Enhanced Analytics Engineering Improves Data Quality – Wonderschool

Before:

Post-Acquisition Identifies Data Scalability Challenges

The data team at Wonderschool needed to be as agile as possible to adapt to shifts in business and changing definitions. But a few things were hampering their ability to move quickly.

1. After Wonderschool acquired a company, whose product became their next-generation childcare management app, the team needed to model data in the new product, aggregate all of their data, and stitch their new and existing products together.
2. While the team did have some repeatable analytics processes, they lacked a truly modular way to make changes and update products.

“When we have to change something, we have to change it in a bunch of different places,” said Martin Bourqui, Data Analyst at Wonderschool. “It was the equivalent of handing out 5000 pieces of paper, then needing to find all of those pieces of paper to change a definition on each one of them. It’s easier to make a change once that shows up everywhere.”

The team struggled with lag times, a lack of data integration, and an overall lack of visibility into the state of the business.

Solution:

Analytics Engineering Adoption Generates Centralized & Accurate Data

Wonderschool partnered with Data Clymer, a Spaulding Ridge Company to set up a standardized, consistent analytics engineering practice. Using dbt, the team was able to centralize data from several different sources and model it effectively. We provided the extra resources Wonderschool needed to build out introductory models and efficient processes to leverage data they never had the time and resources to tap into before.

Additionally, we played an important role in helping Wonderschool think about data and analytics differently. They helped them drive additional conversations on division of labor, the hiring process, and the roles of data analytics, data modeling, and analytics engineering. A modular, repeatable analytics engineering process helps the team be more productive and work more cost-effectively.

“They didn’t just build the table,” Bourqui said. “They taught me how to think about building the table, and every other table. Data Clymer helped us create a whole framework of how to think about the modern data stack.”

The team now has insights into organized data and analytics and will continue to get all of Wonderschool’s data into its centralized data warehouse, scaling solutions to deliver even more value to the business.

“Things used to be very opaque, but we’re now in much better shape,” Bourqui added. “It’s easier to understand what we’re doing. And it’s easier to know how to build solutions for the future.”