Every tool its own truth: what scattered software costs sales and operations

The customer at the counter asks whether the last item is still available. The salesperson checks the point of sale: one unit. The warehouse worker checks the inventory package: zero. Both read correctly what their screen shows, and that is exactly where the problem sits.

That difference is not a bug. It is the result of a choice no one made deliberately, namely that each system keeps its own copy of the truth. The point of sale does not know what the warehouse booked, the warehouse does not know what sales promised. Between those two screens stands a person who reconciles the difference, usually with a phone call.

A hundred tools, a hundred truths

Figures from BetterCloud (2024) show that the average company used about 106 SaaS applications that year. Each package was once chosen to solve a real problem: a better quoting tool when the previous one grew too slow, a point of sale the shop floor finally enjoyed using. None of those choices was wrong in itself. Together they do form a landscape in which well over a hundred applications each keep their own piece of data and rarely talk to one another.

That fragmentation costs money in two ways. The first is visible on the invoice. The Flexera 2024 State of ITAM report estimates that organisations waste 20% to 30% of their IT spending, and that more than half of SaaS licences go unused. You pay per user, per month, also for the accounts no one has logged into for months.

The most expensive cost is not on the invoice

The second way is larger and shows up nowhere in the accounts. It is the work between the tools. Someone copies the quote over into the accounting package. Someone calls the warehouse to check a figure. Someone exports a list from one system to import it into another. Your people have become the connection the software does not provide, and they do that work again every day.

For sales that is a direct loss. A quote based on yesterday's stock figure promises a delivery date the warehouse cannot meet. The salesperson who calls to confirm loses half an hour and sometimes the customer's trust. Whoever does not call sells something that is no longer there. Both cost margin, and neither shows up in a dashboard.

The licence is the cheapest line. The most expensive is the time lost to reconciling systems that do not know each other.

What shared data changes

One platform does not solve this by adding software, but by allowing fewer truths. Stock, customers, quotes and tickets sit in one data model. What sales promises, purchasing sees at the same moment. What arrives in the warehouse is on the next quote right away. So the same stock drives both the buying and the selling at once, instead of reconciling both after the fact.

• Quotes, CRM, ticketing and point of sale behind one login, without retyping between systems.
• Stock that works in both directions: purchasing sees what sales reserves, sales sees what is actually on the shelf.
• One update across all locations at once, instead of five packages each running their own version.
• Offline at the counter, so the point of sale keeps working when the line drops and syncs as soon as it is back.

Operations feels the gain mostly in that last point. Anyone who runs multiple locations knows the evening when one shop is still running an old version and the prices no longer match. With one platform there is one update, at one moment, for everyone. The counter keeps running in the meantime when the internet stutters, because a point of sale should also work without a connection and only sync as soon as the line is back.

IT looks at something else: do you still own the data afterwards? Consolidating onto one platform must not become a new dependency on a single vendor. The gain of shared data only counts for real when the data model is open and the code sits in your own repository, so you win the coherence without getting a lock in return.

So do not start with the question of which package should go. Start with the two screens that today show a different number for the same item. Bring those together in one data model, measure what the retyping and calling between them cost you, and only then decide about the next piece. From a hundred tools to one platform then becomes not a switch on one date, but a first wave with an outcome you can measure afterwards.