Technology Category
- Analytics & Modeling - Machine Learning
- Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
- Buildings
- Cement
Applicable Functions
- Sales & Marketing
Use Cases
- Building Automation & Control
- Time Sensitive Networking
Services
- Training
About The Customer
HOMER is an early learning company that was founded in 2013 as a reading platform. Over the years, the platform has evolved to offer functionality far beyond a reading app, with courses in Math, Social and Emotional Learning, Creativity, and more. The company's analytics structure is based on three core aspects: acquisition, engagement, and retention. From an acquisition perspective, the analytics team is instrumental in determining the efficiency of advertising spend, allowing marketing teams to more accurately allocate budget based on business outcomes. When it comes to user engagement, HOMER faces a unique challenge as the app’s target audience of parents and guardians are not the end users of the app. From the retention side, the team is focused on understanding customer behavior.
The Challenge
HOMER, an early learning company, was facing significant challenges with its data management. Despite the company's data-driven roots, the data and analytics architecture was considered to be at a foundational stage when Joe Nowicki joined as Vice President of Data and Insights in February 2021. The company's data team was spending a significant amount of time building ETL pipelines, a laborious and time-consuming process. Flattening and maintaining Stripe data was costing the team dozens of hours each month, preventing them from adding value to the wider organization. The legacy data practices had led to a feeling of distrust, with leaders unable to depend on dashboard views and unclear business intelligence. Access to timely data was critical but lacking. There had been multiple visions and revisions of the entire data infrastructure, leading to the selection of Databricks’ Delta Lake, to set HOMER up for future Machine Learning applications.
The Solution
Joe Nowicki, with his previous experience with Fivetran, saw the opportunity to streamline the data management process with automated ELT. He knew the massive efficiencies that an out-of-the-box ELT platform could provide a data team, and the tremendous amount of effort involved in maintaining connectors. He found a good amount of crossover in Fivetran’s connectors and the business’s needs. From payment platforms to marketing vendors, HOMER’s data team was dealing with a high volume of structured data from common sources that could easily be imported with Fivetran. This allowed the engineering time to be focused on maintaining custom sources and event streams. Joe built a proof-of-concept with six of Fivetran’s connectors, including Stripe, to demonstrate the tool to the HOMER team.
Operational Impact
Quantitative Benefit
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