In these difficult economic times, cutting costs isn’t just optimization, it’s survival. You can’t reduce what you can’t quantify so it’s critical to analyze the accounts payable (AP), or purchasing data to identify the areas where cost savings are possible. This is one of the most useful financial data marts because spending is often something that can be controlled quickly once understood.

In the next series of posts I am going walk through the design and implementation of a purchasing data mart, including its fact tables and dimensions to allow us to analyze some typical purchasing data. I’ll build this data mart model using the Datamartist tool.

This will create a “snapshot” analysis of purchasing data with a desktop data analysis tool that can be built quickly yet will access millions of rows of data, and deal with data quality issues such as duplicate rows.

For the purchasing data mart model that we’ll be defining, I’ll use a fictitious company that manufactures and sells a broad range of things- the “Acme does everything company”.

Acme is a long standing enterprise, with a number of offices and factories in the US. But they’ve never done an in-depth analysis of their costs because they didn’t have to until now- profits were good, and the business was growing well. But then the economy took a turn for the worst, and Acme’s customers are cutting back on pretty much everything. Acme’s CFO has announced that if costs aren’t reduced quickly, Acme is going to simply run out of cash. He wants you to head up the analysis on the company’s purchases- where can Acme save?

I look forward to showcasing the functionality in the Datamartist tool that makes it possible to do this without programming, and without requiring database software, developers or servers. This kind of snapshot, immediate data transformation is what we think will make Datamartist such a cost effective and efficient addition to any serious analyst’s toolkit.

This is part of a 5 part series- here are the links to the various parts: 1,2 , 3 , 4 and 5