Most data projects fail not because of the technology chosen, but because business logic is undocumented, inconsistent, and nobody wants to admit it.
We've worked with clients where a straightforward reporting automation project revealed that two different departments were calculating the same metric two different ways, and nobody knew. The number on the CFO's dashboard and the number the operations team were tracking had quietly diverged. Both teams thought they were right.
That's not a data problem. That's a business logic problem that data work exposed.
The Conversations Nobody Wants to Have
The real value of a data warehouse project isn't the architecture or the tooling. It's that it forces your organisation to answer questions that have been quietly avoided:
- What does this number actually mean?
- Why are these two reports showing different figures?
- Who is the source of truth, and are they still here?
These are uncomfortable conversations. They surface disagreements between departments, expose decisions that were never formally made, and sometimes reveal that processes everyone assumed were standardised are anything but.
What This Means for Your Project
If you're embarking on a data project and nobody is having uncomfortable conversations about business logic, the technology is getting ahead of the problem. A well-built data warehouse sitting on top of inconsistent definitions doesn't solve anything. It just makes the inconsistency faster and more expensive to produce.
The organisations that get lasting value from data projects are the ones willing to slow down at the start and do the definitional work: agreeing on what things mean, who owns them, and how disagreements will be resolved going forward.
That groundwork is rarely glamorous. But it's what separates a data project that delivers from one that gets quietly shelved eighteen months later. If you're starting that work, we can help.