What if data turned billions of records into instant business insights?

Modernizing decades of legacy reporting

Before Domo and Snowflake, Ken Garff relied on a decades-old, on-premise data management and reporting system that was no longer capable of meeting the scale, complexity, and massive data volumes of the business.  

“Combining all our data sets and producing reports in a fast and efficient way was becoming more and more difficult. It could take us weeks to build a report in the old environment,” said Brent Lessing, CIO at Ken Garff Automotive Group.  

To create a modern architecture for reporting, data, and orchestration, Ken Garff chose Snowflake as its cloud data platform and Domo as its analytics and visualization layer. After selecting Snowflake and Domo, Ken Garff’s data engineering team focused on building a modern pipeline architecture capable of supporting the massive data volumes generated across its dealership network.  

“Domo lets us manage very large datasets in Snowflake while still delivering fast, interactive dashboards to the business,” Peterson said. “When we started, we had about 1.4 billion records in Snowflake. Now we’re over 4 billion records. There’s no way we could have handled that with our traditional system.”

Snowflake acts as the company’s central data platform, consolidating operational data from sales systems, service platforms, finance tools, and third-party applications across dozens of dealerships. Ken Garff uses a Medallion Architecture to create a layered approach to data organization: a bronze layer for raw data, a silver layer for cleansed data, and a gold layer for business-ready data. This lets the company ingest, filter, transform, and curate data faster and cost-effectively.

Domo connects directly to its Snowflake environment, allowing business teams to access trusted data sets without requiring complex custom reporting or manual data extraction. Once data sets are updated in Snowflake, Domo can automatically refresh dashboards using API endpoints, ensuring dealership teams always have access to the latest operational data. This direct integration between the two platforms allows Ken Garff to rapidly surface new data sets and insights as they become available.  

“When we first started exploring Domo’s Snowflake connectors, within about 30 seconds I had connected Snowflake and created a dashboard that showed me all the information I needed in one place,” Peterson said. “It was so easy, and I didn't have to do anything. From that point on, we’ve been all-in on Domo.”

‍Turning dealership data into faster decisions
Domo and Snowflake have dramatically improved how quickly employees can access operational data. For example, the company has a report that provides daily insights into service department performance across dealerships.  

“During one month, we found there were around 400 hours of wait time just from people running that one report in the old system,” said Tom Howa, senior director of data and analytics at Ken Garff Automotive Group. “Instead of running the report and getting a coffee while they wait, they can now use it to make decisions instantly.”

With Snowflake and Domo powering its analytics platform, Ken Garff has expanded data access across nearly every business function. Corporate teams have standardized dashboards and reporting frameworks that can be shared across the dealership network. Executives and general managers can open dashboards each morning to review how their stores performed the previous day, track key metrics, and identify areas that need attention. Role-specific dashboards support weekly operational reviews and let users compare results across locations.

Ken Garff also uses Domo to analyze internal processes and identify operational improvements across departments. For example, the IT team used Domo to analyze employee support surveys and service desk ticket data to uncover recurring issues affecting productivity. By visualizing ticket trends and drilling into the underlying data, the team quickly discovered a significant number of requests were tied to a single category of technical problems.

“Now we can dig into where those issues are happening and why,” Lessing said. “That should reduce our tickets by probably 10 to 15 percent next year.”

Looking ahead, Ken Garff sees Snowflake and Domo as its foundation for expanding its data-driven culture further, with its data team already exploring ways to support applications such as service desk chatbots and tools that help teams analyze data sets and build stronger data governance practices.

“When you start thinking about where AI can be implemented most effectively, the answer is always where your data lives,” said Howa. “For us, that’s Snowflake and Domo.”