And if you act fast, to prove they can do it, they’ll recover it for free. All you have to do is provide them 12 months of data from your old cube. More on this at the end of the post, but first …
As per our article yesterday, many organizations, often through no fault of their own, end up with a spend cube (filled with their IP) that they spent a lot of money to acquire, but which they can’t maintain — either because it was built by experts using a third party system, built by experts who did manual re-mappings with no explanations (or repeatable rules), built by a vendor that used AI “pattern matching”, or built by a vendor that ceased supporting the cube (and simply provided it to the company without any of the rules that were used to accomplish the categorization).
Such a cube is unusable, and unless maintainable rules can be recovered, it’s money down the drain. But, as per yesterday’s post, it doesn’t have to be.
- It’s possible to build the vast majority of spend cubes on the largest data sets in a matter of days using the classic secret sauce described in our last post.
- All mappings leave evidence, and that evidence can be used to reconstruct a new and maintainable rules set.
Spendata has figured out that it’s possible to reverse engineer old spend cubes by deriving new rules by inference, based on the existing mappings. This is possible because the majority of such (lost) cubes are indirect spending cubes (where most organizations find the most bang for their buck). These can often be mapped to 95% or better accuracy using just Vendor and General Ledger code, with outliers mapped (if necessary) by Item Description.
And it doesn’t matter how your original cube was mapped — keyword matching algorithms, the deep neural net de jour, or by Elves from Rivendell — because supplier, GL-code, and supplier and GL-code patterns can be deduced from the original mappings, and then poked at with intelligent (AI) algorithms to find and address the exceptions.
In fact, Spendata is so confident of its reverse-engineering that — for at least the first 10 volunteers who contact them (at the number here) — they’ll take your old spend cube and use Spendata (at no charge) to reverse-engineer its rules, returning a cube to you so you can see the results (as well as the reverse-engineering algorithms that were applied) and the sequenced plain-English rules that can be used (and modified) to maintain it going forward.
Note that there’s a big advantage to rules-based mapping that is not found in black-box AI solutions — you can easily see any new items at refresh time that are unmapped, and define rules to handle them. This has two advantages.
- You can see if you are spending where you are supposed to be spending against your contracts and policies.
- You can see how fast new suppliers, products, and human errors are entering your system. [And you can speak with the offending personnel in the latter case to prevent these errors in the future.]
And mapping this new data is not a significant effort. If you think about it, how many new suppliers with meaningful spending does your company add in one month? Is it five? Ten? Twenty? It’s not many, and you should know who they are. The same goes for products. Chances are you’ll be able to keep up with the necessary rule additions and changes in an hour a month. That’s not much effort for having a spend cube you can fully understand and manage and that helps you identify what’s new or changed month over month.
If you’re interested in doing this, the doctor is interested in the results, so let SI know what happens and we’ll publish a follow-up article.
And if you take Spendata up on the offer:
- take a view of the old cube with 13 consecutive months of data
- give Spendata the first 12 consecutive months, and get the new cube back
- then add the 13th month of data to the new cube to see what the reverse-engineered rules miss.
You will likely find that the new rules catch almost all of the month 13 spending, showing that the maintenance effort is minimal, and that you can update the spend cube yourself without dependence on a third party.