A manufacturer I was talking to recently almost lost a decades-old warehouse lease. The lease existed on paper. One copy, no scan, no backup. When a records dispute came up, it turned out their copy was the only one left anywhere. The landlord’s own file had gone missing.
They got lucky. But what nearly cost them a building had nothing to do with data quality. It was a single point of failure, and that’s far more common in mid-market companies than most BI vendors like to admit.
When the Data Was Never Captured
Every analytics vendor tells a similar story about dashboards and a single source of truth. That story works when the data already exists somewhere and is just scattered, duplicated, or poorly modeled. Clean it up, model it properly, and you get there.
For a lot of mid-market manufacturers, that’s not the situation. The data was never captured in the first place. You can’t build a single source of truth on a paper lease, or on a buyer’s memory of what a supplier charged last year. A maintenance log that lives in one technician’s head won’t feed a dashboard either.
That changes where you start. Dirty data means fixing the pipeline. Missing data means digitizing the process that creates it, before analytics can do anything useful. Skip that step and you get the BI rollout that stalls weeks after go-live: the dashboard looks great, but half the numbers underneath it are guesses.
Silos Hide the Real Story
A pattern I see constantly. Picture a week where deliveries get hit by an outside disruption, a storm or a delayed shipment. Sales dip the same week. Nobody connects the two, because the people tracking the disruption and the people tracking sales use systems that don’t talk to each other.
So the sales dip gets treated as a sales problem. Reps get questioned. The forecast gets cut. The actual cause sits in a field report nobody thought to check.
The instinct is to say “we need better BI.” But more dashboards on top of disconnected systems just show you the wrong story with nicer charts. What fixes this is capturing operational data somewhere structured from the start, so it can be lined up against the financials when something needs explaining.
Start Small, Prove It, Then Expand
None of this means digitizing everything at once. That’s how these projects die. The scope gets too big, nothing visible ships for months, and the people who championed it lose interest or move on.
A better path is one narrow slice. Pick a single operational area with a fast, clear payoff, like order processing or inventory, and prove the value there. Once people have seen a real win in their own numbers, expanding is a much easier conversation.
This is where SAP Business One users have a head start. Your orders, inventory, and invoices are already captured in a structured system. That’s a natural first slice, one that can show results in weeks, and it gives you a base to build on as more of the business moves off paper.
Fix the Foundation First
If your BI rollout keeps stalling, the problem probably isn’t your BI vendor. More likely, a real share of your operational knowledge still lives on paper or in someone’s memory, where no system can reach it. Deal with that first, and the dashboards will finally have something true to show you.
Not sure which of your numbers rest on solid ground?
Book a 30 min strategy session with Corey.
He’ll walk through where your SAP B1 data is strong and where the gaps are.
