With Tableau's latest update, which provides each Tableau Cloud deployment with multiple Cloud sites that can be dedicated to Production, Testing, and Development environments, it's a good time to review data governance and operational best practices in the world of data visualisation. The update is good news for Tableau users, and part of a broader story that began with the evolution of software development practices.
The Impact of Git
Back in 2005, Linus Torvalds, the creator of Linux, introduced Git. This distributed version control system improved collaboration among software developers by enabling them to track changes, experiment in isolated branches, and merge updates without fear of overwriting critical code. Git fundamentally changed how software teams worked, fostering collaboration, accountability, and innovation.
Git's adoption drove the proliferation of better workflows with separate environments for development, testing, and production. Developers could innovate safely without the constant fear of breaking a live application. Code reviews, version history, tests, and rollbacks became standard practices, further enhancing quality and reducing errors. The software world quickly recognised that structured, layered environments were indispensable to maintain stability while continuously delivering new features.
Data and dbt’s Influence
A few years later, data teams saw the potential benefits of similar workflows for their own processes. Enter dbt (data build tool - yes lower case), which emerged for SQL-based data modelling. Founded in 2016, dbt popularised the concepts of version control, testing, and modular reusable SQL. With dbt, data teams could create staging, development, and production models, just like software engineers.
The impact of dbt was profound. SQL transformations were no longer opaque processes handled ad hoc; instead, they became collaborative, testable, and documented workflows. Teams could run automated tests to catch anomalies, use version control to track changes, and collaborate without stepping on each other's toes. dbt effectively introduced software development best practices into the data domain, empowering analysts and engineers alike to build robust data pipelines.
For the non-technical reader, it basically introduced "save, undo, review and track changes" into database queries, which is a huge help.
Bringing Best Practices to Visualisation
It always seemed unusual that back-end code had such strict quality management but the front-facing reports and dashboards were a free for all to edit and update. For years, data visualisation tools lagged behind in adopting these structured workflows. Dashboards were often built and updated directly in production, leading to errors, inconsistencies, and the dreaded "Why did the chart break today?" moments. The lack of dedicated development and testing environments was a strange anomaly given that this was the output often seen by managers and executives. Analysts and developers had to tread carefully or risk causing disruptions to business-critical reporting.
Tableau’s announcement of dedicated development, testing, and production environments changes this and brings better governance to this. Now, analysts can experiment and innovate in the development site without fear of affecting live dashboards. Content can then be promoted to the testing site for feedback and validation from business users before reaching the production environment. It's a simple but important update.
The addition of the Content Migration Tool further enhances this workflow by simplifying the promotion process between environments, making it easier to adopt consistent practices. These updates demonstrate that Tableau recognises the lessons learned from software development and data modelling workflows and is applying them to the world of data visualisation.
This shift is not just a technical upgrade—it represents a cultural change in how organisations view their data. Testing, feedback, and iteration are becoming integral parts of the data visualisation lifecycle, leading to more robust, reliable dashboards and more confident decision-making.