cleancult wanted insights informed by a variety of data sources, but was limited by existing infrastructure and available technical resources. Reporting on key business processes required access to manually entered data as well as data from in-house and third-party tools, but building and maintaining custom integrations with a single full-time technical resource would quickly become burdensome. To address cleancult’s data access needs, Brooklyn Data employed Stitch, an easy-to-use, extensible ingestion tool, to make cleancult’s data available for reporting.

With an economical ingestion layer in place, Brooklyn Data shifted its focus to the transformation and presentation layers of cleancult’s data stack. cleancult had recently selected Sisense for Cloud Data Teams (previously known as Periscope Data) as their business intelligence platform and wanted to maximize its value.

 

Many of cleancult’s critical reports were built in Sisense and directly connected to cleancult’s production database. While convenient, the application database was not designed for the analytics use case, making for more complex and less efficient querying and slow-running reports. Brooklyn Data provisioned an Amazon Redshift data warehouse to store a copy of the production data, the newly integrated data sources, and the analytics-optimized tables that would be built next.

 

Like many organizations, cleancult was beginning to see the challenge of trying to manage data modeling via its BI tool. While an excellent choice for quickly building a comprehensive set of dashboards and easily managing data access for external vendors, Sisense lacked the dependency management and unit testing to support the more complex data transformations that cleancult needed. With the data models Brooklyn Data constructed using data transformation tool dbt, cleancult now has a more unified vision of its customers and a more maintainable analytics code base.

 

After addressing data storage, ingestion, and transformation, Brooklyn Data moved on to delivering a more performant and maintainable Sisense setup. Applying advanced features of the BI tool meant that similar reports could be combined. Reports run faster on the shorter, simpler queries enabled by handling data ingestion and transformation outside of the BI tool. Finally, with a warehouse designed around analytics use cases, building new reports goes much faster.

We brought on Brooklyn Data Company to help us build a solid foundation for all our analytics efforts in cleancult. They have gone above and beyond my expectations in building this platform and as the sole technical team member on the cleancult team, I am very cautious as to who I bring on as a partner.

ZACHARY BEDROSIAN, COFOUNDER AND CTO

This flexible, scalable data infrastructure is a springboard for cleancult’s analytics efforts. Now that the company has a solid foundation in place, they can easily add new features to reports, expand available data sources, and automate more reporting. Whether business needs call for analyzing customer lifetime value, diving deep into marketing efficiency, or refining manufacturing forecasts, cleancult can deliver what it needs to maintain investor confidence and streamline operational decision-making. With the next stage of growth in their sights, cleancult can advance knowing that the Brooklyn Data team has set them up for analytics success.