Spend analysis or spend cleansing, what’s the difference?
In a very linear sense, spend cleansing happens before spend analysis, but both can happen simultaneously.
To get the most out of spend analysis, there needs to be some coherence to your procurement data. But we don’t believe in “garbage in, garbage out” as a hard rule (read Procurement❤️Data to learn why).
You can already begin spend analysis without perfect data. As you work more with the data, you can identify errors and make updates, improving the efficiency of cleansing.
Spend cleansing is a continual process since new spend data is always coming in. So, spend analysis and spend cleansing go hand-in-hand.
The same goes for data enrichment, which improves the visibility into your spend for all users.
Examples of data errors
Error-ridden spend data has side effects that impact the entire organization. It can lead to inefficient processes, decisions based on inaccurate data, loss of visibility, compliance issues, and ultimately negative customer and supplier experiences.
Let’s say you use three ERP systems across different procurement groups. Users could enter multiple supplier names, use different currencies or languages, or make small mistakes in category codes. Combining this master data for analytics will lead to much confusion.
Incorrect master data needs to be addressed before accurate analysis can be done.
Here are the most common examples of procurement data errors:
Duplicate supplier names (e.g., PricewaterhouseCoopers, PwC, and P.W.C.)
No standard format for dates (e.g., 1-15-23 or 15-1-23)
No standard units of measure (e.g., litres, liters, LT, or Lt)
General or misleading category descriptions (e.g., “Services” could mean anything from plumbing to auditing)
Duplicated product codes (e.g., IT055 could be entered as It005, ITOO5 or similar)
Incorrect currencies or time zones (e.g., monthly cut-off times listed in the varying time zone)
Different document languages or broken characters
Implementing a Spend Cleansing Project: A Step-by-Step Guide
To start cleansing your procurement data, you’ll need many helping hands. One procurement person alone cannot do this. Usually, the IT department is heavily involved in managing master data.
But analytics providers like Sievo don’t need to alter your master data, only extract it for cleansing purposes.
Start by getting a broad view of how spend data is managed. Pay particular attention to:
How is spend data input into your system?
How many ERP systems are in use?
Who can change or edit spend data?
What data policies currently exist?
What category structures are in use?
What spend data would most benefit from cleansing?
What areas are irrelevant for analytics?
By getting a basic idea of the data landscape, you can identify the most critical areas first. Remember, you eat an elephant one bite at a time.
Identify areas for improvement
Once you have set the scope of data for cleansing, it’s possible to narrow down the areas of improvement. For most organizations, supplier normalization is the main challenge. Text-based fields have a high chance of error, such as category descriptions.
If your data is in generally good condition, it might make sense to focus on enrichment activities, such as translations and currency harmonization.
For international businesses with many procurement centers, having comparable data in one language and currency is necessary for large spend analysis projects.
For spend analysis, you’ll want to remove unrelated or un-addressable spend which causes noise.
Look for areas to automate
For many organizations, they will have millions of data points to consider. Machine learning and other forms of automation are incredibly efficient ways of processing large amounts of data.
Human-machine collaboration is still necessary, but that’s even better! Training an algorithm with human oversight is much more accurate and efficient than either alone.
During implementation, Sievo conducts a one-off cleansing project without changing your master data. Based on your needs, we construct automated processes that continually keep your new spend data clean.
You don’t need perfect data to gain spend visibility: our procurement expertise provides excellent results no matter the industry.
Also, you’ll gain the ability to enrich your data and make it even more insightful, with added attributes and descriptions fit for purpose. When master data is not there, our systems extract key information from other sources.
We believe in the democratization of data, which is why Sievo enables user-generated error correction with a clear audit trail.
Data cleansing is not a one-time effort. With the positive feedback loop of analytics insights, data quality improvements, and enrichment, you’ll see much greater results from your procurement data.