When I worked at the Price Club we started looking for a replacement merchandising system. Oddly enough the project was called NMS for New Merchandising System.
One of our requirements was for Average Weekly Sales. We looked at a software package used by Boscovs which is a regional department store in Pennsylvania. It appeared to fit the bill for our requirements. We asked if they had Average Weekly Sales as a check off on our requirements. The vendor replied yes. Awesome, check that one off. Other questions got good answers so we thought we had a good fit.
When we got the software installed we soon found out that we really required Average Weekly Sales by Item by Warehouse/Store. Boscovs, being the regional retailer they were and not too spread out, only had Average Weekly Sales by Item. Not by location? Whoops! We didn’t go deep enough into their data model to discover that issue. We might have also assumed that everyone had Average Weekly Sales by location so didn’t bother to ask if it was by location.
The idiom “the devil is in the details” comes to mind.
So my lessons learned from this was:
Look at the underlying data model for a software package to match up your requirements. They had Average Weekly Sales but it wasn’t an attribute on the right entity in the data model.
Be careful about any assumptions (even hidden) you might be making.