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Tatango, America’s leading SMS platform for nonprofit fundraising, helps nonprofits raise money and engage supporters through SMS, MMS, and emerging channels like RCS. Since 2007, Tatango has helped organizations raise over $600 million through SMS campaigns.
As a business powering high-volume, compliant messaging for nonprofits, Tatango couldn’t afford to make slow decisions. But that’s exactly the position they found themselves in.
“Before Lightdash, we had no formal analytics stack, no dbt, no centralized reporting, and no single source of truth. Reporting was fragmented across spreadsheets and ad-hoc tools. It was slow, inconsistent, and confusing.”
- Matthew Powers, Chief Technology & Product Officer
Picture this: You're the Chief Technology and Product Officer at a rapidly growing company. Your platform is gaining success and clients report an average return of 500% on their SMS marketing investment. But inside your organization, basic questions like "How many campaigns did we run last month?" take hours to answer.
That was reality for Tatango before Lightdash.
The symptoms were all too familiar. Reporting scattered across different teams and tools. Manual processes that took hours to complete. When critical business decisions kept getting delayed, they knew something had to change.
Tatango was already migrating to Snowflake and knew they wanted to build their analytics foundation on dbt. But here's the thing about most BI tools: they force you to rebuild your business logic all over again in their proprietary modeling language.
Tatango didn't want to maintain logic in two places; it doesn't follow DRY principles and it causes unnecessary work. Why define "monthly recurring revenue" in your dbt models and then again in whatever BI tool you're using?
When Tatango evaluated options like Looker, Metabase, and Preset, Lightdash stood out for one crucial reason: its native integration with dbt, which allows data teams to define all metrics and logic in one place without duplicating work.
“The native dbt integration was the game-changer. Our data team could work in familiar workflows without learning a new modeling language. Everything just worked.”
- Matthew Powers, Chief Technology & Product Officer
Other than being dbt-native by design, Lightdash also won because of its:
Most BI migrations are measured in months or quarters but Tatango's took four weeks. And the hardest part of their migration wasn't Lightdash, it was modeling their data. With over 1,000 dbt models powering their business, nearly everything had to be custom-built. But once the models were in place, Lightdash made the rest smooth.
"Once those models were in place, Lightdash made defining metrics, building dashboards, and rolling out to users a smooth and efficient process."
- Matthew Powers, Chief Technology & Product Officer
The integration was seamless and defining metrics became fast and straightforward. Dashboards were also easy to create and rolling out to users took little training. Everything just worked immediately.
Here's where being dbt-native really shines. Most BI tools require your data team to learn yet another modeling language, maintain separate metric definitions, and constantly sync between systems.
With Lightdash, Tatango's data team was productive from day one.
"Because Lightdash is tightly integrated with dbt, our data team was productive almost immediately, they could work within familiar workflows and didn't need to learn a new modeling language."
- Matthew Powers, Chief Technology & Product Officer
The Git-based workflow was another game-changer. Just like code, dashboards and metrics now had full version control, complete history, and easy rollbacks. Tatango’s team can define their metrics and dimensions directly in their dbt project, keeping all of their business logic in one place and increasing the context around their analytics.
The impact? A faster turnaround on new dashboards, alignment on key metrics across teams, and a significant reduction in rework for the data team.
“Turnaround time went from days to hours. Conflicting numbers in meetings are essentially gone.”
- Matthew Powers, Chief Technology & Product Officer
Tatango's approach highlights something crucial about modern data teams: they don't want yet another tool to learn. They want tools that amplify what they're already doing well. When your business logic lives in one place, changes propagate everywhere automatically and save your team time from manually updating dashboards.
Tatango isn't done innovating. Having migrated to Snowflake, they're now looking to layer AI on top of their Snowflake + dbt + Lightdash stack. And they’re excited to see where Lightdash’s own AI roadmap goes.
“If you’re tired of fragmented reporting, duplicated work, and inconsistent numbers, Lightdash is the fastest way to get to a single source of truth - especially if you’re already using dbt.”
- Matthew Powers, Chief Technology & Product Officer
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