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Both spend analysis and general-purpose BI tools derive their power from the ability to query large datasets quickly and efficiently. BI tools applied to spend data can tell us the vendor, the amount, the buyer, and how the spend was booked. But spend analysis systems, from the same data, can use mapping tools to deduce 'what' was bought, which is the key to launching successful sourcing and spend management initiatives.

Deloitte's 2021 Global CPO Survey, titled "Agility: The antidote to complexity", spotlights agility as key for high performing procurement organizations. At Spendata we're delighted with this conclusion, because we've built our product around that very principle.

It is customary for companies to require in-depth security reviews for web-based software vendors, because sensitive customer datasets end up on cloud servers that are exposed to attack and breach. But what if your data never leaves your machine?

Buried in many consulting groups is an analyst who has mastered the art of building spend cubes with spreadsheets, and has therefore become indispensable. It’s a waste of talent!

Sometimes it's hard to decide whether spend analysis is a product or a service, especially when data preparation is opaque to users.

Plugging in just a few numbers that you already know (or can make a reasonable guess at) can generate a pretty good estimate of your short-term spend analysis savings potential.

It doesn't matter who built your spend cube or how it was built. You can leverage Spendata's powerful mapping facilities and unsurpassed analysis capability without losing any of the work you've put into your old system.

Your company likely has many different systems involved in procurement, each with their respective silos of data. A data inventory map soon becomes a critical resource in any spend management effort.

Here's a 3 minute video from a European distribution partner explaining Spendata's unique inheritance feature. Inheritance allows analysts complete freedom to modify spend cubes because their changes are preserved across refreshes.

There's a key difference between Spendata's auto-mapping and the "opaque" auto-mapping provided by, for example, neural-net based AI's. Those solutions provide the mapping as a fait accompli. You can't review what they've done, or understand why they've done it.

The 20th-century art movement revolutionized traditional painting. Instead of limiting the representation of an object to two dimensions, it showed the viewer all the possible viewpoints of an object at once. Okay, so it may not be a perfect comparison, but Spendata’s “cubism” brings a similar perspective to the field of spend analysis.

Though ‘agile’ principles originated with software developers, the central idea has universal value: move from idea to impact as quickly as possible. Unfortunately, that’s easier said than done without supporting processes and systems.

Data analysis can take a long time with the wrong tools. That's why it's perfectly reasonable to avoid low-probability analysis, since the expected value of the effort outweighs the expense. But when proper tools are in place and the cost assumptions change, it makes perfect sense to go looking for value!

It’s the 95% perspiration necessary for the 5% insight. With spend analysis, there are two ways to do the sweaty part of the job: (a) get a spend analysis tool that can easily map and segment your data, or (b) spend your nights and weekends hacking on spreadsheets.

“Segmentation” refers to the way in which data can be organized into buckets in order to gain insight. "Qualitative segmentation" is segmentation that is not trivially supported by the data.

Spend analysis pre- and post-merger can drive merger premiums significantly higher.

Here's a one-and-a-half minute video on Spendata's unique inheritance feature. Inheritance allows the entire enterprise to modify and adapt spend cubes for their own purposes, while preserving their changes across refreshes.

The segmentation performed by all spend analysis systems is the grouping of like vendors together ("Familying") and the assignment of spending to Commodity ("Mapping"). This is a continuous process as new data is added. If that process is opaque to you, a dependence on the spend analysis vendor is created that is both expensive and difficult to sever.

BI tools are terrific at some things, but they're pretty awful at creating dynamic data structures like a Preferred Vendor dimension.

It's easy to lose a lot of sleep setting up and maintaining a Preferred Vendor column with Excel.

Spend analysis vendors don't want to talk about it, but automatic mapping always requires manual corrections and enhancements. There are lots of reasons why, but the simplest reason is that the way you use a vendor is not necessarily the way that another company uses them.