Overview
In this episode of The Dashboard Effect, Brick Thompson and Caleb Oaks walk through a practical technique that addresses one of the quieter but persistent challenges in BI work: finding realistic, usable data for testing, training, and demonstration purposes without exposing sensitive client information. The solution they walk through uses ChatGPT to generate synthetic datasets that look authentic, behave like real data, and can be scaled to whatever volume the work requires.
The conversation moves quickly from the rationale to the actual process, making it a useful reference for any BI professional who has wrestled with the gap between the data they have access to and the data they need to build and test effectively. See how Blue Margin’s Managed Analytics & Insights applies innovative development practices like synthetic data generation to deliver faster, more rigorous BI development without compromising client data privacy or security.
What This Episode Covers
Why Synthetic Data Matters (1:37 – 4:54)
High-quality datasets large enough to build and test real BI reports against are genuinely difficult to find. Public datasets are often too clean, too small, or too mismatched to the specific scenarios a developer needs to work through. Client data solves the realism problem but creates privacy and confidentiality constraints that make it unsuitable for demonstrations, training exercises, or portfolio work. Synthetic data generated through ChatGPT bridges that gap, producing datasets that include realistic email addresses, location codes, and other specific data types while keeping actual client information out of the picture entirely.
The Process for Smaller Datasets (5:07 – 6:54)
For smaller volumes, the ChatGPT web interface is sufficient. Users can specify the exact fields, formats, and volume they need and receive output that is ready to load into a reporting environment. The approach is fast, flexible, and requires no additional tooling beyond what most BI professionals already have access to.
Scaling with Python (5:07 – 7:24)
For larger datasets, the recommended approach is to use ChatGPT to generate a foundation of unique values and then use Python to scale those values into the full volume required. This combination keeps the generation process manageable while producing datasets large enough to test realistic performance scenarios, model behavior under load, and edge cases that only surface at scale.
Loading into the Data Stack (6:54 – 7:24)
Once generated, synthetic data is typically loaded into a data lake such as Azure Synapse and connected to Power BI in the same way real data would be. The workflow mirrors a production setup, which means the testing and demonstration work done against synthetic data translates directly to how the same reports will behave against real data when they go live.
Training Applications (9:31 – 10:05)
Synthetic data is particularly valuable for practicing complex modeling scenarios that require specific data conditions to work through properly. An accounts receivable aging report, for example, requires data structured around payment timing, aging buckets, and outstanding balances that a generic public dataset is unlikely to provide. Generating synthetic data tailored to that scenario allows developers to practice the modeling work in a realistic context without needing a live client environment.
Testing Edge Cases and Dirty Data (10:28 – 11:23)
Beyond clean demonstrations, synthetic data generation is useful for creating intentionally problematic datasets to test how reports and models handle edge cases. Generating dirty data to stress-test row-level security configurations, referential integrity rules, and error handling logic is significantly easier than waiting for those conditions to appear naturally in production and significantly safer than introducing test conditions into a live environment.
Who It’s For
This episode is worth your time if you are a BI developer or data engineer looking for a faster and more flexible way to generate realistic test data without relying on client datasets, a consultant or freelance BI professional who needs demonstration-ready reports that showcase real modeling complexity without exposing client information, a data team running training programs that require consistent, scenario-specific datasets for practice exercises, or anyone who has experienced the frustration of trying to build or test a report against data that does not adequately represent the conditions the report will face in production.
Why It’s Worth a Listen
The synthetic data problem is one of those workflow friction points that does not get much attention because it feels like a secondary concern compared to the modeling and reporting work it supports. This episode reframes it as a skill worth developing deliberately, and the approach the hosts describe is practical enough that most BI professionals can apply it immediately with tools they already use.
The dirty data testing application is particularly valuable. Most reports are built and tested against clean, well-structured data and then encounter the messier reality of production environments. Having a systematic way to generate edge cases and intentionally problematic data before a report goes live is a quality assurance practice that is easy to overlook and genuinely useful when something unexpected surfaces after deployment.
And for developers building portfolio work or preparing client demonstrations, the privacy dimension of the technique is not just a convenience. It is what makes it possible to show realistic, complex BI work without the legal and ethical complications that come with using real client data outside of a controlled engagement context.
