Database Schema Design: Why Your Customers Can’t Query Your Data (and How to Fix It)

If you’re building a SaaS platform or data product, it’s important to consider what BI tools your customers are already using. They want to connect Tableau, Power BI, Logi Symphony, or their own analytics stack directly to your data. They want SQL access, and to query your platform the way they query everything else.

But expectations don’t quite meet reality once as tickets start flooding in. This is a common problem amongst ISVs, which receive an average of 6-20 analytics requests every month on average, according to a study by insightsoftware and Hanover Research. Nearly half (45%) of organizations devote 40%-59% of their software development budget to building and supporting analytics.

Despite the platform working exactly as designed, customers may find themselves stuck. They can’t figure out which tables to join. Field names don’t match what they expect. Relationships that exist logically in your data aren’t surfaced anywhere their tool can discover. Queries that should be simple return nothing useful, or don’t run at all. The platform is working exactly as designed. The customers are still stuck.

This is one of the most common friction points for SaaS and data platform vendors, and it almost never gets resolved by changing the database. Here, we discuss why your customers struggle to query data and how to fix it.

Why Internal Schemas Aren’t Built for Analytics

A schema designed for an operational application and a schema designed for analytics access solve different problems. Operational schemas are optimized for write performance, transactional consistency, and application logic with tables structured around how the application reads and writes data instead of how an analyst thinks about it.

That often means highly normalized structures where a concept a customer thinks of as a single entity is spread across multiple tables. Field names that made sense to the engineers who built the system might not be user-friendly, while key relationships that exist in application code aren’t declared in the database, and BI tools can’t discover them.

The problem is baked into the design itself, the expected output of building a schema for one purpose and then asking it to serve a completely different one.

What Most Guidance Gets Wrong

If your customers struggle with your schema, instinct often tells us to treat it as a schema design problem.

While actions like adding better documentation, renaming confusing fields, and creating additional views makes sense if you’re starting fresh, for a production platform with customers already on it, they could cause more problems than they solve. For example:

• Renaming fields breaks existing integrations.
• Restructuring tables requires coordinated migrations.
• Adding a parallel analytics-friendly schema means maintaining two data models simultaneously, with all the synchronization overhead that comes with it.

Fundamentally, this approach doesn’t get to the heart of the problem.

Schema Exposure Is a Product Decision

The gap between your internal data model and what your customers can query is a connectivity problem at its core.

When a customer connects their BI tool to your platform, something has to translate between your internal schema and the interface their tool expects. That translation layer determines what table names they see, which relationships their tool can discover, what data types get reported back, and whether a simple drag-and-drop query in Tableau produces a result or an error.

Most data vendors don’t think about that translation layer explicitly. The connection works, which make it feel like the job is done. But working and queryable are different standards, and the gap between them is where the support tickets live.

Treating schema exposure as a deliberate product decision means asking different questions, such as:

• What do customers need to be able to do with this data?
• What does their BI tool need to see to make those tasks straightforward?
• How should complex or nested structures in the internal model be flattened into relational tables that analysts can work with?
• Which relationships need to be explicitly surfaced through metadata, even if the application enforces them in code?

The answer to these questions requires building a translation layer that sits between your data and your customers’ tools.

How Drivers Solve This

A driver built specifically for your platform can handle that translation. A driver can query your internal data model, map it to the relational structure your customers expect, surfac relationships and schema metadata that their tools can discover automatically, and returns results through a standard SQL interface over ODBC or JDBC.

From the customer’s perspective, connecting their BI tool works the way it would with any well-structured database, staying user friendly while the underlying complexity of your internal model is invisible to them.

From your perspective, the internal schema doesn’t change.

This is the architecture Simba SDK from insightsoftware is built to support. The SDK provides the query parsing, schema mapping, and metadata discovery infrastructure that custom driver development requires. It handles the mechanics of translating SQL into queries your data store can execute, mapping your internal structures to relational tables and surfacing the schema information that BI tools need to work correctly.

The result is that your engineering team focuses on the data source integration rather than the driver infrastructure. The translation layer between your platform and your customers’ tools gets built once, maintained as your platform evolves, and delivers consistent SQL access across every BI tool your customers use.

Ultimately, customers who can’t get value from their data don’t stay customers. But by building a translation layer deliberately rather than leaving it as an afterthought, you can build a platform that works for the people who depend on it. Simba SDK gives platform teams the infrastructure to do that without standing up a driver engineering practice from scratch with schema mapping, metadata discovery, SQL parsing, and standards-based ODBC and JDBC output are included. Your team connects the SDK to your data store and configures the schema presentation your customers need.

Ready to learn more? Watch Us Build an ODBC Driver in 30 Minutes: Simba SDK Live Demo.

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