Procurement teams constantly run into the same wall when they try to grow their addressable spend, rationalize the supplier base, and negotiate better terms: the data isn't ready. This is a deep dive into the data-quality and homogeneity challenges behind that, and how to resolve them.
The challenge
At the World Digital Procurement Summit in Berlin, we ran a live poll on the key challenges of expanding addressable spend. Across industries and company sizes, the answer was striking and consistent.
of the room pointed to lack of access to data and multiple sources of data as their main barrier.
We've written before about how technology bridges the IT-support gap. Here we want to tackle a different problem head-on: harmonizing data across many sources of information.
Why data harmonization matters
Spend data is usually spread across different systems and locations, managed under inconsistent governance by fully independent organizations. The outcome is a mixture of formats with varying completeness, plenty of duplications, and the occasional error adding noise. It becomes genuinely hard to get a consistent, granular, accurate view of the supplier base.
Leave that data untransformed and it leads to sub-optimized insights, missed savings, redundancies, and errors that hit the bottom line. The value and ROI of harmonization are immense, and badly underappreciated. The ideal state is a smart engine that processes, enriches and transforms every variety of spend data into one consistent, improved, accurate view ready for analysis.
Two places it pays off
Two aspects have outsized impact across both direct and indirect spend: the supplier records, and the transactions.
Supplier records
Normalize & enrich- Normalize supplier names across every source
- Map each supplier to a unique identifier
- Find parent-child relationships from external data
- Identify and merge duplicate suppliers
Transactional data
Categorize to one taxonomy- Categorize every transaction to the master taxonomy
- Consistent analysis across all spend
- Reporting that lines up source to source
- A granular, comparable view of the whole estate
The first step is to normalize supplier names and identify the relationships within and across each data source. Mithra maps every supplier to a unique identifier, so the same vendor is the same vendor everywhere, then uses external data to resolve parent-child relationships and merge the duplicates.
Throughout the process, subject-matter experts review and overwrite where needed. The machine-learning models capture that feedback and apply it to similar transactions in the future, so the system gets smarter and the same correction never has to be made twice.
The payoff
Data harmonization is foundational to procurement success. This approach to merging spend data gives your team a single, unified view in a matter of days, not the months it usually takes.
With accurate, meaningful data, your team makes better decisions exponentially faster, and realizes value for the organization and its end-customers.
One view, in days
A smart engine merges scattered sources into a single, consistent, accurate view of spend in days rather than months.
Addressable spend expands
Clean supplier records and categorized transactions unlock spend that was previously invisible, the basis for consolidation and better terms.
The system keeps learning
Expert feedback is captured once and reused, so accuracy compounds and harmonization holds as new data arrives.
The ROI of harmonization is immense and underappreciated. Let us show you what one trusted view of your spend looks like, on your own data.