Spendflo was experiencing rapid growth, and it became clear that their original analytics system was too limited. It couldn't scale to meet increasing demands from customers and internal teams who required fast access to data insights. The scattered nature of the data in MongoDB added to the issue, demonstrating that the old system was inadequate for the company's growing needs.

This led them to take the first step in adopting a modern data stack by investing in Amazon Redshift and modeling the data for more efficient analysis

Spendflo sought a single tool that could handle both their internal analytics and customer-facing analytics use case. They evaluated several BI tools in the market, all of which had similar features for data visualization and sharing. However, Databrain stood out for two main reasons:

• Custom Reporting for Customers: With Databrain's natural language features, Spendflo's users could now create their own reports. This allowed them to do much more with their data than what standard pre-canned reports allowed. Surprisingly, this also improved product usage metrics because customers loved diving into their data and deriving unique insights on their own.
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• Self-Serve Analytics for Internal Teams: Spendflo's data team was overwhelmed with ad-hoc report requests from all departments. Databrain's self-serve platform enabled business users to gather their own insights, freeing up the data team to work on more strategic projects. This also empowered business users to derive insights on the fly, instead of waiting days for a dashboard.

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• Faster Insights: Databrain reduced the time needed to obtain insights for both internal teams and customers.
• Self-Serve Reporting: Teams from sales to finance could now run their own reports, boosting productivity and helping close crucial deals faster.
• True Insight Finding: The data team transitioned from fulfilling mundane report requests to taking on key projects that have a larger impact on Spendflo's goals.