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Saddle Creek Logistics orchestrates the movement of thousands of products across the U.S. every day—on time, in full, and without room for error. With 32 million square feet of warehouse space and more than 6,000 associates, the scale is massive.
Yet, the margin for delay is nearly zero. What that requires is a comprehensive business intelligence stack built for the pace of modern logistics. When the legacy tools Saddle Creek Logstics had been using for 17 years started to slow decision-making, the team decided to make a shift.
By pairing Databricks with Sigma, Saddle Creek reimagined its entire analytics approach. We spoke with Todd Keyser, Senior Director of Information Systems, about how they did it—and why Sigma won unanimous support across a 26-person review team.
For nearly two decades, Saddle Creek Logistics relied on the same BI tool to support a fast-moving logistics network with constantly shifting variables. It was reliable and stable, but the company had outgrown the tool.
“The technology no longer kept pace with how fast our teams needed answers,” said Keyser. “It worked mostly, but it was slow. Getting the right data in front of our end users, at the right time, was a challenge.”
That lag represented a hefty structural problem. Saddle Creek’s BI team supports every corner of the business—from warehousing and transportation systems to corporate operations. As the company scaled, so did the complexity of the data. And their legacy tool simply wasn’t up to the task.
The technology no longer kept pace with how fast our teams needed answers. It worked, but it was slow. Getting the right data in front of our end users, at the right time, was a challenge.
—Todd Keyser, Senior Director of Information Systems
The company had already invested in Databricks to modernize its data architecture, but the front-end didn’t match the power of the warehouse behind it. Business users couldn’t self-serve, dashboards required manual upkeep, and when someone needed a new dataset or a different view, they had to wait.
So, they began the hunt for a tool that could surface real-time insights directly to the people who needed them. That’s when they started looking at Sigma.
The team starting looking for a new BI solution by launching a full RFP process, pulling in every major player from the Gartner Magic Quadrant. Twenty-six stakeholders participated—everyone from frontline operations to the executive suite. The goal was clear: find a platform that could meet their needs today and scale with the business tomorrow.
But while Sigma wasn’t even on the quadrant yet, Keyser had seen it firsthand at the Databricks conference. “I’d stopped by the booth a couple years in a row. The writeback functionality caught my eye. It wasn’t something we’d seen from other tools—and it solved a real problem for us.”
That writeback capability became a key differentiator. Saddle Creek needed a way for users to not only view data, but also add context—like tagging important information about a shipment with a reason code, directly in the platform. Most tools couldn’t do it, or required complex workarounds. Sigma made it simple, and worked natively with Databricks.
Sigma also asked the right questions up front. We didn’t get that from other vendors.
—Todd Keyser, Senior Director of Information Systems
By the end of the evaluation, the choice was clear. “It was a unanimous vote,” says Keyser. “Not a single person in the room chose anything else.” Sigma’s flexibility, the Databricks-native architecture, and the ability to do more than just visualize data, really pushed Sigma over the top. “Sigma also asked the right questions up front,” says Keyser. “We didn’t get that from other vendors—and it made a difference.”
Saddle Creek’s first move was embedding Sigma into their ServiceNow platform to give clients a single, seamless experience. “We had it up and running in under an hour,” Keyser said. “No code, no complexity. Just dropped in the dashboard and it worked.”
Internally, the team began consolidating 150+ static finance reports into a single, governed workbook, with row-level security flowing from Sigma to Databricks. It was a major step toward creating a shared, trusted view of the business.
We had it up and running in under an hour. No code, no complexity. Just dropped in the dashboard and it worked.
—Todd Keyser, Senior Director of Information Systems
Sigma’s writeback functionality was another early unlock. The BI team built input tables to track every legacy report during migration—owner, status, business line, all editable directly in Sigma. They also partnered with a systems integrator to co-develop a dashboard for shared project work, tracking tasks and updates in real time.
“Sigma isn’t just where we view data—it’s where we manage it,” said Keyser. “And that’s helped everyone get hands-on fast.” From embedded dashboards to project tracking and financial reporting, Sigma has already transformed how Saddle Creek works with data.
Saddle Creek’s work in Sigma is just getting started. One of the most ambitious initiatives is a full rebuild of their enterprise data model—consolidating 33,000 legacy objects and 600+ reports down to fewer than 30 governed Sigma workbooks. For the finance team alone, that means going from over 1,500 reports to a single, interactive workbook.
The team is also exploring AI-driven querying to support true self-service at scale. They’ve started testing natural language queries, focusing on warehouse time tracking and financial P&L data. Early results are promising, and the next step is rolling out Sigma’s built-in natural language tool, Ask Sigma, to bring that same accessibility into the business layer.
It’s about creating a single version of truth. When everyone is working from the same data, we move faster, make better decisions, and eliminate the guesswork.
—Todd Keyser, Senior Director of Information Systems
Meanwhile, leadership enablement is next on the rollout plan. Once enterprise-level metrics are finalized, the C-suite will be among the first active users—giving every level of the organization access to aligned, real-time views of performance.
“It’s about creating a single version of truth,” says Keyser. “When everyone—from site managers to senior execs—is working from the same data, we move faster, make better decisions, and eliminate the guesswork.”
The infrastructure is in place. Now it’s about scale.
Read more stories about Sigma and Databricks here.
