With economic signals flashing a slowdown ahead, companies need data-driven insights to find cost efficiencies and effectively prove ROI. A modern data stack (MDS), built on a suite of cloud-based tools used for data integration and analysis, gives marketers and analysts the data they need to make critical decisions. Companies like Snowflake, SpotOn and Red Ventures have embraced this new approach and reaped the rewards for doing so.
Here's a look at why they switched to an MDS built on Fivetran and dbt™ to help increase cost efficiencies and save 100 hours on data integration.
Snowflake dramatically reduces marketing waste and increases ROI
Snowflake has become ubiquitous with the growing modern data stack ecosystem, delivering access to the data cloud for over 6,000 customers worldwide. To further enhance their own marketing efforts, Snowflake wanted to monitor real-time ROI to dynamically optimize campaigns.
Snowflake used to keep their data modeling and transformation logic within a separate BI tool — but this approach had a few downsides. Every time the business needed to run models out of the tool or conduct ad-hoc analytics, analysts needed to recreate their models from scratch. This approach was time-consuming, error prone and held the marketing team back from their goals.
The implementation of Fivetran and dbt enabled a much more flexible experience for end users within the business. The team saw better overall performance as most of the compute-intensive calculations were conducted earlier in the process.
“We use Google, Facebook, and LinkedIn to run digital ads, and we ingest this data with Fivetran,” says Carl-Johan Wehtje, BI & Analytics at Snowflake. “Fivetran’s data models automatically clean up our raw data sources and output them into one model, enabling us to focus on more complex calculations and joining work. It means we can expose this data to our end users faster so that they can start drawing insights from it.”
With a strong ingestion and transformation framework in place, the marketing analytics team can focus on driving value for the organization with advanced data science and machine learning models, attribution scoring, forecasting and segmentation. This has allowed Snowflake to build a 360-degree view of customer behavior, enabling the marketing team to drive massive efficiencies in marketing spend and targeting.
SpotOn delivers analytics 5x faster while saving time and cost
SpotOn is one of the fastest-growing software and payment companies with comprehensive, cloud-based technology for businesses of all types and sizes. With the acquisition of Appetize, SpotOn’s client base grew to include enterprise venues like Madison Square Garden, Fenway Park and MetLife Stadium — all with unique needs and complex data.
The SpotOn product team needed a way to efficiently turn their captured customer transaction data into fast, reliable reporting for their clients. In parallel, their internal data team needed a way to harness data to optimize internal operations.
As the company scaled, collecting client data across dozens of MySQL databases grew complex. There were over 2,000 lines of code behind a single table. To further complicate things, any changes made were not automatically monitored or logged without version control, making QA time intensive — upwards of 15 hours a week. Scaling required writing code from scratch for each new use case, as they didn’t have a modular approach to data modeling. Simultaneously, the engineering team used various databases to capture internal growth metrics but lacked a central repository where they could more efficiently generate reporting.
“As a smaller dev team, we needed a solution that didn’t require full-time data maintenance. We needed a solution that could be set up in weeks, not months,” says Tom Gilbertson, Product Manager, Data & Analytics at SpotOn. “By integrating Fivetran and dbt (with Fivetran Transformations for dbt Core*), Fivetran becomes not only the ELT tool but also the orchestration tool, which is great for reliability, scalability and dev time.”
With Fivetran Transformations for dbt Core, SpotOn automates faster, more reliable reporting for their clients, while saving time and cost. dbt Core modularizes the 2,000 lines of transformation code, making data models more readable, easier to debug and scalable with jinja templates. By integrating their dbt project into Fivetran, Fivetran Transformations for dbt Core can orchestrate those model runs automatically post-Fivetran connector load – no custom scripting, third-party tools or DevOps required.
SpotOn now manages and automates their entire ELT process at scale from one platform. Client-facing reporting is now generated faster, cutting down the dev time by several weeks.
It’s not just customers that reap the benefits of the modernized data stack but the internal team as well. SpotOn’s data engineering team of two supports their 10+ data analyst team by using dbt Cloud to turn large volumes of data from multiple databases — moved by Fivetran — into powerful internal analytics and BI. The web-based UI allows all team members to collaborate, regardless of their comfort level with an IDE or command line. dbt Cloud also provides scheduling and built-in alerts, abstracting away the manual responsibility from the team.
This ensures that the team can continue to power data-driven growth without having to scale their headcount or sacrifice data integrity. By using cloud-based technologies, the entire team can participate in the analysis — expediting the time to insight while cutting down development time by 5x.
Red Ventures delivers up to 30 percent cost efficiencies
When companies want to optimize their marketing strategy, they turn to Red Ventures (RV). The media giant’s Red Digital division provides end-to-end performance marketing services that help business-to-consumer (B2C) service providers attract new customers.
Seeking to process data more efficiently for each client environment, RV has implemented Databricks to scale data engineering pipelines and speed up insights. The company also uses Fivetran to perform data ingestion and dbt to apply data transformations. All three solutions feed data into a machine learning pipeline that drives functions such as budgeting for clients’ advertising spend.
“We’ve put Fivetran in the hands of our marketers to let them set up integrations for ingesting our clients’ data, and we’ve given them access to dbt to define data transformations. Because these tools are so simple, we often don’t need to involve our engineers,” says Brandon Beidel, Director of Product Management at Red Ventures.
Because dbt and Fivetran are so easy to use, more RV employees are also able to be involved in data transformations. This data democratization is contributing to better results for RV’s clients by helping them use data and AI to target the right customers. “Using the highly intuitive machine learning pipeline we’ve built, we’ve helped our clients increase cost efficiency in some channels by 20 to 30 percent,” Beidel notes.
In addition, RV now processes client data more efficiently than ever, saving 100 hours per typical data integration and 80 percent less time on data processing jobs. This frees up data engineers to work on higher-value tasks. And because Fivetran provides the same data set for every client and dbt gives everyone a clear view of data transformations, RV’s engineering team has reduced troubleshooting from 50 percent of their time to less than 20 percent.
A powerful duo that speeds up data analysis
Fivetran combined with dbt has created a powerful solution for companies that have moved their data warehouse to the cloud. Fivetran efficiently handles the extract and load stages while dbt models data into analytics-ready tables, rounding out the ELT process. Combined, the two tools create a complete, end-to-end pipeline that is flexible enough to keep up with the fast-paced changes of the industry.
With Fivetran and dbt as part of a modern data stack, companies gain faster, more reliable reporting and access to data-driven insights. This data democratization enables marketers and other decision-makers to make smarter decisions, while also freeing up the entire data team to focus on helping to deliver even more useful insights – making it a win for everyone.