How to find millions in your business, fast

Look in two places most cost exercises skip. First, what you spend producing information: licenses, the warehouse underneath, and the fully-loaded salaries of everyone whose output is a report. At a mid-sized multi-site operator that is commonly $1.4M to $3.4M a year, and most of it sits in payroll rather than a software line. Second, what you lose because nobody measures it: recognition leaks, discounts outside policy, marketing sitting in weak territories, and the bottom quartile of locations nobody has time to examine. Those four are usually larger than the first and appear in no ledger. The five checks on this page find them with data you already have.

Price the whole thing first, because almost nobody has. Add the licenses, the warehouse or compute meter that bills separately, the training, and the fully-loaded cost of every person whose output is a report. Then ask which of those lines is buying decisions and which is buying production. The production half is what modern platforms remove: SQOR.ai is $48,960 a year for 25 users all-in with the warehouse included, against a fully-loaded traditional stack that our comparison pages itemise at $225,000 to $1,225,000 depending on the vendor.

One junior data analyst or engineer costs $85,000 to $125,000 out of a strong program before benefits and payroll taxes, and a fully-loaded pair of them is around $225,000 a year. SQOR.ai for 25 users is $48,960 a year all-in, which is less than half of one of those hires, and it includes the data warehouse. The honest caveat: a platform does not replace judgment, and you still want people who know your business. It replaces the production work, not the thinking.

Margin leaks are systematic rather than random, which is what makes them findable. The four that recur are discounting outside policy, revenue recognized incorrectly, cost drifting in a category nobody reviews, and price not moving when input cost moves. Each one is invisible per transaction and material per year. Finding them requires comparing behavior across every product, branch and month, continuously, which is why they persist: it is a standing job rather than a project.

Almost always in the parts you never instrumented, because instrumenting them cost analyst hours you could not justify. In practice that means the bottom quartile of locations, the products with small volumes and thin margins, the customers who quietly became unprofitable, and the processes where an exception became the norm. The common factor is not that these are hard to measure. It is that nobody was ever funded to measure them.

Present it as a cost substitution rather than a new line, because that is what it is. Show the current fully-loaded information production cost, including the payroll portion, then show the platform cost against it. Then show one specific leak you expect to close with a number attached, and commit to reporting whether it closed. A board approves a substitution with a measurable outcome far more readily than it approves a capability.

Traditional business intelligence has a poor measured record, and the honest reason is that most of the spend goes to producing reports rather than changing decisions. The return arrives when a specific decision changes and the outcome is tracked. Insist on that structure: name the decision, name the measure, and check afterwards. Any platform that cannot show you whether its recommendation worked cannot demonstrate a return, whatever the deck says.

Pull the usage report before the renewal, and count logins rather than seats. Shelfware is normal in this category, and one operator we work with retired 250 dashboard licenses outright once the questions those dashboards existed to answer could be asked directly. The license saving was the smaller half; the larger half was the people who had been maintaining them moving onto work the business was waiting on.