Compass replaced data anarchy with one standardized view.
Four companies, four systems, one tangled spend picture. With Mithra, Compass extracts one document per company and sees results in week one.
Procurement, Performance & Transformation
Four companies · grown by acquisition
"We created a $10M savings pipeline using Mithra intelligence."
Murilo Benjamin Baggio on going from many spreadsheets and data loads to extracting a single document per company and accessing spend in real time to act faster than ever before.
Murilo Benjamin BaggioHead of Procurement, Performance & Transformation, Compass
Murilo Benjamin BaggioCompass
Four companies, and a data anarchy.
Compass had grown by acquisition into a portfolio of four companies, and that made things genuinely hard. As Murilo puts it, the result was "a data anarchy": different systems, different people, different cultures, with no common language to spend.
- Four acquired companies: different systems, people and culture
- "Data anarchy" with no shared language for spend
- Many spreadsheets and data loads to extract for any analysis
- 3 months and 4 consultants to build a single spend cube
- Taxonomy discrepancies hid price differences across companies
"I like to say that we had a data anarchy, different systems, different people, different cultures. It took us three months and four consultants to build our spend cube. That was our scenario before Mithra."
One document per company. A standardized tree for all.
Instead of extracting endless spreadsheets, Compass now shares a single document from each company with Mithra, which builds one standardized category tree across the whole portfolio and keeps the data accessible in real time.
One extract, not many
No more many spreadsheets and data loads from every system. The team extracts one document per company and shares it and the heavy lifting happens at Mithra.
A standardized category tree
From week one, Compass had visibility of its category tree, then of spend on that tree, standardized across all four companies for the first time.
Real-time, fluid collaboration
A shared team chat to trade questions and insights all day, align on views, and shape the roadmap together with a team Murilo calls very responsive.
Results from week one and a clear list of priorities.
It was a really nice experience to start getting outputs in the first week. With a standardized taxonomy, Compass could finally see which suppliers it had in common, where it paid different prices for the same materials and services, and, for sure, exactly what each company was paying.
See more customer stories- Outputs and visibility from the very first week
- One standardized category tree across all four companies
- Common suppliers and price differences, finally visible
- Exact prices known per company, clear supplier opportunities
- Tail spend, once a blind spot, now fully in view
"Nowadays we can say for sure what the exact price is that we're paying, the difference between our companies, and what suppliers we have opportunities with."
Agility and accuracy were the first wins.
Act in real time
Spend is accessible the moment it's needed, so the team can see what's happening and act far faster than the old extract-and-load cycle ever allowed.
Focus on what matters
Full visibility of spend, including the tail, they were blind to, means clear priorities, instead of guessing where the opportunities are.
A true partnership
Daily chat, shared insights, and joint roadmap discussions make the relationship fluid and the Mithra team genuinely responsive.
"My advice for procurement leaders: do a proof of value or proof of concept. It will always give you the insights you need and show the efficiency you could work on. That's what made the real difference for us."
Murilo Benjamin BaggioHead of Procurement, Performance & Transformation, Compass
See what Mithra could find in your spend data.
Start with a proof of value, just as Compass did. Bring a representative sample, and we'll show you your standardized category tree, normalized suppliers, and a prioritized savings analysis on your own data in weeks.