Significantly faster time to insight with "just in time" analytics and data refreshed on-demand.
500% reduction in data engineering project time and faster data engineering projects: from 5 weeks to 2 days with Datameer.
A simpler, consolidated data stack and entire cloud-first architecture that reduced costs, management overhead, and data movement.
Data Integration, Data Analytics
Reliant Funding is a leading provider of alternative funding for small businesses. Since 2008, Reliant Funding has provided over $2B in funding to over 10,000 small businesses throughout the US. Merchants choose Reliant Funding as a trusted source for working capital thanks to speedy application process and personalized.
A critical aspect of Reliant Funding’s business model and key to its success is speed. Reliant Funding provides approvals within hours and funding within twenty-four hours. Therefore, the entire business and operations must be agile.
Datameer lowered our data engineering time by 500% and helped us produce just-in-time analytics that drove business agility.Ryan Goodman, VP of Data and Analytics, Reliant Funding
Datameer helped us simplify our data stack to reduce our overall cloud costs, management overhead, and data governance risks.Ryan Goodman, VP of Data and Analytics, Reliant Funding
The Reliant Funding data and analytics teams provide operational and predictive models as well as insights to the entire business. This includes insights in the marketing and sales funnels, the originations funnel, and the entire portfolio management.
To keep up with the agile business model, the operational systems (including loan originations) and analytics need to be agile as well. Without up to the minute and in-depth insights, the business teams would not be able to keep up the pace of business.
An important solution requirement for Reliant Funding is for the data stack to be cloud-native. In 20xx (need to validate the year), Reliant Funding converted its entire data stack to the cloud and converged all its analytics into a Snowflake cloud data warehouse. Data from Salesforce.com (their new cloud loan origination system) and their portfolio and cash management systems is pipelined into Snowflake for analytics.
A major challenge for Reliant Funding is speed. Analytics need to be up to the moment for the business teams to execute their unique, agile business and processing model. Previous approaches the company has used took too much time to process data and required overnight jobs, which would leave the business teams with “day old” analytics.
A second challenge is really an opportunity – to find ways to leverage their existing SQL skills while also eliminating their “data engineering gap.” Ryan Goodman, VP of Data and Analytics at Reliant, had already consolidated the data engineering and analytics teams into one group to get them working together effectively. Their solution now required tools that both data and analytics personnel could use, regardless of skills, and where the teams could share information and collaborate.
A final challenge was to eliminate as many data silos as possible. Reliant’s previous systems and approaches left data scattered, which made it difficult to manage and govern. The new approach required all data to reside in Snowflake.
When Reliant Funding made its’ move to the cloud and Snowflake, it was successfully able to pipeline the necessary data into Snowflake, but its tools and approaches for data modeling and transformation slowed them down. Transformations were performed in two places: SQL, which would run on Snowflake, and Tableau Prep. This presented two problems:
Reliant Funding chose Datameer as their new data modeling and transformation platform to replace Tableau Prep, consolidate all their data transformations, and simplify their data stack. Reliant Funding choose Datameer because:
Data and analytics teams could execute data engineering projects much faster – a couple of days versus many weeks.
The hybrid UI allowed their data engineers to leverage their existing deep SQL skills while also allowing less programming-oriented analysts to use a no-code approach to model and transform data.
The platform is Snowflake-native, keeping all the data in Snowflake and executing jobs in a fraction of the time.
The built-in catalog allowed the data and analytics teams to better document and share knowledge about the data.
Teams could create reusable building blocks, making projects faster and the data pipelines more reliable.
Reliant Funding saw significant improvements and benefits from its’ implementation of Datameer. One project that took five weeks in SQL and two weeks in Tableau Prep took only two days with Datameer. In addition, change management processes to adjust and re-deploy data models for new analytics went from multiple days to minutes.
Other results we gathered for this Reliant Funding case study include:
Lower Data Engineering Time
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