As organisations strive to become more data-driven, the need for reliable, transparent, and scalable data transformation processes has never been more urgent. In Southern Africa, where many businesses are navigating resource constraints, legacy infrastructure, and rising regulatory demands, building a modern data stack is both a challenge and an opportunity.
dbt (data build tool) has emerged as a pivotal solution in addressing these challenges. It brings structure and discipline to transformation logic by enabling analytics engineers to treat SQL like software, leveraging best practices such as version control, testing, modular development, and automated documentation. When paired with the emerging power of artificial intelligence, dbt becomes more than a transformation tool. It becomes a platform for productivity, governance, and sustainable data culture.
What is dbt and why should it matter to Southern African enterprises?
Data transformation is not a new concept, it’s a foundational step in analytics. But what has changed is the scale at which transformation must now occur. With the rise of cloud data warehouses like Snowflake, BigQuery, and Databricks, the volume of data, and the speed at which it must be prepared for analysis, has increased significantly.
dbt is purpose-built for this reality. It enables teams to transform raw data within the data warehouse using only SQL, eliminating the need for complex ETL tools or bespoke scripts. More importantly, it does so in a way that promotes transparency, collaboration, and automation.
For businesses, this means:
Reducing dependence on scarce data engineering resources
Bridging the gap between analysts and technical teams
Improving the reliability and traceability of business-critical metrics
dbt also aligns with broader strategic imperatives such as data governance, compliance, and operational efficiency, which are key concerns for industries like financial services, retail, healthcare, and telecommunications.
Augmented analytics engineering: unlocking efficiency with AI
Artificial Intelligence is reshaping the way data teams operate. In the context of dbt, AI does not replace human expertise, it amplifies it.
The integration of AI into dbt Cloud represents a step toward augmented analytics engineering, a model where AI enhances productivity by automating repetitive tasks and guiding users toward best practices.
Key AI-powered capabilities include:
Context-aware code suggestions, reducing development time and standardising SQL patterns
Automated documentation generation, easing the burden of knowledge transfer and audit-readiness
Anomaly detection within pipelines, helping teams proactively address issues before they impact stakeholders
Enhanced data discoverability, supporting data consumers in understanding what metrics are available and how they’re defined
These features allow data teams to shift their focus from operational firefighting to strategic enablement, delivering insights faster while maintaining a high standard of quality and control.
Reference: State of Analytics Engineering 2025 – dbt Labs
The dbt fusion engine: performance meets simplicity
dbt Labs recently introduced the Fusion Engine, a significant architectural upgrade, recently announced, aimed at improving performance and developer experience.
Key features of the Fusion Engine include:
Improved concurrency and scalability, ensuring that transformation jobs can run efficiently even at enterprise scale
Decoupling from Jinja, removing legacy constraints and reducing cognitive load for developers
Composable execution plans, enabling a more modular and reusable approach to building transformation logic
These enhancements are particularly valuable for organisations managing diverse data sources, complex logic chains, and increasing demand for timely insights. The Fusion Engine helps ensure that dbt remains performant, flexible, and fit for purpose as analytics needs evolve.
Real-world results and business impact
The value of dbt and AI is not hypothetical, it is measurable and backed by industry research. According to dbt Labs:
Teams using dbt Cloud with AI features report up to 40% reduction in development time, thanks to intelligent automation and enhanced developer experience.
68% of data practitioners cite “documentation debt” as a barrier to collaboration, a challenge directly addressed by dbt’s automated documentation capabilities.
The dbt Semantic Layer now enables consistent metric definitions across tools like Tableau, Power BI, and Hex, resolving one of the most persistent issues in enterprise BI environments: inconsistent reporting.
These results are not just technical improvements, they represent meaningful progress toward faster decision-making, greater alignment between departments, and stronger data governance.
Sources: dbt Labs, State of Analytics Engineering 2025
Why businesses should act now
The Southern African market presents a unique set of data challenges. Skills shortages, budget limitations, and infrastructure diversity often make it difficult to implement and sustain best-in-class analytics practices.
However, these same constraints make the case for dbt and AI even stronger. By adopting dbt, supported by AI-driven enhancements, businesses can:
Democratise data transformation across analysts and engineers
Introduce automation and repeatability into critical data processes
Improve compliance and auditability by making transformation logic transparent and version-controlled
Reduce time-to-insight, improving competitiveness in fast-moving industries
Whether you're modernising legacy reporting systems, moving to the cloud, or building your first enterprise data platform, dbt and AI offer a future-ready foundation.
Keyrus: Your strategic partner for modern data transformation
Keyrus South Africa is actively helping businesses across the region navigate the journey to modern data maturity. As a trusted partner, we provide more than just implementation services, we offer strategic guidance, training, and support tailored to your business needs.
Our teams have helped clients:
Build scalable transformation layers using dbt and Snowflake
Implement AI-driven workflows to accelerate development
Establish semantic layers to unify reporting across BI tools
Train and upskill internal teams to ensure long-term sustainability
If your organisation is ready to:
Strengthen its data foundations
Streamline its analytics lifecycle
Empower teams to work smarter, not harder
We invite you to start a conversation with Keyrus. Together, we can help you design and implement a modern data platform that delivers trust, agility, and business impact.