5 dbt Implementation Mistakes That Kill Data Trust

dbt changed the game for analytics engineering. But like any powerful tool, it can create as many problems as it solves — especially when the implementation is rushed or the team doesn’t have a clear plan.

Here are the five mistakes I see most often when companies implement dbt, and what to do instead.

1. No Testing Strategy

This is the most common and most damaging mistake. Teams build dozens of models but write zero tests. Then they wonder why stakeholders don’t trust the numbers.

What happens: A source schema changes, a join condition breaks silently, and your revenue dashboard shows a number that’s 15% off. Nobody catches it for two weeks. Now your CFO doesn’t trust any report from your team.

What to do instead: Start with three tests on every model: not_null on your primary key, unique on your primary key, and at least one accepted_values or relationships test that validates business logic. That’s it. You can add more later. But shipping untested models is shipping broken trust.

2. Building for Flexibility Instead of Specificity

Teams design overly generic data models because “we might need it later.” The result is a warehouse full of tables nobody understands and nobody uses.

What happens: You build a model called dim_entity instead of dim_customer because “we might want to track partners too someday.” Six months later, nobody remembers what dim_entity is, and every query against it requires a Slack thread to understand.

What to do instead: Build for today’s questions. If the business needs a customer table, build dim_customer. If you later need a partner table, build dim_partner. Two clear, specific models beat one flexible, confusing one every time.

This is the 80/20 rule applied to data modeling: solve the problem in front of you.

3. No Documentation That Humans Actually Read

dbt has excellent built-in documentation support. Most teams either don’t use it or write documentation that’s technically accurate but practically useless.

What happens: Your schema.yml describes total_revenue as “The total revenue.” Technically correct. Completely unhelpful. When a new analyst joins, they still have to ask five questions in Slack to understand what’s included and excluded.

What to do instead: Document the business context, not just the technical definition. “Total revenue: Includes all closed-won deals in the current fiscal year. Excludes professional services, refunds, and deals under $1,000. Matches the number reported in the monthly board deck.” That’s documentation someone can actually use.

4. Ignoring the Source Layer

Teams jump straight to building marts and dashboards without properly staging and cleaning their source data. Then every downstream model inherits the same data quality problems.

What happens: Your CRM has duplicate contacts, your billing system has test transactions, and your marketing platform has internal email clicks. All of this garbage flows into your models, and your “clean” dashboard is built on dirty data.

What to do instead: Invest in a proper staging layer. Clean, deduplicate, and validate your sources before anything else touches them. This is the least exciting part of a dbt project, and it’s the most important.

5. Treating It as a Technical Project Instead of a Business One

This is the meta-mistake that enables all the others. Someone says “we need dbt,” the data team implements it in isolation, and three months later you have a technically sound warehouse that nobody outside the data team uses.

What happens: The data team builds exactly what they think the business needs. The business has different questions than the data team anticipated. The dashboards go unused. Leadership asks why they invested in “data infrastructure” with no visible ROI.

What to do instead: Start every dbt project with a business outcome. Not “implement dbt” — that’s a technology goal. “Enable the marketing team to see channel-level ROAS by next quarter” — that’s a business outcome. Build toward that specific outcome, validate with the stakeholders who need it, and expand from there.

The Pattern

Notice the pattern? Every mistake comes from the same root cause: prioritizing technical elegance over business usefulness.

dbt is a tool. Its value is measured not by how clean your DAG looks, but by whether it changed how your organization makes decisions.

If your data team has implemented dbt but your stakeholders still don’t trust the numbers, the problem isn’t dbt — it’s the implementation. A data audit can help you find the gaps.

If you’re about to start a dbt implementation and want to get it right the first time, let’s talk about your data foundation.

Go deeper: This post covers the most common pitfalls. For a full guide to building a modern data foundation — from architecture to dbt to governance — read The Complete Guide to Building a Modern Data Foundation.