Procurement data cleansing.
Good data enables good decisions, but in most organisations the quality of procurement data gets in the way. PI cleans, consolidates and categorises your spend data, so you spend less time correcting it and more time acting on it.
From a raw ledger line to a category you can act on.
Spend is often misclassified or inconsistently categorised, and data sits in systems that do not talk to one another. Press the button and watch six raw lines get cleaned, matched and categorised.
Data you can analyse, not just store.
PI's data cleansing turns raw spend data into usable information. We correct the errors that stop analysis:
- Manual entry errors and inconsistent supplier records, so each supplier is counted once.
- Misclassified spend and lines coded to the wrong account.
- Data from several systems combined into a single source of truth.
One taxonomy for all of your spend.
Without an accurate categorisation schema, decisions fall back on rules of thumb and best guesses. PI maps every line to one taxonomy using extensive rulesets and our database of categorised data, with automated categorisation that learns as it goes.
Use an industry standard schema such as UNSPSC, your own internal schema, or a hybrid. You stay in control of the rules that decide the outcome. See how the engine decides.
Then put it in front of the people who decide.
Clean, categorised data flows into ProcureTRAK dashboards designed for non-technical users, so the whole organisation can use what the data shows, not only the people with their heads in the data. See the PI spend cube.
Frequently asked questions.
What is procurement data cleansing?
Correcting and standardising spend data so it can be analysed: fixing entry errors, consolidating duplicate supplier records, correcting account coding and bringing data from different systems together. It is the step that makes spend analysis trustworthy.
What is the difference between data cleansing and data categorisation?
Cleansing fixes what was recorded, such as supplier names and account codes. Categorisation then assigns each cleaned line to a category in one taxonomy, so spend can be added up and compared by category.
Which taxonomies can you use?
Industry standards such as UNSPSC or SIC, your own internal schema, or a hybrid of the two.
Is data cleansing a one-off project?
It can be. Most of PI's work is by subscription, and we also take on one-off projects such as a spend review or a tender.
Is your data a mess?
Tell us where your spend data lives and we will show you what it looks like cleaned and categorised.