Data accuracy is an investment, not a cost!
Are you struggling to get accurate spend information because of incorrectly classified data, unclassified data or limited visibility? Without it, it’s difficult to negotiate better rates with suppliers, track KPI’s, reduce spend, check compliance or make good business decisions.
Accurate data allows you to do all of this, plus prevent costly mistakes.
You could automate your data classification, but this isn’t fool proof. I’ve developed a method to efficiently and accurately classify, cleanse and verify data to minimise the errors in the same amount of time.
A Brief Overview
Data Classification & Normalisation
Your data can be classified from an existing taxonomy or one customised and built by us. We can work with single or multiple file sources and the preferred format to receive this is Excel/CSV.
We start by normalising your suppliers, this allows us to classify more efficiently and accurately. Normalisation involves creating a new column within the data, and removing things the suffixes, quotes, dashes and commas from supplier names.
This is built as the data is classified so that all your spend can be accurately accounted for and classified using your business terminology – no more trying to squeeze some spend in to a vaguely relevant category!
This means as an organisation you can report on exactly what you need to and how you want it. And just like our classification services, the number of levels in your shiny new taxonomy will be reflective of the quality of your data. But we don’t just build it for the right now, we’re also thinking about what you might need in the future, so that you get as much value from it as possible.
Are you implementing a new P2P system and need a “golden” or master record for each supplier, but have multiple records or accounts for the current suppliers, with incorrect or missing information and multiple payment terms?
The missing or incomplete information could be a simple as some missing state information, or more complex such as missing tax ID’s, VAT numbers, or company registration numbers. Or you might need additional information supplier diversity status.
Do you struggle with duplicate records, full names in a single column addresses poorly formatted with missing information, phone numbers with spaces, without spaces, with or without the dialling code, or missing the leading 0?
It’s a nightmare. This could even be be sitting across multiple ERP or CRM systems that you don’t know about, but not to worry, we can bring them together.
It’s great to get someone in to fix your data, but we always recommend where possible for you to manage your own data. By working with it regularly, not only do you become familiar with it, but you start to spot and correct errors far quicker than if they were left.
Suppliers like I.B.M, IBM Inc and IBM Limited are all normalised to “IBM”. This means that you get a true picture of what you are spending with each supplier, which could vary hugely from what you are seeing right now.
Tools of the trade
We don’t use use fancy tricks here. No AI, neural networks, algorithms or any of that fanciness that is designed to scare or intimidate you, we are transparent in our methods. We are a group of classification and cleansing specialists with nearly 40 years of experience, and we do it the original way.
We classify and cleanse the data manually with the help of some software called Omniscope. (If you use this link, you’ll get 10% off when you subscribe). Our founder and MD, Susan, has developed a proprietary methodology to efficiently and accurately cleanse and classify data and has trained the team using this methodology.
Don’t get us wrong, it’s still a very manual process, but we LOVE it! There’s nothing more satisfying than taking a messy data set and tidying it up. And guess what? We can still deliver back your project in around the same time as other companies using automation, the difference being we’ve been through every.single.row. to make sure it’s right for you.
And then if you decide to automate? Not a problem, your nice new shiny data set will be a great base as a training data set. Why do you need this? Well AI and machine learning need clean, accurate data to learn from, otherwise it just creates more dirty data. You can read more below.
Get in Touch
For more information about how we work or any of our services, feel free to contact us using the form below.