Fivetran Case Studies Fivetran Empowers HOMER with Efficient Data Management

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	Fivetran Empowers HOMER with Efficient Data Management Fivetran

Fivetran Empowers HOMER with Efficient Data Management

Fivetran

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
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

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

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.

About 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.

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.

Impact #1	The implementation of Fivetran's connectors led to significant operational improvements at HOMER. The engineering team, which was previously focused on maintaining pipelines, could now focus on problem-solving and building value for the company. The team was thrilled with the results and saw a clear picture of the value they bring to the organization. With marketing spend data, the team can understand and compile marketing performance. The team is also planning to use data from Optimizely to build a more robust suite of reporting and analysis tools. This shift in focus has not only improved the efficiency of the data team but also increased the trust in the data and analytics architecture of the company.

Impact #1

The implementation of Fivetran's connectors led to significant operational improvements at HOMER. The engineering team, which was previously focused on maintaining pipelines, could now focus on problem-solving and building value for the company. The team was thrilled with the results and saw a clear picture of the value they bring to the organization. With marketing spend data, the team can understand and compile marketing performance. The team is also planning to use data from Optimizely to build a more robust suite of reporting and analysis tools. This shift in focus has not only improved the efficiency of the data team but also increased the trust in the data and analytics architecture of the company.

Benefit #1	Saved dozens of hours each month by automating the process of flattening and maintaining Stripe data.
Benefit #2	Enabled the data team to focus on problem-solving and building value for HOMER instead of maintaining pipelines.
Benefit #3	Provided a holistic view of ROI by joining financial and marketing data.

Benefit #1

Saved dozens of hours each month by automating the process of flattening and maintaining Stripe data.

Benefit #2

Enabled the data team to focus on problem-solving and building value for HOMER instead of maintaining pipelines.

Benefit #3

Provided a holistic view of ROI by joining financial and marketing data.

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Overview

Fivetran Empowers HOMER with Efficient Data Management

Operational Impact

Quantitative Benefit