Our Data Is Ready': The Myth That Costs You

Hrvoje Spreicer is an SAP Business One consultant for logistics, but he didn't get to this role through textbooks. He got there through warehouses, wholesale, and retail. He spent more than 20 years "in the field" as a warehouse worker, warehouse manager, deputy head of procurement, and head of service. Then, as a key user in the rollout of a new ERP system, he discovered what he really wanted to do.

Today he passes that experience on to clients he helps with SAP Business One implementations. Unlike consultants who come purely from the technical side, he has gone through all the operational steps himself, so he knows where a project most often gets stuck, and that is almost never where the client expects at the start.

Every SAP consultant knows that moment. The client says, "Our data is clean, we can start." The consultant nods, opens the first file, and then comes the silence. In that silence, says Hrvoje, the real work begins.

We sat down with Hrvoje to hear what really happens between the first meeting and the first day of work in the system. It turned out that the story isn't just about Excel spreadsheets and item codes. It's about people who find it hard to give up their habits, about deadlines that slip, and about why data quality is a discipline rather than a box you tick once and forget.

The First Week, the First Reality

Hrvoje describes the first week on a new project as a search for details that nobody mentions first. You go through the business: procurement, sales, warehouse. You look for the small deviations that complicate things the most.

Users often leave such things unsaid, mostly because they're a normal part of the business to them and they assume that's how it should be. Most often these are special processes for issuing goods or invoices, ones the company has done its own way for years and never written down.

The hardest case he has inherited wasn't a mess but a surplus. The client wanted all data, literally all of it, to go into the new system, just in case. They agreed to cut it down to what was realistically needed. Then the client, through an internal decision, still decided it wanted everything. The result: longer data entry, higher cost, and a system that was less clear than it should have been from day one.

Interestingly, says Hrvoje, the data itself is rarely the biggest obstacle.

"The bigger challenge is people who find it hard to give up old habits and want the new system to look as similar as possible to what they're used to. Then, besides being consultants for the new software, we also have to be business consultants and convince clients that some changes need to be accepted to make their business simpler."

When the System Forces Order

SAP Business One is a solution tailored to small and medium-sized companies, yet many clients arrive convinced the system will be too complicated. Hrvoje says it's usually just a matter of adjustment: the data clients already have is simply scattered across several different places.

And here something happens that you could call a gentle push toward order. More and more clients have to track things they haven't tracked before, for internal or legal reasons. And the moment when a client says, "If we had known this sooner, we would have started right away"? It almost always comes with the reports.

What the Client Doesn't See

Most of a consultant's work happens out of the client's sight. Deadlines are a constant struggle, but the real challenge is data cleanliness, and it requires the client's involvement before the project even gets seriously underway. If this is neglected at the start, consultants are left with less and less room to maneuver later.

When a client insists that their processes are so special that the system has to adapt to them rather than the other way around, Hrvoje doesn't get into an argument about who is right. He acknowledges that SAP Business One leaves room for customization in appearance, processes, or data display. But the argument that usually prevails in the end isn't technical, it's a business one: transparency through standardized processes and document traceability reduce the possibility of both intentional and unintentional errors.

And where is the line between "give me perfect data" and "start with good enough"? That decision, says Hrvoje, ultimately isn't his. Whether the data is perfect or good enough is the clients' task in the process of preparing the system.

Data, AI, and the Next Five Years

The growing talk about artificial intelligence in ERP systems, says Hrvoje, comes down to one thing in practice: less time spent executing tasks inside the system and more time for the actual business. Goods receipt, which today takes a few minutes, could be reduced with AI to a single click that generates all the necessary documents.

The human factor, he adds, remains indispensable in moments when something isn't quite perfect. The finding that 74% of procurement managers believe their data isn't ready for AI doesn't surprise him. In his view, AI readiness isn't something a single company can solve alone; changes are needed across the entire chain, from suppliers to customers.

A Message for Those Starting an ERP Project

When asked for one thing every director should know before signing an ERP implementation contract, Hrvoje says:

"The changes SAP Business One brings can sometimes seem complicated at first, but it's all part of the adjustment process. If you've been working partly through an ERP, partly in Excel, and writing the rest down in a notebook, that doesn't mean the process was faster."

He boils down perfect client preparation to three steps: an open and detailed description of all business processes, without holding back; properly prepared data, preferably less but truly needed, rather than everything just in case; and preparing people, not just the system, because resistance to change almost always comes from those who were forgotten in the story.

Hrvoje's story isn't one of perfect projects. It's a story about guiding people through the discomfort of change, and about the belief that order in data isn't achieved with a single decision, but with hundreds of small conversations before the first record is even entered into the system.