Uberflip Reduces Data Prep from Five Weeks to One Day, Achieves Near Real-Time Product Data Analysis with Matillion ETL for Snowflake

The modern cloud-native product provides agility, proactivity, and self-service for real-time, insight-driven decision making

Matillion, the leading provider of data transformation for cloud data warehouses (CDWs) announced that Uberflip has deployed Matillion ETL for Snowflake. By implementing cloud-native ETL, Uberflip reduced data preparation time from five weeks to just one day, helping their product, marketing, and sales teams to rapidly deliver better business value for customers. Matillion ETL delivered repeatable and scalable processes and models for data orchestration, and decreased required development time, freeing up valuable engineering resources.

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Uberflip is a leading content experience platform and software that enables marketers to create digital experiences with content for every stage of the buyer journey. To better serve internal teams and external customers, Uberflip’s data scientists needed a solution that could help them rapidly extract, load, and transform data to scale analysis within the product, empower internal teams for self-service, and get real-time, accurate data from all sources.

“Uberflip uses a lot of data from multiple sources within our business to serve the needs of our internal and external stakeholders. Our team needed an intuitive solution that required only one full-time data engineer to implement and manage,” said Greg Achenbach, VP of product at Uberflip. “With Matillion, we significantly reduced the time it takes to wrangle and prepare data, allowing our data scientists to focus on using that data to innovate within the business and help shrink the product feature backlog by providing data analysis within the platform.”

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A recent report from IDG cited that it takes about a week, on average, to aggregate and prep data so that it is useful for analysis. Nearly half (45%) of time spent on data analytics projects is dedicated to data preparation, instead of on more strategic, high-value tasks. Prior to implementing Matillion ETL and Snowflake, it took Uberflip’s data scientists five weeks to prepare data for analysis. The Uberflip team kicked off a proof-of-concept (PoC) with Matillion to test five high-value use cases. In just two weeks, Matillion ETL exceeded the success criteria defined by Uberflip for automated ETL workflows, error handling/warnings, ease of onboarding new data sources, required self-service for the data science team, and compliance with data privacy regulations.

Along with a dramatic reduction in data preparation time, Matillion ETL for Snowflake provided Uberflip with accurate data for data scientists and key stakeholders in the business, allowing for real-time decision making. With faster time to insights, their sales and marketing teams identify customer advocates, spot churn risks, and build marketing campaigns for upsell opportunities. The product team uses new insights to deliver resonant information that helps customers optimize campaigns based on how their content performs.

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