Modernize self-service analytics with dbt

For decades, organizations have chased the promise of self-service analytics. They’ve sought to empower business users to answer their own data questions without constantly relying on engineering teams.

Yet despite countless tools and platforms claiming to solve this challenge, most companies still struggle with the same fundamental problems: data silos, ungoverned data and code, and misaligned incentives between technical and business teams.

The persistent failure of self-service analytics stems from a fundamental disconnect between how these systems are designed and how teams actually work. While technology has advanced dramatically, the organizational and workflow challenges that prevent effective collaboration remain largely unsolved.

As the leader in data transformation technologies, dbt has spent years working with dozens of data-driven companies across all industries to solve exactly this challenge. Our latest platform features take aim at the issues that have hobbled self-service analytics, enabling what we call collaboration without chaos.

The persistent pitfalls of self-service analytics

Data and analytics consulting firms working across industries consistently observe the same pattern. Despite significant investments in modern data infrastructure, most organizations still operate in what can only be described as "the wild west" of data analysis.

Data analysts often find themselves forced to do whatever they can to obtain answers, jumping between tools—sometimes visualizing data in one platform, writing queries in another, and creating data in a third. They copy logic from old queries and set up custom tables because everything remains fundamentally siloed.

This chaos stems from an organizational structure where data engineers work in one group and analysts work in another, each with different priorities and needs. Rarely are they able to build together. That means engineers and analysts, more often than not, end up talking at each other rather than with each other.

An organizational issue

The misalignment runs deeper than communication. Data engineering teams are measured on building scalable, well-governed core assets that serve the entire organization. Their sprint cycles often extend six to nine months to ensure proper quality and governance.

Meanwhile, business analysts face entirely different pressures. They need answers in days, not months, to support critical business decisions.

This creates an impossible situation. A data analyst might need a specific table with a particular data model setup, joins, and columns. The engineering team understands the requirement and agrees it's valuable, but can’t schedule the work for another six months. When the analyst's project deadline is one month away, they're forced to create workarounds—Excel spreadsheets, local databases, or custom SQL queries that bypass established governance processes.

The consequences compound quickly. Organizations end up with random SQL queries running everywhere, Excel files being uploaded to create reports, and different definitions for the same business concepts across teams.

This means that the way one analyst defines a metric in Excel differs from how their coworker defines it. Both end up building dashboards using those conflicting definitions. This type of divergence erodes trust in data.

This situation isn’t anyone’s fault. It’s a natural consequence of how most companies are structured.

Who should care? Everyone, but that's complicated

Ideally, a self-service framework for data would serve everyone who works with data. A secure and well-governed self-service data platform needs to accommodate three distinct user types:

Data engineers represent the most technical end, skilled in SQL and scripting languages, focused on building scalable, efficient, and secure data pipelines that serve the entire company.

Power analysts and analytics engineers occupy the middle ground. They're skilled at transforming large datasets and comfortable with SQL, but they value rapid iteration and user-friendly interfaces over writing complex code from scratch.

Business users lean toward the non-technical side while still working directly with data. They understand their business questions deeply but prefer drag-and-drop interfaces and natural language queries over SQL development.

These user types are more of a spectrum than rigid categories. The same job titles can mean completely different things at different companies. A "business analyst" at one organization might have advanced SQL skills and build complex data models, while someone with the same title elsewhere might work exclusively with pre-built dashboards and simple filters.

Ultimately, successful self-service analytics needs to be built for humans:

• People who know what questions they want to ask but don't always know where to start
• Users wanting fast answers without waiting on engineering teams
• Teams needing to move quickly while maintaining governance standards
• Analysts uncomfortable writing complex SQL but requiring sophisticated analysis.

dbt’s solution: Empowerment without compromise

The fundamental issue is the universal tension between governance and empowerment. Organizations typically swing hard in one direction or the other.

Some lock down data access so tightly that business teams can't get work done. Others open everything up so broadly that quality and governance disappear.

dbt is a framework for delivering trusted data faster, better, and more collaboratively. Our platform redefines how companies think about trusted self-service. We do this by adhering to three core principles:

Speaking the same language in a governed environment by supporting four key capabilities:

• Move faster by building on trusted, reusable SQL code;
• Structured collaboration to enable reusing logic across teams without duplication or drift;
• Automated dependency tracking and version control, making it easy to deploy changes safely and scale with confidence; and
• Built-in metadata and model relationships to give AI the context it needs to generate accurate, business-aligned outputs.

Trust in data through comprehensive data lineage, governance, and transparency. Users can trust data when they know where it comes from, how it was created, and when it was last updated. This represents a dramatic improvement over receiving an Excel file from a colleague who says, "trust me, it's what you want." Clear documentation and governance structures eliminate guesswork and reduce human error.

Empowerment without compromise so that everyone can access and work with data within their skill level, technical capabilities, and available time. Business users shouldn't need to complete SQL training courses to get their work done. Data engineers shouldn't need to learn new systems to maintain governance standards. dbt accommodates different working styles while ensuring everyone can collaborate effectively.

Speaking the same language in a governed framework

To enable these capabilities, dbt implements three key features so that everyone in the organization can easily discover, use, and refine data:

• dbt Catalog provides global search across dbt and Snowflake assets so that users know what to ask and where to start
• dbt Insights helps analysts and business users generate, validate, and refine queries to gain insights fast without relying on engineering
• dbt Canvas supports low code and no code development of data models so that analysts can build fast without breaking governance

Let’s take a look at each of these features in detail.

dbt Catalog: Universal data discovery and trust

dbt Catalog solves the fundamental problem of data discovery in complex organizations. Many companies inadvertently create multiple versions of similar tables—such as dim_customer, dim_customers, and dim_customer_v2—because analysts are unaware of the existing data or which version represents the authoritative source.

dbt Catalog provides global search across all dbt projects and Snowflake data warehouse assets, pointing users to existing high-quality data rather than encouraging them to rebuild from scratch. This prevents duplication of effort and reduces the proliferation of slightly different data definitions.

Trust signals embedded throughout the catalog help users evaluate data quality before building reports. These include the status of data tests, lineage information, source freshness, ownership details, and quality validation through upstream dependency status.

Column-level lineage provides a clear view of how each field is derived from source systems, enabling users to understand not only what the data represents but also how it was created and transformed. This transparency fosters confidence in data accuracy and appropriateness for specific use cases.

For organizations using dbt Semantic Layer, dbt Catalog exposes standardized business metrics with complete transparency. Users can see both the high-level metric definition and the compiled SQL that generates results, eliminating guesswork about how key business measures are calculated.

dbt Insights: Embedded analytics with AI assistance

dbt Insights provides embedded analytics capabilities that eliminate the need to switch between discovery, analysis, and development tools. The platform supports the complete Analytics Development Lifecycle (ADLC) from initial data exploration through final insight generation.

The experience begins with immediate data validation through pre-populated queries that allow quick assessment of data quality and structure. This addresses common concerns about the origins and trustworthiness of data.

Within dbt Insights, dbt Copilot can transform natural language requests into executable SQL queries so that no one has to become a SQL expert to find their data. Users can ask for "top 10 best-selling parts for the last six months" and receive complete, runnable SQL statements. The system supports iterative analysis through follow-up questions, enabling users to drill down into specific findings, like monthly sales trends for individual products.

dbt Insights also includes integrated visualization capabilities with customizable charts and titles, allowing users to create compelling presentations of findings without additional tools. All analysis can be bookmarked and shared across teams via links that include both queries and visualizations, enabling others to build on previous work rather than starting from scratch.

dbt Canvas: Low-code transformation for visual learners

dbt Canvas addresses the needs of users who prefer visual, drag-and-drop interfaces for building data transformations. Consider a common use case: a data analyst tasked with cohort analysis needs to combine customer information with historical order data from Snowflake to understand purchase patterns and advertising effectiveness.

The visual interface allows users to input data sources and immediately see previews of results without running full queries. This real-time feedback loop supports efficient development by catching errors early and ensuring transformations produce expected outputs.

The platform enables sophisticated data transformations through intuitive interfaces. Users can create complex logic like case-when statements to clean status fields—for example, converting return_pending to return for consistency—with immediate visual confirmation of results.

AI integration through dbt Copilot enables natural language requests. Users can write something like "Combine stage orders and stage customers and add a column calculating total orders over time." The system will then generate complete workflows with inputs, joins, aggregates, and outputs. The AI makes intelligent suggestions for join keys based on column analysis, though users retain full control over accepting or modifying these recommendations.

Technical governance remains intact throughout the visual development process. Users can view the underlying SQL code at any time, seeing exactly what queries their visual transformations generate. All work gets stored as SQL within the project. That means it can be versioned in Git, audited, tested, and even reverted if required.

The collaboration benefits extend to code reuse. Users can import existing SQL models created by colleagues into Canvas for visualization, making complex transformations more understandable and modifiable even for those who didn't write the original code. Query history provides a foundation for building on previous work, while dbt Semantic Layer support ensures access to standardized business metrics with transparent calculation logic.

Making analytics a team sport

The evolution of self-service analytics isn't about choosing between governance and agility—it's about creating platforms that enable both simultaneously. Our unified approach addresses the root causes of previous failures through centralized definitions with transparent lineage for data quality, visual tools and AI assistance for improved data literacy, and collaborative workflows that clarify data ownership and responsibilities.

By ensuring everything resolves to SQL within a governed framework, organizations can scale self-service analytics without sacrificing the quality standards that enterprise data requires. This transforms analytics from a siloed, ungoverned process into a collaborative discipline where technical and business teams work together effectively.

Making analytics a team sport requires addressing organizational dynamics as well as technical capabilities.

dbt Canvas, dbt Insights, and our expanded dbt Catalog provide the foundation for this transformation, enabling democratized data access while maintaining and enhancing enterprise governance standards.