumer preferences and purchasing decisions. Songfinch, a unique platform launched in 2019, has taken personalization to a whole new level by turning everyday stories and emotions into customized songs. This innovative business model is built on the foundation of storytelling, music, and technology.

As a digital audio company, Songfinch bridges the gap between creators and consumers by offering a unique and emotional experience. Customers provide details such as specific emotions, memories, or stories that they want to convey in a personalized song. This input can be in the form of written stories, anecdotes, or prompts provided by the consumer. Songfinch then partners with a diverse community of talented musicians and songwriters who interpret the customer’s input and craft a one-of-a-kind song, combining the artistry of the creators with the emotional content from the consumer.

The Challenge

At the core of Songfinch’s business is data, which is leveraged to generate revenue through the sale of personalized songs and related merchandise to consumers. Their business model requires an extensive understanding of user event data including customer demographics, artistic experience, product offerings, technology platforms, and multiform marketing to achieve operational efficiency. The need to have multiple data elements aggregated across their data sources and available for analytics processing would allow them to facilitate proactive marketing strategies to engage new and existing customers, which is essential for their long-term success. 

Songfinch’s initial platform did not lend itself to performing data analysis in different functional domains, so to achieve its future state goals of reaching new customers and driving repeat client engagement, a broadly democratized, easy-to-use and maintained data platform would be essential to that success.  In addition, Songfinch needed to move their core application platform from Heroku and Cloudinary to AWS to reduce costs, improve SLAs, and prepare for the implementation of a consolidated AWS data and analytics platform solution. 

 

The Solution

Uturn began with an initial start-up assessment to prepare for the data migration. The assessment provided the readiness for operating on AWS and provided a Total Cost of Ownership analysis, including migration scenarios, migration mapping, and a high-level migration plan. Uturn conducted a detailed review of the target environments and provided recommendations on right sizing and pricing models to optimize the migrated workload for cost.

Additionally, Uturn deployed a secure and scalable foundation for their migration into AWS. Uturn uses a proven AWS onboarding methodology called the Rocketry Rapid Launch program to help clients establish a well-architected environment up front. Uturn’s framework is a proven and enterprise-ready set of Terraform modules that helped Songfinch establish the design framework they were looking for, addressing everything from security, networking, development, staging, and the isolation of key resource environments.

For the Data and Analytics platform, Uturn started by conducting a Data Design Lab to help Songfinch establish their future state architecture.  Uturn’s Data Design Lab helps clients navigate the complexities of data platform design with the goal of establishing an architecture specific to their organization. Uturn’s Data Design Lab addressed Songfinch’s specific use cases and future state requirements and provided design recommendations for their production deployment.

Once the data Lab engagement was completed, Uturn moved froward with the build phase. Uturn began by establishing an optimized analytical data model, and then built a production ready data lake architecture by standing up an S3 Datalake and a Redshift Data Warehouse to provide an end-to-end solution. Uturn delivered Terraform Infrastructure as code for the production data environments to host both the data Lake and redshift data warehouse, incorporating an ETL infrastructure design to populate the three Data Lake stages: Raw, Transformed and Curated, using a Lake formation set up. QuickSight infrastructure was also established so Songfinch could visualize data from the data Lake and Redshift data warehouse environments across the organization.

Uturn successfully implemented all the requirements needed for the deployment to the new AWS infrastructure and data platform, and the Songfinch team immediately noticed a positive impact on their operations through a combination of increased productivity and reduced infrastructure management.