Houston Food Bank improves fundraising and donor outreach by modernizing its data layer

The Houston Food Bank (HFB) is the largest food bank in the U.S. Each year, it serves over 140 million meals across 18 counties in southeast Texas. To do that, it works with an extensive network of over 1,600 community partners in lockstep with their partner food banks.

It’s a massive operation, and it requires a lot of data. But up until recently, HFB relied on siloed systems and manual processes to manage it. Understandably, the data team struggled to scale their efforts, surface strategic insights, or innovate beyond day-to-day operations.

Let’s explore how the data team modernized their data infrastructure using dbt. We’ll see how the journey unlocked critical insights—while increasing collaboration, trust, and governance across HFB.

The problem: unscalable, siloed systems

Before adopting dbt, HFB’s data environment existed in fragments: every department tracked its KPIs in its own spreadsheets, and data transformations were stored across different tools, systems, and formats.

Meanwhile, the data team was stretched thin. There were only two dedicated data professionals, who were bogged down with basic reporting. Without a unified architecture, they couldn’t offer more strategic support, like identifying high-value donors or measuring impact.

When the COVID-19 pandemic hit, HFB realized it had a data problem. As demand for services surged, HFB’s executive leadership needed real-time visibility into operations, but there was no way to view the organization’s core KPIs in one place.

In response, the data team rapidly built stopgap infrastructure to meet emergency needs. It was a turning point: HFB saw the value of data infrastructure and committed to a broader digital transformation.

The solution: a unified data architecture with dbt

After building integrations and a data warehouse, the team struggled with a critical gap: managing and automating data once it was in the warehouse.

That’s where dbt Core came in. The team migrated all of their queries to dbt Core, which allowed them to deploy and orchestrate data models alongside their integration infrastructure.

“For the first time, almost all of our SQL code was under version control via GitLab,” says Herndon-Miller. “dbt Core empowered us to implement software engineering best practices for our data pipelines.”

Two years later, the team officially made the jump to dbt. It’s been a game-changer for collaboration: now, analysts can write, test, and deploy SQL models themselves, without engineering support. Everyone can see what’s changing and why, thanks to dbt’s built-in lineage.

“Today, our data team of eight oversees more than 70 reports that deliver more than 180 metrics across the organization,” says Erwin Kristel, Data Analyst at HFB. “dbt improved our ability to build trust with our stakeholders and help them make faster decisions.”

The impact: data that changes lives

More than that, the data transformation has amplified HFB’s capacity to serve. With better visibility into donor behavior, partner activity, and community needs, HFB has unlocked millions in grant funding and identified patterns for reducing food insecurity.

A centralized dashboard for monetary donors

Kristel led the effort to unify volunteer and monetary-donor data, previously spread across multiple unintegrated systems. To support the fundraising team, he transformed that data into an interactive dashboard designed to surface high-potential donors.

It was a big initiative to complete this—but it paid off. In just one 30-minute meeting, the fundraising team identified nearly 20 major donors who could contribute $10K-$50K or more but hadn’t been prioritized for outreach.

Automating essential metrics and reporting

Previously, the data team had to manually compile essential metrics for things like grant reports, partner-performance tracking, and resource planning.

To automate this process, the data team turned to dbt Seeds and modeling. Now HFB is allocating resources more efficiently—and even uncovering new-funding opportunities.

“Last year, our community-level partner metrics generated $4 million in three different grants,” shares Susan Quiros, Data Analyst at HFB. “Not every community has the same needs, and now we can create tailored funding strategies that serve them effectively.”

Measuring the impact of food-benefits programs

HFB’s Community Assistance Program (CAP) helps people apply for benefits like SNAP. It’s a critical program, but it was deeply siloed, making it difficult to understand the program’s impact.

After integrating CAP data with pantry-usage data, the data team learned that 48% of new SNAP applicants reduced pantry visits within six months. It’s powerful evidence that access to these benefits has an impact on food insecurity.

Armed with these insights, the government relations team is better equipped to advocate for SNAP in conversations with policymakers.

Meeting their neighbors where they are

By adopting dbt, the HFB data team has increased data accessibility across the organization. They’ve built trust in metrics at every level: their work has improved donor engagement, identified funding opportunities, and validated the impact of state programs.

For HFB, their data transformation has been a force multiplier. dbt is now central to how HFB operates—helping the organization serve the community, one meal at a time.

If you’re a data professional at a nonprofit looking to modernize your data stack, we’d be honored to help you build your transformation strategy. Book a demo to see how dbt works; you can also sign up for dbt to connect your data warehouse and start building.