Very simply, “spend analytics is easier than you think and can deliver results very quickly” was the core message of my previous article in this series on spend analytics. It was a bit of a rant and a soapbox moment, but in this day and age, there is no reasonable excuse for a procurement team in an organization – whether private, not-for-profit or public in nature, small, medium or large in size – not to know how much it spends, where, on what and with whom.

In my previous article, I alluded to the fourth excuse we often hear an organization give for why they can’t do spend analysis – their data is so bad that no one, anywhere, could fix it. In any system or organization where hundreds, if not thousands of people can buy goods and services, there will inevitable data quality issues that make spend visibility more difficult, but certainly not impossible.

Some of the key data culprits are: errors in the data extraction, duplicate suppliers (a problem which increases as purchasing card spend increases), inconsistent, incorrect or missing classifications and lack of additional information about suppliers (size, diversity and location). Here, I want to provide a process which you or your chosen partner can take when you have decided that you are ready to overcome those data challenges, and you are no longer willing to live with poor quality spend data.

Overcoming the Data Deficit — Collect and Normalize

If you are part of a small organization, you may very well be able to do all of this on spreadsheets with your existing team. If you are a medium-to-large organization, the process becomes more difficult, but the same basic theory still applies.

First, collect the data from each of your transaction systems. At a minimum, this will be accounts payable data for every organization. Layered on top of that may be pCard data, eProcurement data and/or contract data. At a bare minimum, you need to know who was paid, at least one date (invoice, payment or accounting date typically), how much was paid, an invoice number and, ideally, the part of the organization which spent the money (department, business unit etc.)

Second, put the data you have into standardized columns. In other words, you may see “transaction date” in your pCard spend file and “invoice date” in your AP file. One source may say “supplier name” and another may say “vendor name”. The purpose of this step is to take each heading and match them between files. This could be done in Excel or Access, but there are more robust tools used by data scientists to complete this step as the dataset gets larger.