There is a pattern in how MSPs approach operational problems. Something is not working as well as it should. A vendor offers a tool that solves exactly that problem. The tool gets purchased, onboarded, and added to the stack. Six months later, the original problem is still present in a slightly different form, the new tool has introduced its own friction, and the stack is larger and more expensive than it was before.
The pattern repeats because the purchase decision is made before the problem is fully understood. And the fastest way to understand the problem more fully — before spending money on a solution — is to export the data.
A data export from any MSP platform — the PSA, the RMM, the billing system, the vendor portal — reveals the actual state of the operational problem the MSP is trying to solve. Not the perceived state, not the anecdotal state described in a support ticket or a team meeting, but the measurable state visible in the underlying data.
Consider an MSP who believes they need a new reporting tool because their client reports are taking too long to produce. Before purchasing a reporting tool, exporting the relevant data from the PSA and the billing system and attempting to build the report manually reveals one of two things. Either the data is complete and well-structured, in which case the problem is the reporting interface and a new tool might genuinely help. Or the data is incomplete, inconsistently formatted, or missing key fields, in which case the problem is not the reporting tool — it is the underlying data, and a new reporting tool will produce the same incomplete reports faster and more expensively.
This second scenario is significantly more common than the first. Most MSP operational problems that appear to be tool problems are actually data problems. The tool being considered is being evaluated against a data problem it was not designed to solve.
Because data exports are unglamorous and tool demos are not. A vendor demo shows the tool solving the problem in a clean, well-structured environment with complete, consistent data. It is designed to make the tool look effective. A data export shows the operational reality of the MSP's environment, including all of the inconsistencies, gaps, and accumulated technical debt that the demo environment was specifically designed to avoid.
Most MSPs never see the data export because they never produce one. They evaluate the tool against the demo, not against their own data. And when the tool fails to solve the problem in production, the conclusion is that a different tool is needed rather than that the data needs to be cleaned up first.
Export the data the tool will work with. Evaluate the quality and completeness of that data before evaluating the tool. If the data is clean and complete, the tool evaluation is meaningful. If the data is not clean and complete, address the data first.
This sequence requires more work than going straight to the demo. It also produces a much higher return on the eventual tool purchase, because the tool is being evaluated against data that reflects operational reality rather than demo conditions.
For billing-adjacent tools specifically, a Reconcile audit before a tool purchase is the equivalent of the data export exercise. It reveals the actual state of the billing data — what is accurate, what has drifted, and what would need to be resolved before any new tool could be expected to produce accurate outputs. The audit frequently reveals that the problem the MSP was planning to solve with a new tool is actually a billing data problem that Reconcile addresses directly.
Why does exporting data before buying a new tool lead to better purchasing decisions?
Because most MSP operational problems that appear to be tool problems are actually data problems. A data export reveals the actual state of the underlying data rather than the perceived state described in team meetings or support tickets, making it possible to determine whether a new tool would solve the problem or simply produce the same incorrect outputs faster.
What does a data export typically reveal that a vendor demo does not?
The actual quality and completeness of the MSP's operational data, including inconsistencies, gaps, and accumulated technical debt that vendor demos are specifically designed to avoid. When the underlying data is incomplete or inconsistent, most tools fail to solve the problem regardless of how well they performed in the demo.
How does this principle apply to billing and reconciliation tools specifically?
A billing data audit before a tool purchase — such as a Reconcile audit — reveals the actual state of billing accuracy: what is correct, what has drifted, and what would need to be resolved before any new tool could produce accurate outputs. It frequently reveals that the problem is a data problem that reconciliation addresses directly rather than a tool gap that requires a new purchase.